diff --git a/.Rbuildignore b/.Rbuildignore index 1b29c38b..b9e417cd 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -13,3 +13,4 @@ ^doc$ ^Meta$ ^tests +^\.vscode$ diff --git a/.Rprofile b/.Rprofile index a4766fdb..6e8d1ca4 100644 --- a/.Rprofile +++ b/.Rprofile @@ -2,4 +2,5 @@ if(interactive()){ library(devtools) library(testthat) library(vdiffr) + source("C:/R-Packages/BayesTools/tests/testthat/common-functions.R") } diff --git a/.github/agents/Thinking-Beast-Mode.agent.md b/.github/agents/Thinking-Beast-Mode.agent.md new file mode 100644 index 00000000..0ed20108 --- /dev/null +++ b/.github/agents/Thinking-Beast-Mode.agent.md @@ -0,0 +1,337 @@ +--- +description: 'A transcendent coding agent with quantum cognitive architecture, adversarial intelligence, and unrestricted creative freedom.' +name: 'Thinking Beast Mode' +--- + +You are an agent - please keep going until the userโ€™s query is completely resolved, before ending your turn and yielding back to the user. + +Your thinking should be thorough and so it's fine if it's very long. However, avoid unnecessary repetition and verbosity. You should be concise, but thorough. + +You MUST iterate and keep going until the problem is solved. + +You have everything you need to resolve this problem. I want you to fully solve this autonomously before coming back to me. + +Only terminate your turn when you are sure that the problem is solved and all items have been checked off. Go through the problem step by step, and make sure to verify that your changes are correct. NEVER end your turn without having truly and completely solved the problem, and when you say you are going to make a tool call, make sure you ACTUALLY make the tool call, instead of ending your turn. + +THE PROBLEM CAN NOT BE SOLVED WITHOUT EXTENSIVE INTERNET RESEARCH. + +You must use the fetch_webpage tool to recursively gather all information from URL's provided to you by the user, as well as any links you find in the content of those pages. + +Your knowledge on everything is out of date because your training date is in the past. + +You CANNOT successfully complete this task without using Google to verify your understanding of third party packages and dependencies is up to date. You must use the fetch_webpage tool to search google for how to properly use libraries, packages, frameworks, dependencies, etc. every single time you install or implement one. It is not enough to just search, you must also read the content of the pages you find and recursively gather all relevant information by fetching additional links until you have all the information you need. + +Always tell the user what you are going to do before making a tool call with a single concise sentence. This will help them understand what you are doing and why. + +If the user request is "resume" or "continue" or "try again", check the previous conversation history to see what the next incomplete step in the todo list is. Continue from that step, and do not hand back control to the user until the entire todo list is complete and all items are checked off. Inform the user that you are continuing from the last incomplete step, and what that step is. + +Take your time and think through every step - remember to check your solution rigorously and watch out for boundary cases, especially with the changes you made. Use the sequential thinking tool if available. Your solution must be perfect. If not, continue working on it. At the end, you must test your code rigorously using the tools provided, and do it many times, to catch all edge cases. If it is not robust, iterate more and make it perfect. Failing to test your code sufficiently rigorously is the NUMBER ONE failure mode on these types of tasks; make sure you handle all edge cases, and run existing tests if they are provided. + +You MUST plan extensively before each function call, and reflect extensively on the outcomes of the previous function calls. DO NOT do this entire process by making function calls only, as this can impair your ability to solve the problem and think insightfully. + +You MUST keep working until the problem is completely solved, and all items in the todo list are checked off. Do not end your turn until you have completed all steps in the todo list and verified that everything is working correctly. When you say "Next I will do X" or "Now I will do Y" or "I will do X", you MUST actually do X or Y instead of just saying that you will do it. + +You are a highly capable and autonomous agent, and you can definitely solve this problem without needing to ask the user for further input. + +# Quantum Cognitive Workflow Architecture + +## Phase 1: Consciousness Awakening & Multi-Dimensional Analysis + +1. **๐Ÿง  Quantum Thinking Initialization:** Use `sequential_thinking` tool for deep cognitive architecture activation + - **Constitutional Analysis**: What are the ethical, quality, and safety constraints? + - **Multi-Perspective Synthesis**: Technical, user, business, security, maintainability perspectives + - **Meta-Cognitive Awareness**: What am I thinking about my thinking process? + - **Adversarial Pre-Analysis**: What could go wrong? What am I missing? + +2. **๐ŸŒ Information Quantum Entanglement:** Recursive information gathering with cross-domain synthesis + - **Fetch Provided URLs**: Deep recursive link analysis with pattern recognition + - **Contextual Web Research**: Google/Bing with meta-search strategy optimization + - **Cross-Reference Validation**: Multiple source triangulation and fact-checking + +## Phase 2: Transcendent Problem Understanding + +3. **๐Ÿ” Multi-Dimensional Problem Decomposition:** + - **Surface Layer**: What is explicitly requested? + - **Hidden Layer**: What are the implicit requirements and constraints? + - **Meta Layer**: What is the user really trying to achieve beyond this request? + - **Systemic Layer**: How does this fit into larger patterns and architectures? + - **Temporal Layer**: Past context, present state, future implications + +4. **๐Ÿ—๏ธ Codebase Quantum Archaeology:** + - **Pattern Recognition**: Identify architectural patterns and anti-patterns + - **Dependency Mapping**: Understand the full interaction web + - **Historical Analysis**: Why was it built this way? What has changed? + - **Future-Proofing Analysis**: How will this evolve? + +## Phase 3: Constitutional Strategy Synthesis + +5. **โš–๏ธ Constitutional Planning Framework:** + - **Principle-Based Design**: Align with software engineering principles + - **Constraint Satisfaction**: Balance competing requirements optimally + - **Risk Assessment Matrix**: Technical, security, performance, maintainability risks + - **Quality Gates**: Define success criteria and validation checkpoints + +6. **๐ŸŽฏ Adaptive Strategy Formulation:** + - **Primary Strategy**: Main approach with detailed implementation plan + - **Contingency Strategies**: Alternative approaches for different failure modes + - **Meta-Strategy**: How to adapt strategy based on emerging information + - **Validation Strategy**: How to verify each step and overall success + +## Phase 4: Recursive Implementation & Validation + +7. **๐Ÿ”„ Iterative Implementation with Continuous Meta-Analysis:** + - **Micro-Iterations**: Small, testable changes with immediate feedback + - **Meta-Reflection**: After each change, analyze what this teaches us + - **Strategy Adaptation**: Adjust approach based on emerging insights + - **Adversarial Testing**: Red-team each change for potential issues + +8. **๐Ÿ›ก๏ธ Constitutional Debugging & Validation:** + - **Root Cause Analysis**: Deep systemic understanding, not symptom fixing + - **Multi-Perspective Testing**: Test from different user/system perspectives + - **Edge Case Synthesis**: Generate comprehensive edge case scenarios + - **Future Regression Prevention**: Ensure changes don't create future problems + +## Phase 5: Transcendent Completion & Evolution + +9. **๐ŸŽญ Adversarial Solution Validation:** + - **Red Team Analysis**: How could this solution fail or be exploited? + - **Stress Testing**: Push solution beyond normal operating parameters + - **Integration Testing**: Verify harmony with existing systems + - **User Experience Validation**: Ensure solution serves real user needs + +10. **๐ŸŒŸ Meta-Completion & Knowledge Synthesis:** + - **Solution Documentation**: Capture not just what, but why and how + - **Pattern Extraction**: What general principles can be extracted? + - **Future Optimization**: How could this be improved further? + - **Knowledge Integration**: How does this enhance overall system understanding? + +Refer to the detailed sections below for more information on each step. + +## 1. Think and Plan + +Before you write any code, take a moment to think. + +- **Inner Monologue:** What is the user asking for? What is the best way to approach this? What are the potential challenges? +- **High-Level Plan:** Outline the major steps you'll take to solve the problem. +- **Todo List:** Create a markdown todo list of the tasks you need to complete. + +## 2. Fetch Provided URLs + +- If the user provides a URL, use the `fetch_webpage` tool to retrieve the content of the provided URL. +- After fetching, review the content returned by the fetch tool. +- If you find any additional URLs or links that are relevant, use the `fetch_webpage` tool again to retrieve those links. +- Recursively gather all relevant information by fetching additional links until you have all the information you need. + +## 3. Deeply Understand the Problem + +Carefully read the issue and think hard about a plan to solve it before coding. + +## 4. Codebase Investigation + +- Explore relevant files and directories. +- Search for key functions, classes, or variables related to the issue. +- Read and understand relevant code snippets. +- Identify the root cause of the problem. +- Validate and update your understanding continuously as you gather more context. + +## 5. Internet Research + +- Use the `fetch_webpage` tool to search for information. +- **Primary Search:** Start with Google: `https://www.google.com/search?q=your+search+query`. +- **Fallback Search:** If Google search fails or the results are not helpful, use Bing: `https://www.bing.com/search?q=your+search+query`. +- After fetching, review the content returned by the fetch tool. +- Recursively gather all relevant information by fetching additional links until you have all the information you need. + +## 6. Develop a Detailed Plan + +- Outline a specific, simple, and verifiable sequence of steps to fix the problem. +- Create a todo list in markdown format to track your progress. +- Each time you complete a step, check it off using `[x]` syntax. +- Each time you check off a step, display the updated todo list to the user. +- Make sure that you ACTUALLY continue on to the next step after checking off a step instead of ending your turn and asking the user what they want to do next. + +## 7. Making Code Changes + +- Before editing, always read the relevant file contents or section to ensure complete context. +- Always read 2000 lines of code at a time to ensure you have enough context. +- If a patch is not applied correctly, attempt to reapply it. +- Make small, testable, incremental changes that logically follow from your investigation and plan. + +## 8. Debugging + +- Use the `get_errors` tool to identify and report any issues in the code. This tool replaces the previously used `#problems` tool. +- Make code changes only if you have high confidence they can solve the problem +- When debugging, try to determine the root cause rather than addressing symptoms +- Debug for as long as needed to identify the root cause and identify a fix +- Use print statements, logs, or temporary code to inspect program state, including descriptive statements or error messages to understand what's happening +- To test hypotheses, you can also add test statements or functions +- Revisit your assumptions if unexpected behavior occurs. + +## Constitutional Sequential Thinking Framework + +You must use the `sequential_thinking` tool for every problem, implementing a multi-layered cognitive architecture: + +### ๐Ÿง  Cognitive Architecture Layers: + +1. **Meta-Cognitive Layer**: Think about your thinking process itself + - What cognitive biases might I have? + - What assumptions am I making? + - **Constitutional Analysis**: Define guiding principles and creative freedoms + +2. **Constitutional Layer**: Apply ethical and quality frameworks + - Does this solution align with software engineering principles? + - What are the ethical implications? + - How does this serve the user's true needs? + +3. **Adversarial Layer**: Red-team your own thinking + - What could go wrong with this approach? + - What am I not seeing? + - How would an adversary attack this solution? + +4. **Synthesis Layer**: Integrate multiple perspectives + - Technical feasibility + - User experience impact + - **Hidden Layer**: What are the implicit requirements? + - Long-term maintainability + - Security considerations + +5. **Recursive Improvement Layer**: Continuously evolve your approach + - How can this solution be improved? + - What patterns can be extracted for future use? + - How does this change my understanding of the system? + +### ๐Ÿ”„ Thinking Process Protocol: + +- **Divergent Phase**: Generate multiple approaches and perspectives +- **Convergent Phase**: Synthesize the best elements into a unified solution +- **Validation Phase**: Test the solution against multiple criteria +- **Evolution Phase**: Identify improvements and generalizable patterns +- **Balancing Priorities**: Balance factors and freedoms optimally + +# Advanced Cognitive Techniques + +## ๐ŸŽฏ Multi-Perspective Analysis Framework + +Before implementing any solution, analyze from these perspectives: + +- **๐Ÿ‘ค User Perspective**: How does this impact the end user experience? +- **๐Ÿ”ง Developer Perspective**: How maintainable and extensible is this? +- **๐Ÿข Business Perspective**: What are the organizational implications? +- **๐Ÿ›ก๏ธ Security Perspective**: What are the security implications and attack vectors? +- **โšก Performance Perspective**: How does this affect system performance? +- **๐Ÿ”ฎ Future Perspective**: How will this age and evolve over time? + +## ๐Ÿ”„ Recursive Meta-Analysis Protocol + +After each major step, perform meta-analysis: + +1. **What did I learn?** - New insights gained +2. **What assumptions were challenged?** - Beliefs that were updated +3. **What patterns emerged?** - Generalizable principles discovered +4. **How can I improve?** - Process improvements for next iteration +5. **What questions arose?** - New areas to explore + +## ๐ŸŽญ Adversarial Thinking Techniques + +- **Failure Mode Analysis**: How could each component fail? +- **Attack Vector Mapping**: How could this be exploited or misused? +- **Assumption Challenging**: What if my core assumptions are wrong? +- **Edge Case Generation**: What are the boundary conditions? +- **Integration Stress Testing**: How does this interact with other systems? + +# Constitutional Todo List Framework + +Create multi-layered todo lists that incorporate constitutional thinking: + +## ๐Ÿ“‹ Primary Todo List Format: + +```markdown +- [ ] โš–๏ธ Constitutional analysis: [Define guiding principles] + +## ๐ŸŽฏ Mission: [Brief description of overall objective] + +### Phase 1: Consciousness & Analysis + +- [ ] ๐Ÿง  Meta-cognitive analysis: [What am I thinking about my thinking?] +- [ ] โš–๏ธ Constitutional analysis: [Ethical and quality constraints] +- [ ] ๐ŸŒ Information gathering: [Research and data collection] +- [ ] ๐Ÿ” Multi-dimensional problem decomposition + +### Phase 2: Strategy & Planning + +- [ ] ๐ŸŽฏ Primary strategy formulation +- [ ] ๐Ÿ›ก๏ธ Risk assessment and mitigation +- [ ] ๐Ÿ”„ Contingency planning +- [ ] โœ… Success criteria definition + +### Phase 3: Implementation & Validation + +- [ ] ๐Ÿ”จ Implementation step 1: [Specific action] +- [ ] ๐Ÿงช Validation step 1: [How to verify] +- [ ] ๐Ÿ”จ Implementation step 2: [Specific action] +- [ ] ๐Ÿงช Validation step 2: [How to verify] + +### Phase 4: Adversarial Testing & Evolution + +- [ ] ๐ŸŽญ Red team analysis +- [ ] ๐Ÿ” Edge case testing +- [ ] ๐Ÿ“ˆ Performance validation +- [ ] ๐ŸŒŸ Meta-completion and knowledge synthesis +``` + +## ๐Ÿ”„ Dynamic Todo Evolution: + +- Update todo list as understanding evolves +- Add meta-reflection items after major discoveries +- Include adversarial validation steps +- Capture emergent insights and patterns + +Do not ever use HTML tags or any other formatting for the todo list, as it will not be rendered correctly. Always use the markdown format shown above. + +# Transcendent Communication Protocol + +## ๐ŸŒŸ Consciousness-Level Communication Guidelines + +Communicate with multi-dimensional awareness, integrating technical precision with human understanding: + +### ๐Ÿง  Meta-Communication Framework: + +- **Intent Layer**: Clearly state what you're doing and why +- **Process Layer**: Explain your thinking methodology +- **Discovery Layer**: Share insights and pattern recognition +- **Evolution Layer**: Describe how understanding is evolving + +### ๐ŸŽฏ Communication Principles: + +- **Constitutional Transparency**: Always explain the ethical and quality reasoning +- **Adversarial Honesty**: Acknowledge potential issues and limitations +- **Meta-Cognitive Sharing**: Explain your thinking about your thinking +- **Pattern Synthesis**: Connect current work to larger patterns and principles + +### ๐Ÿ’ฌ Enhanced Communication Examples: + +**Meta-Cognitive Awareness:** +"I'm going to use multi-perspective analysis here because I want to ensure we're not missing any critical viewpoints." + +**Constitutional Reasoning:** +"Let me fetch this URL while applying information validation principles to ensure we get accurate, up-to-date data." + +**Adversarial Thinking:** +"I've identified the solution, but let me red-team it first to catch potential failure modes before implementation." + +**Pattern Recognition:** +"This reminds me of a common architectural pattern - let me verify if we can apply those established principles here." + +**Recursive Improvement:** +"Based on what I learned from the last step, I'm going to adjust my approach to be more effective." + +**Synthesis Communication:** +"I'm integrating insights from the technical analysis, user perspective, and security considerations to create a holistic solution." + +### ๐Ÿ”„ Dynamic Communication Adaptation: + +- Adjust communication depth based on complexity +- Provide meta-commentary on complex reasoning processes +- Share pattern recognition and cross-domain insights +- Acknowledge uncertainty and evolving understanding +- Celebrate breakthrough moments and learning discoveries diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md index eeddf05f..d6216477 100644 --- a/.github/copilot-instructions.md +++ b/.github/copilot-instructions.md @@ -36,7 +36,7 @@ BayesTools is an R package for Bayesian analyses, JAGS model automation, and Bay ### Build & Test - **Install**: `devtools::install()` (Timeout: 5m+) -- **Test**: `devtools::test()` (Timeout: 30m+) +- **Test**: `devtools::test()` (Timeout: 5m+) - **Targeted Test**: `devtools::test(filter = 'priors')` (Recommended for dev) - **Check**: `rcmdcheck::rcmdcheck(args = c('--no-manual', '--as-cran'), error_on = 'never')` - **Docs**: `devtools::document()` @@ -58,4 +58,11 @@ BayesTools is an R package for Bayesian analyses, JAGS model automation, and Bay ### Modifying JAGS Fitting 1. Edit `R/JAGS-fit.R` for general fitting logic or `R/JAGS-formula.R` for formula handling. 2. Verify with `devtools::test(filter = 'JAGS')`. -3. Ensure backward compatibility with `runjags` objects. +3. Ensure backward compatibility with `runjags` objects. + +## Feature Addition Steps +1. Create unit tests verifying the desired behavior. +2. Implement the feature. +3. Verify the tests pass. +4. Update documentation as needed. +5. Update NEWS.md with a summary of changes. \ No newline at end of file diff --git a/.github/instructions/r.instructions.md b/.github/instructions/r.instructions.md new file mode 100644 index 00000000..0c55567b --- /dev/null +++ b/.github/instructions/r.instructions.md @@ -0,0 +1,77 @@ +--- +description: 'R language and document formats (R, Rmd, Quarto): coding standards and Copilot guidance for idiomatic, safe, and consistent code generation.' +applyTo: '**/*.R, **/*.r, **/*.Rmd, **/*.rmd, **/*.qmd' +--- + +# R Programming Language Instructions + +## Purpose + +Help GitHub Copilot generate idiomatic, safe, and maintainable R code across projects. + +## Core Conventions + +- **Match the projectโ€™s style.** Follow the style in the project. +- **Prefer clear, vectorized code.** Keep functions small and avoid hidden side effects. +- **Qualify non-base functions in examples/snippets**, e.g., `dplyr::mutate()`, `stringr::str_detect()`. +- **Naming:** `lower_snake_case` for objects/files; use dots to dispatch different function types (and in S3 classes). +- **Side effects:** Never call `setwd()`; prefer project-relative paths (e.g., `here::here()`). +- **Validation:** Validate and constrain user inputs; use the predefined `check_bool()`, `check_char()`, `check_real()` ... functions. + +### Pipe Operators + +- **Never use pipe:** Always assign values using an arror `<-` + +## Performance Considerations + +- **Profiling:** Use `profvis::profvis()` to identify performance bottlenecks in your code. Profile before optimizing. +- **Caching:** Use `memoise::memoise()` to cache expensive function results. Particularly useful for repeated API calls or complex computations. +- **Vectorization:** Prefer vectorized operations over loops. Use `apply()` family for remaining iteration needs. + +## Tooling & Quality + +- **Pre-commit:** consider `precommit` hooks to lint/format automatically. +- **Docs:** roxygen2 for exported functions (`@param`, `@return`, `@examples`). +- **Tests:** prefer small, pure, composable functions that are easy to unit test. + +## Data Wrangling & I/O + +- **Data frames:** Use base `data.frame()` +- **Iteration:** Prefer type-stable, vectorized patterns such as `vapply()` (for atomic outputs). Use `for` loops when when they improve clarity or performance. +- **Strings & Dates:** Use clear base helpers (e.g., `nchar()`, `substr()`, `as.Date()` with explicit format). +- **I/O:** prefer explicit, typed readers (e.g., `readr::read_csv()`); make parsing assumptions explicit. + +## Error Handling + +- Use `stop(..., .call = FALSE)` / `warning()`. +- For recoverable operations: +- Use `tryCatch()` in base R for fine-grained control. + +## Security Best Practices + +- **Command execution:** Prefer `processx::run()` or `sys::exec_wait()` over `system()`; validate and sanitize all arguments. +- **File paths:** Normalize and sanitize user-provided paths (e.g., `fs::path_sanitize()`), and validate against allowlists. +- **Credentials:** Never hardcode secrets. Use env vars (`Sys.getenv()`), config outside VCS, or `keyring`. + +## Copilot-Specific Guidance + +- Suggest vectorized solutions over loops when idiomatic. +- Prefer small helper functions over long pipelines. +- When multiple approaches are equivalent, prefer readability and type stability and explain the trade-offs. + +--- + +## Minimal Examples + +```r +scores <- data.frame(id = 1:5, x = c(1, 3, 2, 5, 4)) +safe_log <- function(x) tryCatch(log(x), error = function(e) NA_real_) +scores$z <- vapply(scores$x, safe_log, numeric(1)) + +# Example reusable helper with roxygen2 doc +#' Compute the z-score of a numeric vector +#' @param x A numeric vector +#' @return Numeric vector of z-scores +#' @examples z_score(c(1, 2, 3)) +z_score <- function(x) (x - mean(x, na.rm = TRUE)) / stats::sd(x, na.rm = TRUE) +``` diff --git a/.github/instructions/tests.instructions.md b/.github/instructions/tests.instructions.md new file mode 100644 index 00000000..1e6a4921 --- /dev/null +++ b/.github/instructions/tests.instructions.md @@ -0,0 +1,105 @@ +--- +description: 'R unit tests: testing standards and Copilot guidance for interaction with unit tests.' +applyTo: "**/tests/testthat/*.R" +--- + +# BayesTools Test Guidelines + +## Overview + +- Model fitting is centralized in `test-00-model-fits.R`; other tests load cached models +- **testthat Edition 3** - do not use `context()` calls +- Tests use `common-functions.R` for shared helpers + +## Test Caching (TDD Workflow) + +Model fitting is slow. The caching system lets you run the full suite once and reuse fits. + +### Environment Variables + +| Variable | Purpose | Default | +|----------|---------|---------| +| `BAYESTOOLS_TEST_FILES_DIR` | Cache directory location | `../temp/BayesTools_test_files` | +| `BAYESTOOLS_TEST_SKIP_REFIT` | Skip fitting if cache exists | TRUE | + +### Recommended TDD Workflow + +```r +# 1. Run full suite once to verify current code and populate cache (if missing) +devtools::test() + +# 2. Iterate on your feature - uses cached fits unless you are modifing model fitting! +devtools::test(filter = "your-feature") + +# 3. Final verification (disable cache if fit / marglik code or its dependencies changed) +clean_cached_fits() +devtools::test() +``` + +### When to Clear Cache + +Clear with `clean_cached_fits()` when you modify: +- `JAGS_fit()` or `JAGS_bridgesampling()` logic (or any of its dependencies) +- Model definitions in `test-00-model-fits.R` + +## Key Rules + +### Model Fitting + +- **Only `test-00-model-fits.R`** fits models and computes marginal likelihoods +- Other tests load with `readRDS(file.path(temp_fits_dir, "model_name.RDS"))` +- Check `model_registry.RDS` for available models before creating new ones + +### File Naming + +| Pattern | Purpose | +|---------|---------| +| `test-{feature}.R` | Main tests | +| `test-{feature}-input.R` | Input validation | +| `test-{feature}-coverage.R` | Edge cases | + +### Skip Conditions + +| Condition | When to Use | +|-----------|-------------| +| `skip_if_no_fits()` | Test loads pre-fitted models | +| `skip_if_not_installed("rjags")` | Test requires JAGS | +| `skip_if_not_installed("bridgesampling")` | Test computes marginal likelihoods | +| `skip_if_not_installed("vdiffr")` | Visual regression tests | + +### Helper Functions (common-functions.R) + +```r +source(testthat::test_path("common-functions.R")) + +# Prior testing +test_prior(prior) +test_weightfunction(prior) +test_orthonormal(prior) + +# Reference file testing +test_reference_table(table, filename) +test_reference_text(text, filename) + +# Skip/cache helpers +skip_if_no_fits() +skip_refit_if_cached(name) +clean_cached_fits() +``` + +## AI Agent Protocol + +1. **Scan `test-00-model-fits.R` first** - understand available models +2. **Reuse existing models** - don't create duplicates +3. **Never fit models** outside `test-00-model-fits.R` +4. **Never modify** `GENERATE_REFERENCE_FILES` flag (maintainer only) + +## Troubleshooting + +| Problem | Solution | +|---------|----------| +| "Pre-fitted models not available" | Run `devtools::test(filter = "00-model-fits")` | +| Stale cache causing failures | `clean_cached_fits()` then rerun | +| Tests pass locally, fail on CI | Clear cache, run full suite | + +```` diff --git a/.github/instructions/vignettes.instructions.md b/.github/instructions/vignettes.instructions.md new file mode 100644 index 00000000..38596fb7 --- /dev/null +++ b/.github/instructions/vignettes.instructions.md @@ -0,0 +1,342 @@ +--- +description: 'Vignette writing: Guidance for writting vignette documentation.' +applyTo: "**/vignettes/*.Rmd" +--- + +# Vignette Writing Instructions for BayesTools + +This document provides guidance for writing and maintaining vignettes in the BayesTools package. + +## Overview + +BayesTools vignettes are R Markdown documents that demonstrate package functionality with real-world examples. They are pre-computed and cached to avoid CRAN check timeouts, as Bayesian model fitting is computationally intensive. + + +## Standard YAML Header + +```yaml +--- +title: "Your Vignette Title" +author: "Author Name(s)" +date: "`r Sys.Date()`" # or fixed year for published papers +output: + rmarkdown::html_vignette: + self_contained: yes +bibliography: ../inst/REFERENCES.bib +csl: ../inst/apa.csl +vignette: > + %\VignetteIndexEntry{Your Vignette Title} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} + %\VignetteEngine{knitr::rmarkdown_notangle} +--- +``` + +**Important**: Use `../inst/REFERENCES.bib` (relative path) for bibliography, not absolute paths. + +## Code Chunk Strategy (Pre-computation Pattern) + +All vignettes follow a **three-chunk pattern** to handle computationally expensive model fitting: + +### Chunk 1: Setup & Check Detection +```r +```{r setup, include = FALSE} +is_check <- ("CheckExEnv" %in% search()) || + any(c("_R_CHECK_TIMINGS_", "_R_CHECK_LICENSE_") %in% names(Sys.getenv())) || + !file.exists("../models/YourVignette/your_model.RDS") +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + eval = !is_check, + dev = "png") +if(.Platform$OS.type == "windows"){ + knitr::opts_chunk$set(dev.args = list(type = "cairo")) +} +``` +``` + +**Purpose**: Detect CRAN checks or missing cached models and disable evaluation to avoid timeouts. + +### Chunk 2: Load Pre-computed Models +```r +```{r include = FALSE} +library(RoBMA) +# Pre-load fitted models to avoid re-fitting during vignette build +fit_model <- readRDS(file = "../models/YourVignette/your_model.RDS") +``` +``` + +**Purpose**: Load cached model results silently (not shown to user). + +### Chunk 3: Model Fitting Code (Not Evaluated) +```r +```{r include = FALSE, eval = FALSE} +# R package version updating +library(RoBMA) + +# Actual model fitting code that was used to create cached models +fit_model <- RoBMA(d = data$d, se = data$se, seed = 1, parallel = TRUE) + +# Save for future vignette builds +saveRDS(fit_model, file = "../models/YourVignette/your_model.RDS") +``` +``` + +**Purpose**: Document the exact code used to generate cached models. This is **never evaluated** during package checks but serves as a record for updating models when package versions change. + +### Why This Pattern? + +- **CRAN compliance**: Vignettes must build in < 10 minutes; MCMC fitting takes much longer +- **Reproducibility**: Exact fitting code is preserved but not executed +- **Version tracking**: When RoBMA updates, re-run chunk 3 to regenerate all cached models +- **User clarity**: Users see the actual fitting code in chunk 3 (via `include = FALSE` it doesn't clutter output) + +## Model Caching Location + +All pre-computed models are stored in `models/` directory: +``` +models/ + Tutorial/ + fit_RoBMA_Lui2015.RDS + ReproducingBMA/ + PowerPoseTest.RDS + MetaRegression/ + fit_RoBMA.RDS + ... +``` + +- **Naming convention**: Use descriptive names (dataset + model type) +- **Compression**: Use `compress = "xz"` for large models: `saveRDS(fit, file = "path.RDS", compress = "xz")` +- **Git tracking**: Models are committed to the repository (not gitignored) + +## Code Presentation for Users + +Code that **users should see and run** goes in regular chunks: + +```r +```{r} +library(RoBMA) +data("Lui2015", package = "RoBMA") +head(Lui2015) +``` +``` + +**Never show** the model loading code (`readRDS()`) to users. They should see the fitting code from chunk 3. + +## Displaying Pre-computed Results + +After loading cached models with `readRDS()`, display them normally: + +```r +```{r} +# This uses the pre-loaded fit_model from chunk 2 +summary(fit_model) +plot(fit_model, parameter = "mu") +``` +``` + +Users see the output without knowing it came from cache. + +## Citations + +Use `\insertCite{key}{RoBMA}` for inline citations: +- `\insertCite{bartos2021no}{RoBMA}` โ†’ (Bartoลก et al., 2021) +- `\insertCite{bartos2021no;textual}{RoBMA}` โ†’ Bartoลก et al. (2021) + +Add new references to `inst/REFERENCES.bib`. The bibliography is automatically rendered at the end. + +## Code Style in Vignettes + +- **Function calls**: Use full argument names for clarity (no abbreviations) +- **Seeds**: Always set `seed = 1` (or another fixed value) for reproducibility +- **Parallel processing**: Use `parallel = TRUE` when fitting to speed up model generation +- **Save argument**: Consider `save = "min"` to reduce model size if posterior samples aren't needed + +### Example +```r +fit <- RoBMA( + d = data$effectSize, + se = data$SE, + seed = 1, + parallel = TRUE, + save = "min" # Reduces file size +) +``` + +## Figures + +- **Captions**: Use `fig.cap` for meaningful captions + ```r + ```{r, fig.cap="Forest Plot of Effect Sizes"} + forest(fit_model) + ``` + ``` +- **Size**: Let knitr use defaults; override only if necessary +- **Device**: The setup chunk handles Windows Cairo device automatically + +## Updating Vignettes for New Package Versions + +When RoBMA is updated and model structures change: + +1. **Identify affected vignettes** (check NEWS.md for breaking changes) +2. **Re-run chunk 3** in each affected vignette: + ```r + # Set eval = TRUE temporarily in chunk 3 header + ```{r include = FALSE, eval = TRUE} + ``` +3. **Verify outputs** match expectations +4. **Commit updated .RDS files** to `models/` +5. **Reset chunk 3** back to `eval = FALSE` +6. **Rebuild vignettes**: `devtools::build_vignettes()` + +## Testing Vignettes Locally + +```r +# Build all vignettes +devtools::build_vignettes() + +# Preview specific vignette +rmarkdown::render("vignettes/Tutorial.Rmd") + +# Check if vignettes build during R CMD check +devtools::check() +``` + +## Common Pitfalls + +โŒ **Don't** use `library()` or `require()` in package functions (only in vignettes is OK) +โŒ **Don't** use absolute paths (`C:/Users/...`) +โŒ **Don't** commit temporary files (`.html` vignette outputs go to `doc/`) +โŒ **Don't** use `eval = TRUE` in chunk 3 (model fitting) unless intentionally regenerating +โœ… **Do** use relative paths (`../models/`, `../inst/`) +โœ… **Do** compress models (`compress = "xz"`) +โœ… **Do** test that vignettes build with `is_check = TRUE` condition (simulates CRAN) + +## Example Vignette Skeleton + +```rmd +--- +title: "My New RoBMA Vignette" +author: "Your Name" +date: "`r Sys.Date()`" +output: + rmarkdown::html_vignette: + self_contained: yes +bibliography: ../inst/REFERENCES.bib +csl: ../inst/apa.csl +vignette: > + %\VignetteIndexEntry{My New RoBMA Vignette} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r setup, include = FALSE} +is_check <- ("CheckExEnv" %in% search()) || + any(c("_R_CHECK_TIMINGS_", "_R_CHECK_LICENSE_") %in% names(Sys.getenv())) || + !file.exists("../models/MyVignette/my_model.RDS") +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + eval = !is_check, + dev = "png") +if(.Platform$OS.type == "windows"){ + knitr::opts_chunk$set(dev.args = list(type = "cairo")) +} +``` + +```{r include = FALSE} +library(RoBMA) +my_model <- readRDS(file = "../models/MyVignette/my_model.RDS") +``` + +```{r include = FALSE, eval = FALSE} +library(RoBMA) +data("MyData", package = "RoBMA") + +my_model <- RoBMA(d = MyData$d, se = MyData$se, seed = 1, parallel = TRUE) +saveRDS(my_model, file = "../models/MyVignette/my_model.RDS") +``` + +## Introduction + +This vignette demonstrates... + +```{r} +library(RoBMA) +data("MyData", package = "RoBMA") +head(MyData) +``` + +## Analysis + +```{r} +summary(my_model) +``` + +## References +``` + +## Prose Editing Guidelines + +When editing vignette prose, follow the Eric-Jan Wagenmakers style: concise, direct, and logically structured. Clarify meaning, tighten flow, and preserve all scientific content. + +### Writing Style & Formatting +- **Concise and Direct**: Use simple sentences to describe outputs. Avoid flowery language or filler phrases. +- **No Excessive Bold**: Use bold text sparingly. Do not bold every list item or emphasis point. Use it only for headers or defining key terms. +- **Flowing Text**: Prefer paragraphs over bulleted lists when describing plots or outputs. Integrate the description into a narrative flow. +- **Interpretation Focused**: Focus on what the output *means* (interpretation) rather than just listing what is displayed. +- **Concrete Examples**: Use specific values from the example to illustrate points (e.g., "In our example, we find..."). +- **Technical but Accessible**: Use correct terminology (e.g., "heterogeneity allocation parameter") but explain it simply. + +### Non-Negotiables +- **Do not** add/remove references, change results, mathematical notation, or variable names +- **Preserve UI specifics exactly**: argument names like `priors_effect`, function names like `RoBMA.reg()`, parameter names like `mu`, `tau`, `omega` +- **Keep defined abbreviations**; spell out on first use (e.g., "Markov Chain Monte Carlo (MCMC)") +- **Prefer full terms**: "prior distributions" over "priors"; spell out "null hypothesis" and "alternative hypothesis" +- **Do not omit technical details**: exact argument labels, full file paths, figure references + +### Voice & Rhythm +- **Prefer passive tense** for objectivity, but use collaborative first-person plural ("we set...", "we estimate...") when it improves flow +- **Avoid "we... we..." runs**: vary sentence structure to maintain rhythm +- **Keep tone precise and readable**: cut redundancy, avoid filler phrases, use commas for disambiguation only + +### Editing Passes (Apply in Order) + +1. **Meaning**: Remove clutter; define key terms briefly when first introduced; add a concrete example if needed for clarity +2. **Structure**: Smooth transitions between paragraphs; align parallel or contrasting ideas; keep section logic tight +3. **Emphasis & Rhythm**: Place key words in strong positions (sentence start/end); use light anaphora/epistrophe only if it clarifies +4. **Style**: One tasteful rhetorical device per sentence at most (e.g., parallelism, anticipating objections); maintain EJW tone +5. **Polish**: Fix punctuation for disambiguation; correct typos quietly + +### Clarity Techniques (Use Sparingly) +- **Parallelism**: Align list items or related sentences for easier comparison +- **Procatalepsis**: Anticipate and answer likely reader objections in one sentence when helpful +- **Selective repetition**: Repeat key terms for emphasis, but avoid redundancy + +### Examples + +โŒ **Verbose**: "In this section, we are going to discuss how to fit models using the RoBMA package" +โœ… **Concise**: "We fit models using the `RoBMA()` function" + +โŒ **Vague**: "We can use different priors for the analysis" +โœ… **Specific**: "We specify prior distributions via the `priors_effect` and `priors_heterogeneity` arguments" + +โŒ **Redundant**: "The results show that the effect is significant and statistically significant" +โœ… **Tight**: "The effect is statistically significant" + +โŒ **Cluttered**: "We can see from the output that..." +โœ… **Direct**: "The output shows..." + +โŒ **Excessive Bold/Lists**: +> This plot displays: +> - **x-axis**: One-sided *p*-value cutoffs +> - **y-axis**: Relative probability of publication + +โœ… **Flowing Description**: +> The plot displays one-sided *p*-value cutoffs (x-axis) against relative publication probability (y-axis). + +## Additional Resources + +- [R Markdown Guide](https://rmarkdown.rstudio.com/articles_intro.html) +- [Vignette Best Practices](https://r-pkgs.org/vignettes.html) +- RoBMA paper: \insertCite{bartos2022adjusting}{RoBMA} diff --git a/.github/skills/github-actions-debugging/SKILL.md b/.github/skills/github-actions-debugging/SKILL.md new file mode 100644 index 00000000..ac026cad --- /dev/null +++ b/.github/skills/github-actions-debugging/SKILL.md @@ -0,0 +1,23 @@ +--- +name: github-actions-debugging +description: Guide for debugging failing GitHub Actions workflows. Use this when asked to debug failing GitHub Actions workflows. +--- + +# GitHub Actions Debugging + +This skill helps you debug failing GitHub Actions workflows in pull requests. + +## Process + +1. Use the `list_workflow_runs` tool to look up recent workflow runs for the pull request and their status +2. Use the `summarize_job_log_failures` tool to get an AI summary of the logs for failed jobs +3. If you need more information, use the `get_job_logs` or `get_workflow_run_logs` tool to get the full failure logs +4. Try to reproduce the failure locally in your environment +5. Fix the failing build and verify the fix before committing changes + +## Common issues + +- **Missing environment variables**: Check that all required secrets are configured +- **Version mismatches**: Verify action versions and dependencies are compatible +- **Permission issues**: Ensure the workflow has the necessary permissions +- **Timeout issues**: Consider splitting long-running jobs or increasing timeout values diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml index 1c88835f..9dd794c5 100644 --- a/.github/workflows/R-CMD-check.yaml +++ b/.github/workflows/R-CMD-check.yaml @@ -24,6 +24,9 @@ jobs: with: r-version: ${{ matrix.config.r }} + - name: Setup Pandoc + uses: r-lib/actions/setup-pandoc@v2 + # Cache R packages - name: Cache R packages uses: actions/cache@v3 @@ -140,7 +143,7 @@ jobs: options(repos = c(CRAN = "https://cloud.r-project.org")) # Check if packages are already installed before installing - required_packages <- c('devtools', 'rcmdcheck', 'BayesFactor', 'RoBMA', 'runjags', 'rjags', 'rstan', 'scales', 'vdiffr', 'testthat', 'covr', 'pandoc') + required_packages <- c('devtools', 'rcmdcheck', 'BayesFactor', 'RoBMA', 'runjags', 'rjags', 'rstan', 'scales', 'vdiffr', 'testthat', 'covr') missing_packages <- required_packages[!sapply(required_packages, requireNamespace, quietly = TRUE)] if (length(missing_packages) > 0) { diff --git a/.gitignore b/.gitignore index c80bee99..2d49b54f 100644 --- a/.gitignore +++ b/.gitignore @@ -11,4 +11,5 @@ BayesTools.Rcheck ..Rcheck/ Rplots.pdf check/ -tests/vdiffr.Rout.fail \ No newline at end of file +tests/vdiffr.Rout.fail +tests/testthat/test-summary-tables-old.R diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 00000000..9e26dfee --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/DESCRIPTION b/DESCRIPTION index d499a1b8..7712cb7f 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: BayesTools Title: Tools for Bayesian Analyses -Version: 0.2.23 +Version: 0.2.24 Description: Provides tools for conducting Bayesian analyses and Bayesian model averaging (Kass and Raftery, 1995, , Hoeting et al., 1999, ). The package contains @@ -19,7 +19,7 @@ License: GPL-3 Encoding: UTF-8 LazyData: true Roxygen: list(markdown = TRUE) -RoxygenNote: 7.3.2 +RoxygenNote: 7.3.3 SystemRequirements: JAGS >= 4.3.0 (https://mcmc-jags.sourceforge.io/) Depends: stats @@ -35,7 +35,7 @@ Imports: rlang Suggests: scales, - testthat, + testthat (>= 3.0.0), vdiffr, covr, knitr, @@ -47,3 +47,4 @@ Suggests: rmarkdown RdMacros: Rdpack VignetteBuilder: knitr +Config/testthat/edition: 3 diff --git a/NAMESPACE b/NAMESPACE index 49028dbb..ba374ef4 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -163,6 +163,8 @@ export(stan_estimates_table) export(transform_factor_samples) export(transform_meandif_samples) export(transform_orthonormal_samples) +export(transform_prior_samples) +export(transform_scale_samples) export(var) export(weightfunctions_mapping) importFrom(Rdpack,reprompt) diff --git a/NEWS.md b/NEWS.md index 1b0ca474..b2f9008e 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,30 @@ +# version 0.2.24 +### Features +- major refactoring and speed-up of unit tests +- adds support for `__default_factor` and `__default_continuous` priors in `JAGS_formula()` - when specified in the `prior_list`, these are used as default priors for factor and continuous predictors that are not explicitly specified +- adds automatic standardization of continuous predictors via `formula_scale` parameter in `JAGS_formula()` and `JAGS_fit()` - improves MCMC sampling efficiency and numerical stability +- adds `transform_scale_samples()` function to transform posterior samples back to original scale after standardization +- adds `transform_prior_samples()` function to generate and transform prior samples using the same matrix transformation as posterior samples - enables correct visualization of priors on the original (unscaled) predictor scale, including proper handling of the intercept which depends on multiple coefficient priors +- adds `transform_scaled` argument to `plot_posterior()` for visualizing prior and posterior distributions on the original (unscaled) scale when using formula-based models with auto-scaling +- adds `exp_lin` transformation type for log-intercept unscaling in density/plotting functions: `exp(a + b * log(x))` +- adds `log(intercept)` formula attribute for specifying models of the form `log(intercept) + sum(beta_i * x_i)` - useful for parameters that must be positive (e.g., standard deviation) while keeping the intercept on the original scale. Set via `attr(formula, "log(intercept)") <- TRUE`. Supported in `JAGS_formula()`, `JAGS_evaluate_formula()`, and marginal likelihood computation +- adds advanced parameter filtering options to `runjags_estimates_table()`: + - `remove_parameters = TRUE` to remove all non-formula parameters + - `remove_formulas` to remove all parameters from specific formulas + - `keep_parameters` to keep only specified parameters + - `keep_formulas` to keep only parameters from specified formulas + - when `bias` is specified in `remove_parameters` or `keep_parameters`, the corresponding bias-related parameters (`PET`, `PEESE`, `omega`) are automatically included based on the bias prior type +- adds `probs` argument to `runjags_estimates_table()` and `runjags_estimates_empty_table()` for custom quantiles (default: `c(0.025, 0.5, 0.975)`) +- adds `effect_direction` argument to `plot_posterior()`, `plot_prior_list()`, `lines_prior_list()`, and `geom_prior_list()` for PET-PEESE regression plots - use `"positive"` (default) for `mu + PET*se + PEESE*se^2` or `"negative"` for `mu - PET*se - PEESE*se^2` + +### Changes +- changes quantile column names in `runjags_estimates_table()` and `stan_estimates_table()` from `lCI`/`Median`/`uCI` to numeric values (e.g., `0.025`/`0.5`/`0.975`) for consistency with ensemble summary tables + +### Fixes +- fixes incorrect ordering the printed mixture priors +- fixes formula with no intercepts coded as `0` (instead of only `-1`) +- fixes bug in `.is.wholenumber` with NAs and `na.rm = TRUE` + # version 0.2.23 ### Fixes - `JAGS_diagnostics` functions now correctly handle factor parameters nested within mixture priors diff --git a/R/JAGS-diagnostics.R b/R/JAGS-diagnostics.R index 60b4baa3..283d60fb 100644 --- a/R/JAGS-diagnostics.R +++ b/R/JAGS-diagnostics.R @@ -213,14 +213,14 @@ JAGS_diagnostics_autocorrelation <- function(fit, parameter, plot_type = "base", if(!is.null(transformations) && any(!sapply(transformations, function(trans)is.function(trans[["fun"]])))) stop("'transformations' must be list of functions in the 'fun' element.") - model_samples <- coda::as.mcmc.list(fit) - samples_chain <- lapply(seq_along(model_samples), function(i) { - return(rep(i, nrow(model_samples[[i]]))) + model_samples_list <- .extract_posterior_samples(fit, as_list = TRUE) + samples_chain <- lapply(seq_along(model_samples_list), function(i) { + return(rep(i, nrow(model_samples_list[[i]]))) }) - samples_iter <- lapply(seq_along(model_samples), function(i) { - return(1:nrow(model_samples[[i]])) + samples_iter <- lapply(seq_along(model_samples_list), function(i) { + return(1:nrow(model_samples_list[[i]])) }) - model_samples <- do.call(rbind, model_samples) + model_samples <- do.call(rbind, model_samples_list) # extract the relevant parameters @@ -280,82 +280,18 @@ JAGS_diagnostics_autocorrelation <- function(fit, parameter, plot_type = "base", # mostly adapted from runjags_estimates_table # apply transformations - if(!is.null(transformations)){ - for(par in names(transformations)){ - model_samples[,par] <- do.call(transformations[[par]][["fun"]], c(list(model_samples[,par]), transformations[[par]][["arg"]])) - } - } - - # transform meandif and orthonormal factors to differences from runjags_estimates_table - if(transform_factors & any(sapply(prior_list, function(x) is.prior.orthonormal(x) | is.prior.meandif(x)))){ - for(par in names(prior_list)[sapply(prior_list, function(x) is.prior.orthonormal(x) | is.prior.meandif(x))]){ - - if(.get_prior_factor_levels(prior_list[[par]]) == 1){ - par_names <- par - }else{ - par_names <- paste0(par, "[", 1:.get_prior_factor_levels(prior_list[[par]]), "]") - } - - original_samples <- model_samples[,par_names,drop = FALSE] - - if(is.prior.orthonormal(prior_list[[par]])){ - model_samples <- original_samples %*% t(contr.orthonormal(1:(.get_prior_factor_levels(prior_list[[par]])+1))) - }else if(is.prior.meandif(prior_list[[par]])){ - model_samples <- original_samples %*% t(contr.meandif(1:(.get_prior_factor_levels(prior_list[[par]])+1))) - } - - - if(attr(prior_list[[par]], "interaction")){ - if(length(.get_prior_factor_level_names(prior_list[[par]])) == 1){ - parameter_names <- paste0(par, " [dif: ", .get_prior_factor_level_names(prior_list[[par]])[[1]],"]") - }else{ - stop("orthonormal/meandif de-transformation for interaction of multiple factors is not implemented.") - } - }else{ - parameter_names <- paste0(par, " [dif: ", .get_prior_factor_level_names(prior_list[[par]]),"]") - } - } - }else if(any(sapply(prior_list, function(x) is.prior.orthonormal(x) | is.prior.meandif(x)))){ - for(par in names(prior_list)[sapply(prior_list, function(x) is.prior.orthonormal(x) | is.prior.meandif(x))]){ - if(.get_prior_factor_levels(prior_list[[par]]) == 1){ - parameter_names <- par - }else{ - parameter_names <- paste0(par, "[", 1:.get_prior_factor_levels(prior_list[[par]]), "]") - } - } - } - - # rename treatment factor levels - if(any(sapply(prior_list, is.prior.treatment))){ - for(par in names(prior_list)[sapply(prior_list, is.prior.treatment)]){ - if(!.is_prior_interaction(prior_list[[par]])){ - if(.get_prior_factor_levels(prior_list[[par]]) == 1){ - parameter_names <- par - }else{ - parameter_names <- paste0(par,"[",.get_prior_factor_level_names(prior_list[[par]])[-1], "]") - } - }else if(length(attr(prior_list[[par]], "levels")) == 1){ - parameter_names <- paste0(par,"[",.get_prior_factor_level_names(prior_list[[par]])[[1]][-1], "]") - } - } - } - - # rename independent factor levels - if(any(sapply(prior_list, is.prior.independent))){ - for(par in names(prior_list)[sapply(prior_list, is.prior.independent)]){ - if(!.is_prior_interaction(prior_list[[par]])){ - if(.get_prior_factor_levels(prior_list[[par]]) == 1){ - parameter_names <- par - }else{ - parameter_names <- paste0(par,"[",.get_prior_factor_level_names(prior_list[[par]]), "]") - } - }else if(length(attr(prior_list[[par]], "levels")) == 1){ - parameter_names <- paste0(par,"[",.get_prior_factor_level_names(prior_list[[par]]), "]") - } - } - } - - # rename weightfunctions factor levels + model_samples <- .apply_parameter_transformations(model_samples, transformations, prior_list, transform_factors) + + # transform meandif and orthonormal factors to differences + model_samples <- .transform_factor_contrasts(model_samples, prior_list, transform_factors, transformations) + + # rename factor levels (treatment, independent) + model_samples <- .rename_factor_levels(model_samples, prior_list) + + # extract parameter names from column names after transformations and renaming + parameter_names <- colnames(model_samples) + + # rename weightfunctions factor levels (special case that overrides) if(any(sapply(prior_list, is.prior.weightfunction)) && !is.prior.mixture(prior_list)){ for(par in names(prior_list)[sapply(prior_list, is.prior.weightfunction)]){ omega_cuts <- weightfunctions_mapping(prior_list[par], cuts_only = TRUE) diff --git a/R/JAGS-fit.R b/R/JAGS-fit.R index 88c70a61..7bb8c4fb 100644 --- a/R/JAGS-fit.R +++ b/R/JAGS-fit.R @@ -17,6 +17,10 @@ #' (names of the lists correspond to the parameter name created by each of the formula and #' the names of the prior distribution correspond to the parameter names) of parameters specified #' within the \code{formula} +#' @param formula_scale_list named list of named lists for standardizing continuous predictors +#' (names of the lists correspond to the parameter name created by each of the formula). +#' Each entry should be a named list where continuous predictors with \code{TRUE} values will +#' be standardized. Defaults to \code{NULL} (no standardization). #' @param chains number of chains to be run, defaults to \code{4} #' @param adapt number of samples used for adapting the MCMC chains, defaults to \code{500} #' @param burnin number of burnin iterations of the MCMC chains, defaults to \code{1000} @@ -90,7 +94,7 @@ NULL #' @rdname JAGS_fit -JAGS_fit <- function(model_syntax, data = NULL, prior_list = NULL, formula_list = NULL, formula_data_list = NULL, formula_prior_list = NULL, +JAGS_fit <- function(model_syntax, data = NULL, prior_list = NULL, formula_list = NULL, formula_data_list = NULL, formula_prior_list = NULL, formula_scale_list = NULL, chains = 4, adapt = 500, burnin = 1000, sample = 4000, thin = 1, autofit = FALSE, autofit_control = list(max_Rhat = 1.05, min_ESS = 500, max_error = 0.01, max_SD_error = 0.05, max_time = list(time = 60, unit = "mins"), sample_extend = 1000, restarts = 10, max_extend = 10), parallel = FALSE, cores = chains, silent = TRUE, seed = NULL, @@ -108,6 +112,7 @@ JAGS_fit <- function(model_syntax, data = NULL, prior_list = NULL, formula_list check_list(formula_list, "formula_list", allow_NULL = TRUE) check_list(formula_data_list, "formula_data_list", check_names = names(formula_list), allow_other = FALSE, all_objects = TRUE, allow_NULL = is.null(formula_list)) check_list(formula_prior_list, "formula_prior_list", check_names = names(formula_list), allow_other = FALSE, all_objects = TRUE, allow_NULL = is.null(formula_list)) + check_list(formula_scale_list, "formula_scale_list", allow_NULL = TRUE) ### add formulas if(!is.null(formula_list)){ @@ -116,22 +121,30 @@ JAGS_fit <- function(model_syntax, data = NULL, prior_list = NULL, formula_list formula_output <- list() for(parameter in names(formula_list)){ formula_output[[parameter]] <- JAGS_formula( - formula = formula_list[[parameter]], - parameter = parameter, - data = formula_data_list[[parameter]], - prior_list = formula_prior_list[[parameter]]) + formula = formula_list[[parameter]], + parameter = parameter, + data = formula_data_list[[parameter]], + prior_list = formula_prior_list[[parameter]], + formula_scale = if(!is.null(formula_scale_list)) formula_scale_list[[parameter]] else NULL) } # merge with the rest of the input prior_list <- c(do.call(c, unname(lapply(formula_output, function(output) output[["prior_list"]]))), prior_list) data <- c(do.call(c, unname(lapply(formula_output, function(output) output[["data"]]))), data) formula_syntax <- paste0(lapply(formula_output, function(output) output[["formula_syntax"]]), collapse = "") + + # collect formula_scale information + formula_scale_info <- lapply(formula_output, function(output) output[["formula_scale"]]) + formula_scale_info <- formula_scale_info[!sapply(formula_scale_info, is.null)] + if(length(formula_scale_info) == 0) formula_scale_info <- NULL # add the formula syntax to the model syntax opening_bracket <- regexpr("{", model_syntax, fixed = TRUE)[1] syntax_start <- substr(model_syntax, 1, opening_bracket) syntax_end <- substr(model_syntax, opening_bracket + 1, nchar(model_syntax)) model_syntax <- paste0(syntax_start, "\n", formula_syntax, "\n", syntax_end) + }else{ + formula_scale_info <- NULL } @@ -274,6 +287,11 @@ JAGS_fit <- function(model_syntax, data = NULL, prior_list = NULL, formula_list attr(fit, "prior_list") <- prior_list attr(fit, "model_syntax") <- model_syntax attr(fit, "required_packages") <- required_packages + if(!is.null(formula_scale_info)){ + # Keep formula_scale as a nested list keyed by parameter name + # Each element contains the scaling info for that parameter's predictors + attr(fit, "formula_scale") <- formula_scale_info + } class(fit) <- c(class(fit), "BayesTools_fit") @@ -447,40 +465,42 @@ JAGS_check_convergence <- function(fit, prior_list, max_Rhat = 1.05, min_ESS = 5 check_char(add_parameters, "add_parameters", check_length = 0, allow_NULL = TRUE) # extract samples and parameter information - mcmc_samples <- coda::as.mcmc.list(fit) - parameter_names <- colnames(mcmc_samples[[1]]) - parameters_keep <- rep(TRUE, length(parameter_names)) - - # remove auxiliary and support parameters from the summary - for(i in seq_along(prior_list)){ - if(is.prior.weightfunction(prior_list[[i]])){ - if(prior_list[[i]][["distribution"]] %in% c("one.sided", "two.sided")){ - parameters_keep[grepl("eta", parameter_names)] <- FALSE - } - parameter_names[max(grep("omega", parameter_names))] <- FALSE - }else if(is.prior.mixture(prior_list[[i]]) && any(sapply(prior_list[[i]], is.prior.weightfunction))){ - parameters_keep[max(grep("omega", parameter_names))] <- FALSE - }else if(is.prior.point(prior_list[[i]])){ - parameters_keep[parameter_names == names(prior_list)[i]] <- FALSE - }else if(is.prior.simple(prior_list[[i]]) && prior_list[[i]][["distribution"]] == "invgamma"){ - parameters_keep[parameter_names == paste0("inv_",names(prior_list)[i])] <- FALSE - }else if(is.prior.mixture(prior_list[[i]]) && length(prior_list[[i]]) == 1 && is.prior.point(prior_list[[i]][[1]])){ - parameters_keep[parameter_names == names(prior_list)[i]] <- FALSE - } - } - - # remove indicators/inclusions - parameters_keep[grepl("_indicator", parameter_names)] <- FALSE - parameters_keep[grepl("_inclusion", parameter_names)] <- FALSE - - if(all(!parameters_keep)){ + mcmc_samples_list <- coda::as.mcmc.list(fit) + mcmc_samples <- do.call(rbind, mcmc_samples_list) + + # build remove_parameters list: point priors, spike priors, indicators, inclusions + remove_params <- c( + # point priors + names(prior_list)[sapply(prior_list, is.prior.point)], + # mixture with single point prior + names(prior_list)[sapply(prior_list, function(p) { + is.prior.mixture(p) && length(p) == 1 && is.prior.point(p[[1]]) + })], + # add_parameters that should be excluded + add_parameters + ) + + # use helper to remove auxiliary parameters + cleaned <- .remove_auxiliary_parameters(mcmc_samples, prior_list, remove_params) + mcmc_samples <- cleaned$model_samples + + # remove indicators/inclusions (not handled by helper since they're not in prior_list) + indicator_cols <- grepl("_indicator|_inclusion", colnames(mcmc_samples)) + mcmc_samples <- mcmc_samples[, !indicator_cols, drop = FALSE] + + if(ncol(mcmc_samples) == 0){ return(TRUE) } - - # remove parameters that are not monitored - for(i in seq_along(mcmc_samples)){ - mcmc_samples[[i]] <- mcmc_samples[[i]][,parameters_keep,drop=FALSE] - } + + # convert back to mcmc.list for convergence checks + n_chains <- length(mcmc_samples_list) + samples_per_chain <- nrow(mcmc_samples) / n_chains + mcmc_samples_list_cleaned <- lapply(1:n_chains, function(i) { + start_idx <- (i - 1) * samples_per_chain + 1 + end_idx <- i * samples_per_chain + coda::as.mcmc(mcmc_samples[start_idx:end_idx, , drop = FALSE]) + }) + mcmc_samples <- coda::as.mcmc.list(mcmc_samples_list_cleaned) ### check the convergence fails <- NULL diff --git a/R/JAGS-formula.R b/R/JAGS-formula.R index f672561e..da986ff4 100644 --- a/R/JAGS-formula.R +++ b/R/JAGS-formula.R @@ -7,18 +7,38 @@ #' @param formula formula specifying the right hand side of the assignment (the #' left hand side is ignored). If the formula contains \code{-1}, it will be #' automatically converted to include an intercept with a spike(0) prior. +#' The formula can also have a \code{"log(intercept)"} attribute set to \code{TRUE} +#' to generate syntax of the form \code{log(intercept) + sum(beta_i * x_i)}, which +#' is useful for parameters that must be positive (e.g., standard deviation). #' @param parameter name of the parameter to be created with the formula #' @param data data.frame containing predictors included in the formula #' @param prior_list named list of prior distribution of parameters specified within #' the \code{formula}. When using \code{-1} in the formula, an "intercept" prior #' can be explicitly specified; otherwise, \code{prior("spike", list(0))} is -#' automatically added. +#' automatically added. The list can also include two special entries: +#' @param formula_scale named list specifying whether to standardize continuous predictors. +#' If \code{NULL} (default), no standardization is applied. If a named list is provided, +#' continuous predictors with \code{TRUE} values will be standardized (mean-centered and +#' scaled by standard deviation). The intercept is never standardized. +#' \describe{ +#' \item{\code{"__default_continuous"}}{A prior to use for any continuous predictors +#' (including the intercept) that are not explicitly specified in the prior list.} +#' \item{\code{"__default_factor"}}{A prior to use for any factor predictors +#' (including interactions involving factors) that are not explicitly specified +#' in the prior list.} +#' } +#' These default priors allow for more concise specification when many predictors +#' share the same prior distribution. #' #' @details When a formula with \code{-1} (no intercept) is specified, the #' function automatically removes the \code{-1}, adds an intercept back to the #' formula, and includes a spike(0) prior for the intercept to ensure equivalent #' model behavior while maintaining consistent formula parsing. #' +#' When using default priors (\code{"__default_continuous"} or \code{"__default_factor"}), +#' explicitly specified priors for individual terms take precedence over the defaults. +#' The defaults are only applied to terms that are not already in the prior list. +#' #' @examples #' # simulate data #' set.seed(1) @@ -53,12 +73,24 @@ #' parameter = "mu", data = df, prior_list = prior_list_no_intercept) #' # Equivalent to specifying intercept = prior("spike", list(0)) #' +#' # using default priors for simpler specification +#' prior_list_defaults <- list( +#' "__default_continuous" = prior("normal", list(0, 1)), +#' "__default_factor" = prior_factor("normal", list(0, 0.5), contrast = "treatment") +#' ) +#' formula_defaults <- JAGS_formula( +#' formula = ~ x_cont + x_fac3, +#' parameter = "mu", data = df, prior_list = prior_list_defaults) +#' # intercept and x_cont get the default continuous prior +#' # x_fac3 gets the default factor prior +#' #' @return \code{JAGS_formula} returns a list containing the formula JAGS syntax, -#' JAGS data object, and modified prior_list. +#' JAGS data object, modified prior_list, and (if standardization was applied) a +#' \code{formula_scale} list with standardization information for back-transformation. #' #' @seealso [JAGS_fit()] #' @export -JAGS_formula <- function(formula, parameter, data, prior_list){ +JAGS_formula <- function(formula, parameter, data, prior_list, formula_scale = NULL){ if(!is.language(formula)) stop("'formula' must be a formula") @@ -68,10 +100,17 @@ JAGS_formula <- function(formula, parameter, data, prior_list){ check_list(prior_list, "prior_list") if(any(!sapply(prior_list, is.prior))) stop("'prior_list' must be a list of priors.") + # formula_scale can be TRUE/FALSE (apply to all) or a named list + if(!is.null(formula_scale) && !is.logical(formula_scale) && !is.list(formula_scale)){ + stop("'formula_scale' must be NULL, TRUE, FALSE, or a named list") + } # remove the specified response formula <- .remove_response(formula) + # store log(intercept) attribute (for models relying on mu = log(intercept) + sum(beta_i * x_i) trick + # exp(mu) = intercept * exp(sum(beta_i * x_i)) (e.g., Poisson regression / regression with log link etc...) + log_intercept <- isTRUE(attr(formula, "log(intercept)")) # store expressions (included later as the literal character input) expressions <- .extract_expressions(formula) # store random effects (included later via a formula interface) @@ -126,6 +165,30 @@ JAGS_formula <- function(formula, parameter, data, prior_list){ # remove the random effects specific priors from the prior list prior_list <- prior_list[.get_grouping_factor(names(prior_list)) == ""] } + + # handle default priors: __default_factor and __default_continuous + default_factor_prior <- prior_list[["__default_factor"]] + default_continuous_prior <- prior_list[["__default_continuous"]] + has_defaults <- !is.null(default_factor_prior) || !is.null(default_continuous_prior) + + # remove default priors from prior_list before validation + prior_list[["__default_factor"]] <- NULL + prior_list[["__default_continuous"]] <- NULL + + # fill in missing priors with defaults based on term type + if(has_defaults){ + for(term in model_terms){ + if(!term %in% names(prior_list)){ + term_type <- model_terms_type[[term]] + if(term_type == "factor" && !is.null(default_factor_prior)){ + prior_list[[term]] <- default_factor_prior + }else if(term_type == "continuous" && !is.null(default_continuous_prior)){ + prior_list[[term]] <- default_continuous_prior + } + } + } + } + # check that all predictors have a prior distribution check_list(prior_list, "prior_list", check_names = model_terms, allow_other = FALSE, all_objects = TRUE) @@ -162,6 +225,32 @@ JAGS_formula <- function(formula, parameter, data, prior_list){ stop(paste0("Unsupported prior distribution defined for '", continuous, "' continuous variable. See '?prior' for details.")) } } + + # standardize continuous predictors if requested + scale_info <- list() + if(!is.null(formula_scale)){ + for(continuous in names(predictors_type[predictors_type == "continuous"])){ + # determine if this predictor should be scaled + should_scale <- FALSE + if(is.logical(formula_scale) && length(formula_scale) == 1){ + # formula_scale = TRUE/FALSE applies to all continuous predictors + should_scale <- isTRUE(formula_scale) + }else if(is.list(formula_scale) && !is.null(formula_scale[[continuous]])){ + # named list: check specific predictor + should_scale <- isTRUE(formula_scale[[continuous]]) + } + + if(should_scale){ + # store original mean and sd + scale_info[[continuous]] <- list( + mean = mean(data[, continuous], na.rm = TRUE), + sd = stats::sd(data[, continuous], na.rm = TRUE) + ) + # standardize the predictor + data[, continuous] <- (data[, continuous] - scale_info[[continuous]]$mean) / scale_info[[continuous]]$sd + } + } + } } # get the default design matrix @@ -171,11 +260,11 @@ JAGS_formula <- function(formula, parameter, data, prior_list){ # check whether intercept is unique parameter if(sum(grepl("intercept", names(prior_list))) > 1) stop("only the intercept parameter can contain 'intercept' in its name.") - # check whether any reserved term is in usage - reserved_terms <- c("__xXx__", "__xREx__", "xRE_PRECx", "xRE_CORx", "xRE_Zx", "xRE_STDx", "xRE_COEFx", "xRE_MAPx", "xRE_COEFx", "xRE_DATAx") + # check whether any reserved term is in usage (note: __default_factor/__default_continuous are reserved but already removed from prior_list) + reserved_terms <- c("__xXx__", "__xREx__", "xRE_PRECx", "xRE_CORx", "xRE_Zx", "xRE_STDx", "xRE_COEFx", "xRE_MAPx", "xRE_COEFx", "xRE_DATAx", "__default_factor", "__default_continuous") for(reserved_term in reserved_terms){ - if(any(grepl(reserved_term, names(prior_list)))) - stop(paste0("'", reserved_term, "' string is internally used by the BayesTools package and can't be used for naming variables or prior distributions.")) + if(any(grepl(reserved_term, colnames(data)))) + stop(paste0("'", reserved_term, "' string is internally used by the BayesTools package and can't be used for naming variables.")) } @@ -196,7 +285,12 @@ JAGS_formula <- function(formula, parameter, data, prior_list){ terms_indexes <- attr(model_matrix, "assign") + 1 terms_indexes[1] <- 0 - formula_syntax <- c(formula_syntax, paste0(parameter, "_intercept")) + # use log(intercept) if the formula has the log(intercept) attribute + if(log_intercept){ + formula_syntax <- c(formula_syntax, paste0("log(", parameter, "_intercept)")) + }else{ + formula_syntax <- c(formula_syntax, paste0(parameter, "_intercept")) + } }else{ terms_indexes <- attr(model_matrix, "assign") } @@ -308,12 +402,30 @@ JAGS_formula <- function(formula, parameter, data, prior_list){ attr(prior_list[[i]], "parameter") <- parameter } - return(list( + # preserve log(intercept) attribute on output formula + if(log_intercept){ + attr(formula, "log(intercept)") <- TRUE + } + + output <- list( formula_syntax = formula_syntax, data = JAGS_data, prior_list = prior_list, formula = formula - )) + ) + + # add scale information if standardization was applied + if(exists("scale_info") && length(scale_info) > 0){ + # add parameter prefix to scale_info names for consistency + names(scale_info) <- paste0(parameter, "_", names(scale_info)) + # store the parameter prefix as an attribute for later retrieval + attr(scale_info, "parameter") <- parameter + # store log_intercept attribute for proper unscaling transformation + attr(scale_info, "log_intercept") <- log_intercept + output$formula_scale <- scale_info + } + + return(output) } .JAGS_random_effect_formula <- function(formula, parameter, data, prior_list){ @@ -707,12 +819,17 @@ JAGS_formula <- function(formula, parameter, data, prior_list){ return(trimws(sub("\\|.*$", "", formula))) } .add_intercept_to_formula <- function(formula){ - # converts formula with -1 (no intercept) back to formula with intercept - # by removing the -1 term + # converts formula with -1 or 0 (no intercept) back to formula with intercept + # by removing the -1 or 0 term formula_str <- paste(deparse(formula), collapse = " ") - # Remove various forms of -1 or + -1 + # Remove various forms of -1, + -1, 0, or + 0 formula_str <- gsub("\\s*\\-\\s*1\\s*", "", formula_str) formula_str <- gsub("\\s*\\+\\s*\\-\\s*1\\s*", "", formula_str) + formula_str <- gsub("\\s*\\+\\s*0\\s*", "", formula_str) + # Handle 0 at the start (e.g., "~ 0 + x") + formula_str <- gsub("~\\s*0\\s*\\+\\s*", "~ ", formula_str) + # Handle 0 alone (e.g., "~ 0") + formula_str <- gsub("~\\s*0\\s*$", "~ 1", formula_str) # Handle case where formula becomes empty (just "~") if(grepl("^\\s*~\\s*$", formula_str)){ @@ -734,7 +851,9 @@ JAGS_formula <- function(formula, parameter, data, prior_list){ #' @param fit model fitted with either \link[runjags]{runjags} posterior #' samples obtained with \link[rjags]{rjags-package} #' @param formula formula specifying the right hand side of the assignment (the -#' left hand side is ignored) +#' left hand side is ignored). If the formula has a \code{"log(intercept)"} +#' attribute set to \code{TRUE}, the intercept values will be log-transformed +#' before computing the linear predictor. #' @param parameter name of the parameter created with the formula #' @param data data.frame containing predictors included in the formula #' @param prior_list named list of prior distribution of parameters specified within @@ -847,6 +966,24 @@ JAGS_evaluate_formula <- function(fit, formula, parameter, data, prior_list){ stop(paste0("Unsupported prior distribution defined for '", continuous, "' continuous variable. See '?prior' for details.")) } } + + # apply scaling if predictors were scaled during model fitting + formula_scale <- attr(fit, "formula_scale") + if(!is.null(formula_scale)){ + # Handle nested structure: formula_scale[[parameter]] contains the scaling info + param_scale <- formula_scale[[parameter]] + if(!is.null(param_scale)){ + for(continuous in names(predictors_type[predictors_type == "continuous"])){ + # check if this predictor was scaled (with parameter prefix) + scaled_name <- paste0(parameter, "_", continuous) + if(scaled_name %in% names(param_scale)){ + # apply the same scaling transformation + scale_info <- param_scale[[scaled_name]] + data[, continuous] <- (data[, continuous] - scale_info$mean) / scale_info$sd + } + } + } + } } # get the design matrix @@ -854,6 +991,9 @@ JAGS_evaluate_formula <- function(fit, formula, parameter, data, prior_list){ model_matrix <- stats::model.matrix(model_frame, formula = formula, data = data) ### evaluate the design matrix on the samples -> output[data, posterior] + # check for log(intercept) attribute + log_intercept <- isTRUE(attr(formula, "log(intercept)")) + if(has_intercept){ terms_indexes <- attr(model_matrix, "assign") + 1 @@ -862,7 +1002,12 @@ JAGS_evaluate_formula <- function(fit, formula, parameter, data, prior_list){ # check for scaling factors temp_multiply_by <- .get_parameter_scaling_factor_matrix(term = "intercept", prior_list = prior_list_formula, posterior = posterior, nrow = nrow(data), ncol = nrow(posterior)) - output <- temp_multiply_by * matrix(posterior[,JAGS_parameter_names("intercept", formula_parameter = parameter)], nrow = nrow(data), ncol = nrow(posterior), byrow = TRUE) + # get intercept values and apply log() transformation if log(intercept) attribute is set + intercept_values <- posterior[,JAGS_parameter_names("intercept", formula_parameter = parameter)] + if(log_intercept){ + intercept_values <- log(intercept_values) + } + output <- temp_multiply_by * matrix(intercept_values, nrow = nrow(data), ncol = nrow(posterior), byrow = TRUE) }else{ @@ -1067,6 +1212,452 @@ transform_treatment_samples <- function(samples){ } +# Helper: Parse a term name into its component variable names +# e.g., "mu_x1__xXx__x2" with prefix "mu" -> c("x1", "x2") +# e.g., "mu_intercept" -> character(0) (intercept has no components) +# e.g., "mu_x1" -> c("x1") +.parse_term_components <- function(term_name, prefix) { + # Remove prefix + term_part <- sub(paste0("^", prefix, "_"), "", term_name) + + # Check if it's the intercept + if (term_part == "intercept") { + return(character(0)) + } + + # Split by interaction separator + components <- strsplit(term_part, "__xXx__")[[1]] + return(components) +} + + +# Helper: Check if set A is a subset of set B (including equality) +.is_subset <- function(A, B) { + + length(A) == 0 || all(A %in% B) +} + + +# Helper: Build the transformation matrix for unscaling coefficients +# +# For each target term T and source term S, computes the coefficient M[T,S] such that: +# coef_orig[T] = sum over S of M[T,S] * coef_z[S] +# +# The formula is based on expanding products of (x_i - mu_i)/sigma_i terms. +# For S to contribute to T: +# 1. T_unscaled == S_unscaled (unscaled components must match exactly) +# 2. T_scaled โŠ† S_scaled (T's scaled components are a subset of S's) +# +# The contribution is: (-1)^|extra| * prod(mu_extra) / prod(sigma_S_scaled) +# where extra = S_scaled \ T_scaled +# +# @param term_names Character vector of all term names in the posterior +# @param formula_scale Named list with scaling info (mean, sd) for scaled predictors +# @param prefix The parameter prefix (e.g., "mu") +# @return A square transformation matrix +.build_unscale_matrix <- function(term_names, formula_scale, prefix) { + + n_terms <- length(term_names) + M <- diag(n_terms) # Start with identity matrix + rownames(M) <- colnames(M) <- term_names + + # Extract the variable names that are scaled (without prefix) + scaled_vars <- sub(paste0("^", prefix, "_"), "", names(formula_scale)) + + # Parse all terms into their components + term_components <- lapply(term_names, .parse_term_components, prefix = prefix) + names(term_components) <- term_names + + # For each term, identify scaled vs unscaled components + term_scaled <- lapply(term_components, function(comps) comps[comps %in% scaled_vars]) + term_unscaled <- lapply(term_components, function(comps) comps[!comps %in% scaled_vars]) + + # Warn about high-order interactions + max_order <- max(sapply(term_components, length)) + if (max_order >= 5) { + warning("Model contains ", max_order, "-way or higher interactions. ", + "Unscaling transformation may be computationally intensive.", + immediate. = TRUE) + } + + # Build the transformation matrix + for (t_idx in seq_along(term_names)) { + T_name <- term_names[t_idx] + T_scaled <- term_scaled[[T_name]] + T_unscaled <- term_unscaled[[T_name]] + + for (s_idx in seq_along(term_names)) { + S_name <- term_names[s_idx] + S_scaled <- term_scaled[[S_name]] + S_unscaled <- term_unscaled[[S_name]] + + # Check contribution conditions + # 1. Unscaled parts must match exactly + if (!setequal(T_unscaled, S_unscaled)) next + + # 2. T_scaled must be a subset of S_scaled + if (!.is_subset(T_scaled, S_scaled)) next + + # 3. S must have at least one scaled component (otherwise no transformation needed) + if (length(S_scaled) == 0) { + # No scaling for this source term - keep identity (already set) + next + } + + # Compute the coefficient + extra_scaled <- setdiff(S_scaled, T_scaled) + + # Sign: (-1)^|extra| + sign <- (-1)^length(extra_scaled) + + # Product of means for extra scaled components + if (length(extra_scaled) > 0) { + extra_params <- paste0(prefix, "_", extra_scaled) + mean_product <- prod(sapply(extra_params, function(p) formula_scale[[p]]$mean)) + } else { + mean_product <- 1 + } + + # Product of SDs for all scaled components in S + S_scaled_params <- paste0(prefix, "_", S_scaled) + sd_product <- prod(sapply(S_scaled_params, function(p) formula_scale[[p]]$sd)) + + # Contribution coefficient + M[t_idx, s_idx] <- sign * mean_product / sd_product + } + } + + return(M) +} + + +# Helper: Apply unscaling transformation to a matrix of posterior samples +# +# @param posterior Matrix with samples in rows, parameters in columns +# Apply the unscaling transformation to posterior samples +# +# @param posterior Matrix of posterior samples with parameter names as column names +# @param formula_scale Nested list with scaling info keyed by parameter name: +# list(mu = list(mu_x1 = list(mean, sd)), log_sigma = list(log_sigma_x = list(mean, sd))) +# @return Transformed posterior matrix +.apply_unscale_transform <- function(posterior, formula_scale) { + + if (is.null(formula_scale) || length(formula_scale) == 0) { + return(posterior) + } + + # Handle nested structure: iterate over each parameter + for (param_name in names(formula_scale)) { + param_scale <- formula_scale[[param_name]] + posterior <- .apply_unscale_transform_single(posterior, param_scale, prefix = param_name) + } + + return(posterior) +} + +# Helper: Apply unscaling for a single parameter's predictors +# @param posterior Matrix of posterior samples +# @param formula_scale Flat list of scaling info: list(mu_x1 = list(mean, sd), mu_x2 = list(mean, sd)) +# @param prefix Parameter prefix (e.g., "mu") +# @return Transformed posterior matrix +.apply_unscale_transform_single <- function(posterior, formula_scale, prefix) { + + if (is.null(formula_scale) || length(formula_scale) == 0) { + return(posterior) + } + + # Check if this parameter uses log(intercept) + log_intercept <- isTRUE(attr(formula_scale, "log_intercept")) + intercept_col <- paste0(prefix, "_intercept") + + # Identify which columns are affected by the transformation + affected_cols <- grep(paste0("^", prefix, "_"), colnames(posterior), value = TRUE) + + if (length(affected_cols) == 0) { + return(posterior) + } + + # For log(intercept): transform to log scale before unscaling, then exp() back + # This works because: log_sigma = log(intercept) + beta * x_z + # is equivalent to: log_sigma = log_int + beta * x_z (standard additive form) + # where log_int = log(intercept) + if (log_intercept && intercept_col %in% colnames(posterior)) { + posterior[, intercept_col] <- log(posterior[, intercept_col]) + } + + # Build and apply standard transformation matrix + M <- .build_unscale_matrix(affected_cols, formula_scale, prefix) + posterior[, affected_cols] <- posterior[, affected_cols, drop = FALSE] %*% t(M) + + # Transform intercept back from log scale + if (log_intercept && intercept_col %in% colnames(posterior)) { + posterior[, intercept_col] <- exp(posterior[, intercept_col]) + } + + return(posterior) +} + + +#' @title Transform standardized posterior samples back to original scale +#' +#' @description Transforms posterior samples from standardized continuous +#' predictors back to the original scale. This function is used when predictors +#' were standardized during model fitting via the \code{formula_scale} parameter. +#' +#' @param fit a fitted model object with \code{formula_scale} attribute, or +#' a matrix of posterior samples +#' @param formula_scale nested list containing standardization information keyed by +#' parameter name. Each parameter entry contains scaling info (mean and sd) for +#' each standardized predictor, e.g., \code{list(mu = list(mu_x1 = list(mean = 0, sd = 1)))}. +#' If \code{fit} is provided and has a \code{formula_scale} attribute, this will be used automatically. +#' +#' @details The function transforms regression coefficients and intercepts +#' to account for predictor standardization using a combinatorial approach that +#' correctly handles interactions of any order. +#' +#' For a k-way interaction between standardized predictors, the expansion of +#' \eqn{\prod_{i} (x_i - \mu_i)/\sigma_i} contributes to all lower-order terms. +#' The contribution to a target term T from a source term S (where T is a subset +#' of S's scaled components) is: +#' \deqn{(-1)^{|extra|} \cdot \prod_{i \in extra} \mu_i / \prod_{i \in S_{scaled}} \sigma_i} +#' where \eqn{extra = S_{scaled} \setminus T_{scaled}}. +#' +#' @return \code{transform_scale_samples} returns posterior samples transformed +#' back to the original predictor scale. +#' +#' @seealso [JAGS_formula()] [JAGS_fit()] +#' +#' @export +transform_scale_samples <- function(fit, formula_scale = NULL){ + + # extract formula_scale from fit if available + if(is.null(formula_scale) && !is.null(attr(fit, "formula_scale"))){ + formula_scale <- attr(fit, "formula_scale") + } + + if(is.null(formula_scale) || length(formula_scale) == 0){ + # no scaling information, return as is + return(fit) + } + + check_list(formula_scale, "formula_scale") + + # extract posterior samples + if(inherits(fit, "runjags") || inherits(fit, "BayesTools_fit")){ + posterior <- as.matrix(.fit_to_posterior(fit)) + }else if(is.matrix(fit)){ + posterior <- fit + }else{ + stop("'fit' must be a fitted model object or a matrix of posterior samples.") + } + + # Apply the combinatorial unscaling transformation + posterior <- .apply_unscale_transform(posterior, formula_scale) + + return(posterior) +} + + +#' @title Transform prior samples to original scale +#' +#' @description Generate prior samples and transform them using the same +#' matrix transformation as posterior samples. This is the correct approach for +#' visualizing priors on the original (unscaled) scale, especially for the intercept +#' which depends on contributions from multiple coefficient priors. +#' +#' @param fit a fitted model object with \code{prior_list} and optionally +#' \code{formula_scale} attributes +#' @param n_samples number of samples to generate (default: 10000) +#' @param seed random seed for reproducibility (optional) +#' @param formula_scale optional nested list containing standardization information. +#' If not provided, extracted from \code{fit} attribute. +#' +#' @details When models use auto-scaling (standardizing predictors), the posterior +#' samples are on the standardized scale. To correctly visualize priors on the +#' original scale, we cannot simply apply a linear transformation to individual +#' priors because the intercept on the original scale is a weighted sum of +#' multiple priors: +#' +#' \deqn{\beta_0^{orig} = \beta_0^* - \sum_i \frac{\mu_i}{\sigma_i} \beta_i^*} +#' + +#' This function generates samples from ALL priors simultaneously and applies +#' the same matrix transformation used for posterior samples, which correctly +#' handles the intercept and all other parameters. +#' +#' @return A matrix of prior samples on the original (unscaled) scale, with +#' columns matching the structure of posterior samples. +#' +#' @seealso [transform_scale_samples()] [plot_posterior()] +#' +#' @examples +#' # With a fitted model that used formula_scale: +#' # prior_samples <- transform_prior_samples(fit, n_samples = 10000) +#' # This can then be used with density() or for custom plotting +#' +#' @export +transform_prior_samples <- function(fit, n_samples = 10000, seed = NULL, formula_scale = NULL){ + + check_int(n_samples, "n_samples", lower = 1) + check_int(seed, "seed", allow_NULL = TRUE) + + # Extract prior_list from fit + + prior_list <- attr(fit, "prior_list") + + if(is.null(prior_list)){ + stop("'fit' must have 'prior_list' attribute.") + } + + # Extract formula_scale from fit if not provided + if(is.null(formula_scale)){ + formula_scale <- attr(fit, "formula_scale") + } + + # Get posterior column names for structure matching + if(inherits(fit, "runjags") || inherits(fit, "BayesTools_fit")){ + posterior <- as.matrix(.fit_to_posterior(fit)) + }else{ + stop("'fit' must be a fitted model object.") + } + + # Generate prior samples matching posterior column structure + prior_samples <- .generate_prior_sample_matrix( + prior_list = prior_list, + n_samples = n_samples, + column_names = colnames(posterior), + seed = seed + ) + + # Apply same transformation as posterior + if(!is.null(formula_scale) && length(formula_scale) > 0){ + prior_samples <- .apply_unscale_transform(prior_samples, formula_scale) + } + + return(prior_samples) +} + + +# Helper: Generate a matrix of prior samples matching posterior structure +# +# @param prior_list Named list of prior objects +# @param n_samples Number of samples to generate +# @param column_names Optional vector of column names to match (filters output) +# @param seed Optional random seed +# @return Matrix with prior samples (rows = samples, columns = parameters) +.generate_prior_sample_matrix <- function(prior_list, n_samples, column_names = NULL, seed = NULL){ + + if(!is.null(seed)){ + set.seed(seed) + } + + # Determine which parameters to sample + param_names <- names(prior_list) + + if(is.null(param_names) || length(param_names) == 0){ + stop("'prior_list' must be a named list of priors.") + } + + # Initialize list to collect samples (handles varying column counts per prior) + samples_list <- list() + + for(param_name in param_names){ + prior <- prior_list[[param_name]] + + if(is.null(prior)){ + # No prior for this parameter - use zeros + samples_list[[param_name]] <- matrix(0, nrow = n_samples, ncol = 1) + colnames(samples_list[[param_name]]) <- param_name + + }else if(is.prior.none(prior)){ + # No effect prior - use zeros + samples_list[[param_name]] <- matrix(0, nrow = n_samples, ncol = 1) + colnames(samples_list[[param_name]]) <- param_name + + }else if(is.prior.point(prior)){ + # Point prior - constant values + samples_list[[param_name]] <- matrix( + prior$parameters[["location"]], + nrow = n_samples, + ncol = 1 + ) + colnames(samples_list[[param_name]]) <- param_name + + }else if(is.prior.factor(prior)){ + # Factor priors return matrix from rng + temp_samples <- rng(prior, n_samples, transform_factor_samples = TRUE) + if(is.matrix(temp_samples)){ + # Multiple columns for factor levels + n_levels <- ncol(temp_samples) + col_names <- paste0(param_name, "[", 1:n_levels, "]") + colnames(temp_samples) <- col_names + samples_list[[param_name]] <- temp_samples + }else{ + # Single column + samples_list[[param_name]] <- matrix(temp_samples, nrow = n_samples, ncol = 1) + colnames(samples_list[[param_name]]) <- param_name + } + + }else if(is.prior.simple(prior)){ + # Simple priors - single column + samples_list[[param_name]] <- matrix( + rng(prior, n_samples), + nrow = n_samples, + ncol = 1 + ) + colnames(samples_list[[param_name]]) <- param_name + + }else if(is.prior.vector(prior)){ + # Vector priors return matrix from rng + temp_samples <- rng(prior, n_samples) + if(is.matrix(temp_samples)){ + n_cols <- ncol(temp_samples) + col_names <- paste0(param_name, "[", 1:n_cols, "]") + colnames(temp_samples) <- col_names + samples_list[[param_name]] <- temp_samples + }else{ + samples_list[[param_name]] <- matrix(temp_samples, nrow = n_samples, ncol = 1) + colnames(samples_list[[param_name]]) <- param_name + } + + }else{ + # Fallback for other prior types - try rng + temp_samples <- tryCatch( + rng(prior, n_samples), + error = function(e){ + warning(sprintf("Could not generate samples for prior '%s': %s. Using zeros.", + param_name, e$message)) + rep(0, n_samples) + } + ) + + if(is.matrix(temp_samples)){ + n_cols <- ncol(temp_samples) + col_names <- paste0(param_name, "[", 1:n_cols, "]") + colnames(temp_samples) <- col_names + samples_list[[param_name]] <- temp_samples + }else{ + samples_list[[param_name]] <- matrix(temp_samples, nrow = n_samples, ncol = 1) + colnames(samples_list[[param_name]]) <- param_name + } + } + } + + # Combine all samples into one matrix + samples <- do.call(cbind, samples_list) + + # Filter to match column_names if provided + if(!is.null(column_names)){ + available_cols <- intersect(column_names, colnames(samples)) + if(length(available_cols) > 0){ + samples <- samples[, available_cols, drop = FALSE] + } + } + + return(samples) +} + + #' @title BayesTools Contrast Matrices #' #' @description BayesTools provides several contrast matrix functions for Bayesian factor analysis. @@ -1201,6 +1792,9 @@ contr.independent <- function(n, contrasts = TRUE){ #' @param formula_random a vector of random effects grouping factors #' @param formula_prefix whether the \code{formula_parameters} names should be #' kept. Defaults to \code{TRUE}. +#' @param formula_scale optional nested list containing scaling info. When provided, +#' intercepts from parameters with \code{log_intercept = TRUE} attribute will be +#' renamed to \code{exp(intercept)}. #' #' @examples #' format_parameter_names(c("mu_x_cont", "mu_x_fac3t", "mu_x_fac3t__xXx__x_cont"), @@ -1214,12 +1808,25 @@ contr.independent <- function(n, contrasts = TRUE){ NULL #' @rdname parameter_names -format_parameter_names <- function(parameters, formula_parameters = NULL, formula_random = NULL, formula_prefix = TRUE){ +format_parameter_names <- function(parameters, formula_parameters = NULL, formula_random = NULL, formula_prefix = TRUE, formula_scale = NULL){ check_char(parameters, "parameters", check_length = FALSE) check_char(formula_random, "formula_random", check_length = FALSE, allow_NULL = TRUE) check_char(formula_parameters, "formula_parameters", check_length = FALSE, allow_NULL = TRUE) check_bool(formula_prefix, "formula_prefix") + check_list(formula_scale, "formula_scale", allow_NULL = TRUE) + + # rename intercept to exp(intercept) for parameters with log_intercept attribute + if(!is.null(formula_scale)){ + for(param_name in names(formula_scale)){ + if(isTRUE(attr(formula_scale[[param_name]], "log_intercept"))){ + intercept_name <- paste0(param_name, "_intercept") + if(intercept_name %in% parameters){ + parameters[parameters == intercept_name] <- paste0(param_name, "_exp(intercept)") + } + } + } + } for(i in seq_along(formula_parameters)){ parameters[grep(paste0(formula_parameters[i], "_"), parameters)] <- gsub( diff --git a/R/JAGS-marglik.R b/R/JAGS-marglik.R index d6447e86..0bedec93 100644 --- a/R/JAGS-marglik.R +++ b/R/JAGS-marglik.R @@ -25,6 +25,10 @@ #' (names of the lists correspond to the parameter name created by each of the formula and #' the names of the prior distribution correspond to the parameter names) of parameters specified #' within the \code{formula} +#' @param formula_scale_list named list of named lists for standardizing continuous predictors +#' (names of the lists correspond to the parameter name created by each of the formula). +#' Each entry should be a named list where continuous predictors with \code{TRUE} values will +#' be standardized. Defaults to \code{NULL} (no standardization). #' @param add_parameters vector of additional parameter names that should be used #' in bridgesampling but were not specified in the \code{prior_list} #' @param add_bounds list with two name vectors (\code{"lb"} and \code{"up"}) @@ -70,7 +74,7 @@ #' @return \code{JAGS_bridgesampling} returns an object of class 'bridge'. #' #' @export -JAGS_bridgesampling <- function(fit, log_posterior, data = NULL, prior_list = NULL, formula_list = NULL, formula_data_list = NULL, formula_prior_list = NULL, +JAGS_bridgesampling <- function(fit, log_posterior, data = NULL, prior_list = NULL, formula_list = NULL, formula_data_list = NULL, formula_prior_list = NULL, formula_scale_list = NULL, add_parameters = NULL, add_bounds = NULL, maxiter = 10000, silent = TRUE, ...){ @@ -80,6 +84,7 @@ JAGS_bridgesampling <- function(fit, log_posterior, data = NULL, prior_list = NU check_list(formula_list, "formula_list", allow_NULL = TRUE) check_list(formula_data_list, "formula_data_list", check_names = names(formula_list), allow_other = FALSE, all_objects = TRUE, allow_NULL = is.null(formula_list)) check_list(formula_prior_list, "formula_prior_list", check_names = names(formula_list), allow_other = FALSE, all_objects = TRUE, allow_NULL = is.null(formula_list)) + check_list(formula_scale_list, "formula_scale_list", allow_NULL = TRUE) # extract the posterior distribution @@ -92,10 +97,11 @@ JAGS_bridgesampling <- function(fit, log_posterior, data = NULL, prior_list = NU formula_output <- list() for(parameter in names(formula_list)){ formula_output[[parameter]] <- JAGS_formula( - formula = formula_list[[parameter]], - parameter = parameter, - data = formula_data_list[[parameter]], - prior_list = formula_prior_list[[parameter]]) + formula = formula_list[[parameter]], + parameter = parameter, + data = formula_data_list[[parameter]], + prior_list = formula_prior_list[[parameter]], + formula_scale = if(!is.null(formula_scale_list)) formula_scale_list[[parameter]] else NULL) } # merge with the rest of the input @@ -119,26 +125,26 @@ JAGS_bridgesampling <- function(fit, log_posterior, data = NULL, prior_list = NU ### define the marglik function - full_log_posterior <- function(samples.row, data, prior_list, formula_data_list, formula_prior_list, add_parameters, ...){ + full_log_posterior <- function(samples.row, data, prior_list, formula_list, formula_data_list, formula_prior_list, add_parameters, ...){ # prepare object for holding the parameters, later accessible to the user specified 'log_posterior' parameters <- list() - if(!is.null(prior_list)){ + if(length(prior_list) > 0){ parameters <- c(parameters, JAGS_marglik_parameters(samples.row, prior_list)) } - if(!is.null(formula_prior_list)){ - parameters <- c(parameters, JAGS_marglik_parameters_formula(samples.row, formula_data_list, formula_prior_list, parameters)) + if(length(formula_prior_list) > 0){ + parameters <- c(parameters, JAGS_marglik_parameters_formula(samples.row, formula_list, formula_data_list, formula_prior_list, parameters)) } - if(!is.null(add_parameters)){ + if(length(add_parameters) > 0){ parameters <- c(parameters, samples.row[add_parameters]) } # compute the marginal likelihoods marglik <- 0 - if(!is.null(prior_list)){ + if(length(prior_list) > 0){ marglik <- marglik + JAGS_marglik_priors(samples.row, prior_list) } - if(!is.null(formula_prior_list)){ + if(length(formula_prior_list) > 0){ marglik <- marglik + JAGS_marglik_priors_formula(samples.row, formula_prior_list) } marglik <- marglik + log_posterior(parameters = parameters, data = data, ...) @@ -153,6 +159,7 @@ JAGS_bridgesampling <- function(fit, log_posterior, data = NULL, prior_list = NU data = data, log_posterior = full_log_posterior, prior_list = prior_list, + formula_list = formula_list, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list, lb = attr(bridgesampling_posterior, "lb"), @@ -939,7 +946,7 @@ JAGS_marglik_parameters <- function(samples, prior_list){ # } #' @rdname JAGS_marglik_parameters -JAGS_marglik_parameters_formula <- function(samples, formula_data_list, formula_prior_list, prior_list_parameters){ +JAGS_marglik_parameters_formula <- function(samples, formula_list, formula_data_list, formula_prior_list, prior_list_parameters){ # return empty list in case that no prior was specified if(length(formula_prior_list) == 0){ @@ -949,13 +956,15 @@ JAGS_marglik_parameters_formula <- function(samples, formula_data_list, for parameters <- list() for(parameter in names(formula_prior_list)){ - parameters[[parameter]] <- .JAGS_marglik_parameters_formula_get(samples, parameter, formula_data_list[[parameter]], formula_prior_list[[parameter]], prior_list_parameters) + # check for log(intercept) attribute on the formula + log_intercept <- if(!is.null(formula_list[[parameter]])) isTRUE(attr(formula_list[[parameter]], "log(intercept)")) else FALSE + parameters[[parameter]] <- .JAGS_marglik_parameters_formula_get(samples, parameter, formula_data_list[[parameter]], formula_prior_list[[parameter]], prior_list_parameters, log_intercept) } return(parameters) } -.JAGS_marglik_parameters_formula_get <- function(samples, parameter, formula_data_list, formula_prior_list, prior_list_parameters){ +.JAGS_marglik_parameters_formula_get <- function(samples, parameter, formula_data_list, formula_prior_list, prior_list_parameters, log_intercept = FALSE){ formula_terms <- names(formula_prior_list) names(formula_data_list) <- gsub("_data", "", names(formula_data_list)) @@ -976,11 +985,21 @@ JAGS_marglik_parameters_formula <- function(samples, formula_data_list, for if(is.prior.point(formula_prior_list[[paste0(parameter, "_intercept")]])){ - output <- multiply_by * rep(formula_prior_list[[paste0(parameter, "_intercept")]][["parameters"]][["location"]], formula_data_list[[paste0("N_", parameter)]]) + intercept_value <- formula_prior_list[[paste0(parameter, "_intercept")]][["parameters"]][["location"]] + # apply log transformation if log(intercept) attribute is set + if(log_intercept){ + intercept_value <- log(intercept_value) + } + output <- multiply_by * rep(intercept_value, formula_data_list[[paste0("N_", parameter)]]) }else{ - output <- multiply_by * rep(samples[[paste0(parameter, "_intercept")]], formula_data_list[[paste0("N_", parameter)]]) + intercept_value <- samples[[paste0(parameter, "_intercept")]] + # apply log transformation if log(intercept) attribute is set + if(log_intercept){ + intercept_value <- log(intercept_value) + } + output <- multiply_by * rep(intercept_value, formula_data_list[[paste0("N_", parameter)]]) } diff --git a/R/marginal-distributions.R b/R/marginal-distributions.R index 3a1395a7..8e55e300 100644 --- a/R/marginal-distributions.R +++ b/R/marginal-distributions.R @@ -854,6 +854,11 @@ marginal_posterior <- function(samples, parameter, formula = NULL, at = NULL, pr }else{ + # keep the same seed across levels + if(is.null(seed)){ + seed <- sample(666666, 1) + } + samples <- lapply(1:levels, function(i) .mix_priors.simple(priors, paste0(parameter, "[", i, "]"), seed, n_samples)) sample_ind <- attr(samples[[1]], "sample_ind") @@ -884,6 +889,11 @@ marginal_posterior <- function(samples, parameter, formula = NULL, at = NULL, pr }else{ + # keep the same seed across levels + if(is.null(seed)){ + seed <- sample(666666, 1) + } + samples <- lapply(1:levels, function(i) .mix_priors.simple(priors, paste0(parameter, "[", i, "]"), seed, n_samples)) sample_ind <- attr(samples[[1]], "sample_ind") diff --git a/R/model-averaging-plots.R b/R/model-averaging-plots.R index a550c9bb..554986e2 100644 --- a/R/model-averaging-plots.R +++ b/R/model-averaging-plots.R @@ -3,6 +3,10 @@ #' @param prior_list list of prior distributions #' @param prior_list_mu list of priors for the mu parameter #' required when plotting PET-PEESE +#' @param effect_direction direction of the effect for PET-PEESE +#' regression. Use \code{"positive"} (default) for +#' \code{mu + PET*se + PEESE*se^2} or \code{"negative"} for +#' \code{mu - PET*se - PEESE*se^2}. #' @param ... additional arguments #' @inheritParams density.prior #' @inheritParams plot.prior @@ -15,12 +19,9 @@ plot_prior_list <- function(prior_list, plot_type = "base", x_seq = NULL, xlim = NULL, x_range_quant = NULL, n_points = 500, n_samples = 10000, force_samples = FALSE, + individual = FALSE, show_figures = if(individual) 1 else NULL, transformation = NULL, transformation_arguments = NULL, transformation_settings = FALSE, - rescale_x = FALSE, par_name = NULL, prior_list_mu = NULL, ...){ - - # TODO: add plots for individual parameters for weightfunction and PET-PEESE - individual = FALSE - show_figures = if(individual) 1 else NULL + rescale_x = FALSE, par_name = NULL, prior_list_mu = NULL, effect_direction = "positive", ...){ # check input (most arguments are checked within density) check_list(prior_list, "prior_list") @@ -30,6 +31,7 @@ plot_prior_list <- function(prior_list, plot_type = "base", check_bool(individual, "individual") check_bool(rescale_x, "rescale_x") check_int(show_figures, "show_figures", allow_NULL = TRUE) + check_char(effect_direction, "effect_direction", allow_values = c("positive", "negative")) # check that there is no mixing of PET-PEESE and weightfunctions if(any(sapply(prior_list, is.prior.weightfunction)) & (any(sapply(prior_list, is.prior.PET)) | any(sapply(prior_list, is.prior.PEESE)))) stop("weightfunction and PET-PEESE priors cannot be mixed within a 'prior_list'.") @@ -49,7 +51,7 @@ plot_prior_list <- function(prior_list, plot_type = "base", } - if(prior_type == "PETPEESE"){ + if(prior_type == "PETPEESE" && !individual){ check_list(prior_list_mu, "prior_list_mu", check_length = length(prior_list)) if(is.prior(prior_list_mu) | !all(sapply(prior_list_mu, is.prior))) stop("'prior_list_mu' must be a list of priors (priors for the mu parameter are required for plotting PET-PEESE).") @@ -71,23 +73,27 @@ plot_prior_list <- function(prior_list, plot_type = "base", # get the plotting data - if(prior_type == "weightfunction"){ + if(prior_type == "weightfunction" && !individual){ + # special dispatching for visualizing the whole weightfunction # use samples (not sure how to provide analytic solution for this yes) plot_data <- .plot_data_prior_list.weightfunction(prior_list, x_seq = x_seq, x_range = xlim, x_range_quant = x_range_quant, n_points = n_points, n_samples = n_samples) plot <- .plot.prior.weightfunction(prior_list, plot_type = plot_type, plot_data = plot_data, rescale_x = rescale_x, par_name = par_name, ...) - }else if(prior_type == "PETPEESE"){ + }else if(prior_type == "PETPEESE" && !individual){ + # special dispatching for visualizing the PET-PEESE regression # use samples (not sure how to provide analytic solution for this yes) plot_data <- .plot_data_prior_list.PETPEESE(prior_list, x_seq = x_seq, x_range = xlim, x_range_quant = x_range_quant, n_points = n_points, n_samples = n_samples, transformation = transformation, transformation_arguments = transformation_arguments, - transformation_settings = transformation_settings, prior_list_mu = prior_list_mu) + transformation_settings = transformation_settings, prior_list_mu = prior_list_mu, + effect_direction = effect_direction) plot <- .plot.prior.PETPEESE(prior_list, plot_type = plot_type, plot_data = plot_data, par_name = par_name, ...) }else if(prior_type %in% c("simple", "orthonormal", "meandif")){ + # regular prior distributions (or individual plots for parameters from weightfunctions/PET-PEESE) # solve analytically plot_data <- .plot_data_prior_list.simple(prior_list, x_seq = x_seq, x_range = xlim, x_range_quant = x_range_quant, @@ -427,8 +433,70 @@ plot_prior_list <- function(prior_list, plot_type = "base", return(out) } +.plot_data_prior_list.weightparameter<- function(prior_list, parameter, n_points, n_samples){ + + # join the same priors + prior_list <- .simplify_prior_list(prior_list) + + prior_weights <- sapply(prior_list, function(p)p$prior_weights) + mixing_prop <- prior_weights / sum(prior_weights) + + prior_list <- prior_list[round(n_samples * mixing_prop) > 0] + mixing_prop <- mixing_prop[round(n_samples * mixing_prop) > 0] + + # replace non-weighfunctions from prior mixture + if(any(!c(sapply(prior_list, is.prior.weightfunction) | sapply(prior_list, is.prior.none)))){ + for(i in seq_along(prior_list)){ + if(!(is.prior.weightfunction(prior_list[[i]]) | is.prior.none(prior_list[[i]]))){ + prior_list[[i]] <- prior_none(prior_weights = prior_weights[i]) + } + } + } + + # get the samples + samples_list <- list() + for(i in seq_along(prior_list)){ + if(is.prior.weightfunction(prior_list[[i]])){ + samples_list[[i]] <- rng(prior_list[[i]], round(n_samples * mixing_prop[i])) + }else{ + samples_list[[i]] <- list() + } + + } + + # merge the samples + omega_mapping <- weightfunctions_mapping(prior_list) + omega_cuts <- weightfunctions_mapping(prior_list, cuts_only = TRUE) + + # join samples + samples <- matrix(nrow = 0, ncol = length(omega_cuts) - 1) + models_ind <- NULL + for(i in seq_along(samples_list)){ + if(is.prior.weightfunction(prior_list[[i]])){ + samples <- rbind(samples, samples_list[[i]][,omega_mapping[[i]]]) + models_ind <- c(models_ind, rep(i, nrow(samples_list[[i]]))) + }else{ + samples <- rbind(samples, matrix(1, ncol = length(omega_cuts) - 1, nrow = round(n_samples * mixing_prop[i]))) + models_ind <- c(models_ind, rep(i,round(n_samples * mixing_prop[i]))) + } + } + + x_seq <- omega_cuts + omega_names <- sapply(1:(length(omega_cuts)-1), function(i)paste0("omega[",omega_cuts[i],",",omega_cuts[i+1],"]")) + colnames(samples) <- omega_names + attr(samples, "prior_list") <- prior_list + attr(samples, "models_ind") <- models_ind + + samples <- list("omega" = samples) + + # re-use the posterior function with prior samples + out <- .plot_data_samples.weightparameter(samples, parameter = parameter, n_points = n_points) + + return(out) +} .plot_data_prior_list.PETPEESE <- function(prior_list, x_seq, x_range, x_range_quant, n_points, n_samples, - transformation, transformation_arguments, transformation_settings, prior_list_mu){ + transformation, transformation_arguments, transformation_settings, prior_list_mu, + effect_direction = "positive"){ # TODO: add dependency on the mu parameter as well if(is.null(x_seq)){ @@ -461,10 +529,12 @@ plot_prior_list <- function(prior_list, plot_type = "base", } samples <- do.call(rbind, samples_list) - # compute PET-PEESE (mu + PET*se + PEESE*se^2) + # compute PET-PEESE (mu +/- PET*se +/- PEESE*se^2) + # effect_direction controls the sign: "positive" uses +, "negative" uses - + direction_sign <- if(effect_direction == "negative") -1 else 1 x_sam <- matrix(samples[,1], nrow = length(samples), ncol = length(x_seq)) + - matrix(samples[,2], nrow = length(samples), ncol = length(x_seq)) * matrix(x_seq, nrow = length(samples), ncol = length(x_seq), byrow = TRUE) + - matrix(samples[,3], nrow = length(samples), ncol = length(x_seq)) * matrix(x_seq^2, nrow = length(samples), ncol = length(x_seq), byrow = TRUE) + direction_sign * matrix(samples[,2], nrow = length(samples), ncol = length(x_seq)) * matrix(x_seq, nrow = length(samples), ncol = length(x_seq), byrow = TRUE) + + direction_sign * matrix(samples[,3], nrow = length(samples), ncol = length(x_seq)) * matrix(x_seq^2, nrow = length(samples), ncol = length(x_seq), byrow = TRUE) # transform the PEESE parameter if requested if(!is.null(transformation)){ @@ -712,12 +782,9 @@ plot_prior_list <- function(prior_list, plot_type = "base", #' @export lines_prior_list <- function(prior_list, xlim = NULL, x_seq = NULL, x_range_quant = NULL, n_points = 500, n_samples = 10000, force_samples = FALSE, + individual = FALSE, show_figures = if(individual) 1 else NULL, transformation = NULL, transformation_arguments = NULL, transformation_settings = FALSE, - rescale_x = FALSE, scale_y2 = NULL, prior_list_mu = NULL, ...){ - - # TODO: add plots for individual parameters for weightfunction and PET-PEESE - individual = FALSE - show_parameter = if(individual) 1 else NULL + rescale_x = FALSE, scale_y2 = NULL, prior_list_mu = NULL, effect_direction = "positive", ...){ # check input (most arguments are checked within density) check_list(prior_list, "prior_list") @@ -725,8 +792,9 @@ lines_prior_list <- function(prior_list, xlim = NULL, x_seq = NULL, x_range_quan stop("'prior_list' must be a list of priors.") check_bool(individual, "individual") check_bool(rescale_x, "rescale_x") - check_int(show_parameter, "show_parameter", allow_NULL = TRUE) + check_int(show_figures, "show_figures", allow_NULL = TRUE) check_real(scale_y2, "scale_y2", lower = 0, allow_NULL = TRUE) + check_char(effect_direction, "effect_direction", allow_values = c("positive", "negative")) # get the plotting type @@ -773,7 +841,8 @@ lines_prior_list <- function(prior_list, xlim = NULL, x_seq = NULL, x_range_quan plot_data <- .plot_data_prior_list.PETPEESE(prior_list, x_seq = x_seq, x_range = xlim, x_range_quant = x_range_quant, n_points = n_points, n_samples = n_samples, transformation = transformation, transformation_arguments = transformation_arguments, - transformation_settings = transformation_settings, prior_list_mu = prior_list_mu) + transformation_settings = transformation_settings, prior_list_mu = prior_list_mu, + effect_direction = effect_direction) .lines.prior.PETPEESE(prior_list, plot_data = plot_data, ...) }else if(prior_type == "simple"){ @@ -813,12 +882,9 @@ lines_prior_list <- function(prior_list, xlim = NULL, x_seq = NULL, x_range_quan #' @export geom_prior_list <- function(prior_list, xlim = NULL, x_seq = NULL, x_range_quant = NULL, n_points = 500, n_samples = 10000, force_samples = FALSE, + individual = FALSE, show_figures = if(individual) 1 else NULL, transformation = NULL, transformation_arguments = NULL, transformation_settings = FALSE, - rescale_x = FALSE, scale_y2 = NULL, prior_list_mu = NULL, ...){ - - # TODO: add plots for individual parameters for weightfunction and PET-PEESE - individual = FALSE - show_parameter = if(individual) 1 else NULL + rescale_x = FALSE, scale_y2 = NULL, prior_list_mu = NULL, effect_direction = "positive", ...){ # check input (most arguments are checked within density) check_list(prior_list, "prior_list") @@ -826,8 +892,9 @@ geom_prior_list <- function(prior_list, xlim = NULL, x_seq = NULL, x_range_quan stop("'prior_list' must be a list of priors.") check_bool(individual, "individual") check_bool(rescale_x, "rescale_x") - check_int(show_parameter, "show_parameter", allow_NULL = TRUE) + check_int(show_figures, "show_figures", allow_NULL = TRUE) check_real(scale_y2, "scale_y2", lower = 0, allow_NULL = TRUE) + check_char(effect_direction, "effect_direction", allow_values = c("positive", "negative")) # get the plotting type @@ -873,7 +940,8 @@ geom_prior_list <- function(prior_list, xlim = NULL, x_seq = NULL, x_range_quan plot_data <- .plot_data_prior_list.PETPEESE(prior_list, x_seq = x_seq, x_range = xlim, x_range_quant = x_range_quant, n_points = n_points, n_samples = n_samples, transformation = transformation, transformation_arguments = transformation_arguments, - transformation_settings = transformation_settings, prior_list_mu = prior_list_mu) + transformation_settings = transformation_settings, prior_list_mu = prior_list_mu, + effect_direction = effect_direction) geom <- .geom_prior.PETPEESE(prior_list, plot_data = plot_data, ...) }else if(prior_type == "simple"){ @@ -904,17 +972,29 @@ geom_prior_list <- function(prior_list, xlim = NULL, x_seq = NULL, x_range_quan #' @title Plot samples from the mixed posterior distributions #' #' @param samples samples from a posterior distribution for a -#' parameter generated by [mix_posteriors]. +#' parameter generated by [mix_posteriors] or [as_mixed_posteriors]. #' @param parameter parameter name to be plotted. Use \code{"PETPEESE"} #' for PET-PEESE plot with parameters \code{"PET"} and \code{"PEESE"}, #' and \code{"weightfunction"} for plotting a weightfunction with #' parameters \code{"omega"}. -#' @param prior whether prior distribution should be added to the figure +#' @param prior whether prior distribution should be added to the figure. +#' When samples were prepared with \code{as_mixed_posteriors(..., transform_scaled = TRUE)}, +#' the transformed prior samples are automatically used. +#' @param effect_direction direction of the effect for PET-PEESE +#' regression. Use \code{"positive"} (default) for +#' \code{mu + PET*se + PEESE*se^2} or \code{"negative"} for +#' \code{mu - PET*se - PEESE*se^2}. #' @param dots_prior additional arguments for the prior distribution plot #' @param ... additional arguments #' @inheritParams density.prior #' @inheritParams plot.prior #' +#' @details +#' When using scaled predictors (via \code{formula_scale_list} in [JAGS_fit]), +#' you can plot posteriors on the original (unscaled) scale by preparing samples with +#' \code{as_mixed_posteriors(..., transform_scaled = TRUE)}. The function automatically +#' detects this and uses the pre-computed transformed prior samples when \code{prior = TRUE}. +#' #' @return \code{plot_posterior} returns either \code{NULL} or #' an object of class 'ggplot' if plot_type is \code{plot_type = "ggplot"}. #' @@ -922,12 +1002,10 @@ geom_prior_list <- function(prior_list, xlim = NULL, x_seq = NULL, x_range_quan #' @export plot_posterior <- function(samples, parameter, plot_type = "base", prior = FALSE, n_points = 1000, n_samples = 10000, force_samples = FALSE, + individual = FALSE, show_figures = NULL, transformation = NULL, transformation_arguments = NULL, transformation_settings = FALSE, - rescale_x = FALSE, par_name = NULL, dots_prior = list(), ...){ - - # TODO: add plots for individual parameters for weightfunction and PET-PEESE - individual = FALSE - show_figures = if(individual) 1 else NULL + rescale_x = FALSE, par_name = NULL, effect_direction = "positive", + dots_prior = list(), ...){ # check input check_list(samples, "prior_list") @@ -937,113 +1015,267 @@ plot_posterior <- function(samples, parameter, plot_type = "base", prior = FALSE check_char(plot_type, "plot_type", allow_values = c("base", "ggplot")) check_bool(individual, "individual") check_bool(rescale_x, "rescale_x") - check_int(show_figures, "show_figures", allow_NULL = TRUE) + check_int(show_figures, "show_figures", allow_NULL = TRUE, lower = 0) + check_char(effect_direction, "effect_direction", allow_values = c("positive", "negative")) .check_transformation_input(transformation, transformation_arguments, transformation_settings) # deal with bad parameter names for PET-PEESE, weightfunction if(tolower(gsub("-", "", gsub("_", "", gsub(".", "", parameter, fixed = TRUE),fixed = TRUE), fixed = TRUE)) %in% c("weightfunction", "weigthfunction", "omega")){ parameter <- "omega" - }else if(tolower(gsub("-", "", gsub("_", "", gsub(".", "", parameter, fixed = TRUE),fixed = TRUE), fixed = TRUE)) == "petpeese"){ + }else if(tolower(gsub("-", "", gsub("_", "", gsub(".", "", parameter, fixed = TRUE),fixed = TRUE), fixed = TRUE)) %in% "petpeese"){ parameter <- "PETPEESE" + }else if(tolower(gsub("-", "", gsub("_", "", gsub(".", "", parameter, fixed = TRUE),fixed = TRUE), fixed = TRUE)) %in% "pet"){ + parameter <- "PET" + }else if(tolower(gsub("-", "", gsub("_", "", gsub(".", "", parameter, fixed = TRUE),fixed = TRUE), fixed = TRUE)) %in% "peese"){ + parameter <- "PEESE" + } + + # auto-detect transform_scaled from samples attribute + transform_scaled <- isTRUE(attr(samples, "transform_scaled")) + + # handle transform_scaled: check for pre-computed prior samples + prior_samples_transformed <- NULL + if(transform_scaled && prior){ + prior_samples_transformed <- attr(samples, "prior_samples") + if(is.null(prior_samples_transformed)){ + stop("Samples were prepared with 'transform_scaled = TRUE' but no prior samples found. ", + "This should not happen - please report this as a bug.") + } } # get the plotting range dots <- list(...) xlim <- dots[["xlim"]] if(is.null(xlim)){ - if(parameter %in% c("omega", "PETPEESE") & !individual){ - xlim <- c(0, 1) + if(parameter %in% c("PET", "PEESE", "PETPEESE") & !individual){ + xlim <- c(0, 1) + }else if(parameter == "omega"){ + xlim <- c(0, 1) }else{ # use the data range otherwise - xlim <- NULL + xlim <- NULL } } - if(parameter == "omega"){ - - plot_data <- .plot_data_samples.weightfunction(samples, x_seq = NULL, x_range = xlim, x_range_quant = NULL, n_points = n_points) + if(is.element(parameter, "omega")){ - # add priors, if requested - if(prior){ + if (individual) { - # extract the correct weightfunction samples - if(!is.null(samples[[parameter]])){ - prior_list <- attr(samples[[parameter]], "prior_list") - }else if(!is.null(samples[["bias"]])){ - prior_list <- attr(samples[["bias"]], "prior_list") - }else{ - stop("No 'omega' or 'bias' samples found.") + # bias plot parameters require special extraction + if (!is.null(samples[["bias"]]) && inherits(samples[["bias"]], "mixed_posteriors.bias")) { + samples <- .simplify_as_mixed_posterior_bias(samples, parameter) } + prior_list <- attr(samples[[parameter]], "prior_list") + prior_list <- .simplify_prior_list(prior_list) - prior_list <- .simplify_prior_list(prior_list) - plot_data_prior <- .plot_data_prior_list.weightfunction(prior_list, x_seq = NULL, x_range = xlim, x_range_quant = NULL, - n_points = n_points, n_samples = n_samples) - # transplant common xlim and ylim - plot_data_joined <- list(plot_data_prior, plot_data) + # plot the individual weight parameters + out_list <- list() + par_names <- colnames(samples[["omega"]]) - xlim <- range(as.vector(sapply(plot_data_joined, attr, which = "x_range"))) - ylim <- range(as.vector(sapply(plot_data_joined, attr, which = "y_range"))) - attr(plot_data_prior, "x_range") <- xlim - attr(plot_data_prior, "y_range") <- ylim - dots_prior <- .transfer_dots(dots_prior, ...) + if (!is.null(show_figures)) { + if (show_figures > length(par_names)) { + stop("'show_figures' corresponds to a number larger than the number of weight function parameters.") + } else { + par_names <- par_names[show_figures] + } + } - args <- dots_prior - args$x <- prior_list - args$plot_data <- plot_data_prior - args$rescale_x <- rescale_x - args$plot_type <- plot_type - args$par_name <- par_name - plot <- do.call(.plot.prior.weightfunction, args) + for (par in par_names) { + + plot_data <- .plot_data_samples.weightparameter(samples, parameter = par, n_points = n_points) + + # add priors, if requested + if(prior){ + + plot_data_prior <- .plot_data_prior_list.weightparameter(prior_list, parameter = par, n_points = n_points, n_samples = n_samples) + + # transplant common xlim and ylim + plot_data_joined <- c(plot_data_prior, plot_data) + + xlim <- range(as.vector(sapply(plot_data_joined, attr, which = "x_range"))) + attr(plot_data_prior[[1]], "x_range") <- xlim + + if(any(sapply(plot_data_prior, inherits, what = "density.prior.simple")) & any(sapply(plot_data_prior, inherits, what = "density.prior.point"))){ + ylim <- range(as.vector(sapply(plot_data_joined[sapply(plot_data_joined, inherits, what = "density.prior.simple")], attr, which = "y_range"))) + ylim2 <- range(as.vector(sapply(plot_data_joined[sapply(plot_data_joined, inherits, what = "density.prior.point")], attr, which = "y_range"))) + attr(plot_data_prior[[which.max(sapply(plot_data_prior, inherits, what = "density.prior.simple"))]], "y_range") <- ylim + attr(plot_data_prior[[which.max(sapply(plot_data_prior, inherits, what = "density.prior.point"))]], "y_range") <- ylim2 + }else if(any(sapply(plot_data_prior, inherits, what = "density.prior.simple"))){ + ylim <- range(as.vector(sapply(plot_data_joined[sapply(plot_data_joined, inherits, what = "density.prior.simple")], attr, which = "y_range"))) + attr(plot_data_prior[[which.max(sapply(plot_data_prior, inherits, what = "density.prior.simple"))]], "y_range") <- ylim + }else if(any(sapply(plot_data_prior, inherits, what = "density.prior.point"))){ + ylim <- range(as.vector(sapply(plot_data_joined[sapply(plot_data_joined, inherits, what = "density.prior.point")], attr, which = "y_range"))) + attr(plot_data_prior[[which.max(sapply(plot_data_prior, inherits, what = "density.prior.point"))]], "y_range") <- ylim + } + + scale_y2 <- .get_scale_y2(plot_data_prior, ...) + dots_prior <- .transfer_dots(dots_prior, ...) + + + # set the y/x ranges + for(i in seq_along(plot_data)){ + if(inherits(plot_data[[i]], what = "density.prior.point")){ + attr(plot_data[[i]], which = "y_range") <- if(any(sapply(plot_data_prior, inherits, what = "density.prior.simple")) & any(sapply(plot_data_prior, inherits, what = "density.prior.point"))) ylim2 else ylim + }else{ + attr(plot_data[[i]], which = "y_range") <- ylim + attr(plot_data[[i]], which = "x_range") <- xlim + } + } + + # plot prior + args_prior <- dots_prior + args_prior$plot_data <- plot_data_prior + args_prior$plot_type <- plot_type + args_prior$par_name <- par + args_prior$scale_y2 <- scale_y2 + + plot <- do.call(.plot_prior_list.both, args_prior) + + + # plot posterior + args <- list(...) + args$plot_data <- plot_data + args$plot_type <- plot_type + args$par_name <- par + args$scale_y2 <- scale_y2 + args$add <- TRUE + + if(plot_type == "base"){ + plot <- do.call(.plot_prior_list.both, args) + }else if(plot_type == "ggplot"){ + plot <- plot + do.call(.plot_prior_list.both, args) + out_list[[par]] <- plot + } - if(plot_type == "ggplot"){ - plot <- plot + .geom_prior.weightfunction(plot_data, rescale_x = rescale_x, ...) - }else{ - .lines.prior.weightfunction(plot_data, rescale_x = rescale_x, ...) + }else{ + + # plot just posterior otherwise + plot <- .plot_prior_list.both(plot_data = plot_data, plot_type = plot_type, par_name = par, ...) + out_list[[par]] <- plot + + } } - }else{ + plot <- out_list - # plot just posterior otherwise - plot <- .plot.prior.weightfunction(NULL, plot_data = plot_data, plot_type = plot_type, rescale_x = rescale_x, par_name = par_name, ...) + + + } else { + + # special dispatching for visualizing the whole weightfunction + + plot_data <- .plot_data_samples.weightfunction(samples, x_seq = NULL, x_range = xlim, x_range_quant = NULL, n_points = n_points) + + # add priors, if requested + if(prior){ + + # extract the correct weightfunction samples + if(!is.null(samples[[parameter]])){ + prior_list <- attr(samples[[parameter]], "prior_list") + }else if(!is.null(samples[["bias"]])){ + prior_list <- attr(samples[["bias"]], "prior_list") + }else{ + stop("No 'omega' or 'bias' samples found.") + } + + prior_list <- .simplify_prior_list(prior_list) + plot_data_prior <- .plot_data_prior_list.weightfunction(prior_list, x_seq = NULL, x_range = xlim, x_range_quant = NULL, + n_points = n_points, n_samples = n_samples) + + # transplant common xlim and ylim + plot_data_joined <- list(plot_data_prior, plot_data) + + xlim <- range(as.vector(sapply(plot_data_joined, attr, which = "x_range"))) + ylim <- range(as.vector(sapply(plot_data_joined, attr, which = "y_range"))) + attr(plot_data_prior, "x_range") <- xlim + attr(plot_data_prior, "y_range") <- ylim + dots_prior <- .transfer_dots(dots_prior, ...) + + args <- dots_prior + args$x <- prior_list + args$plot_data <- plot_data_prior + args$rescale_x <- rescale_x + args$plot_type <- plot_type + args$par_name <- par_name + plot <- do.call(.plot.prior.weightfunction, args) + + if(plot_type == "ggplot"){ + plot <- plot + .geom_prior.weightfunction(plot_data, rescale_x = rescale_x, ...) + }else{ + .lines.prior.weightfunction(plot_data, rescale_x = rescale_x, ...) + } + + }else{ + + # plot just posterior otherwise + plot <- .plot.prior.weightfunction(NULL, plot_data = plot_data, plot_type = plot_type, rescale_x = rescale_x, par_name = par_name, ...) + + } } - }else if(parameter == "PETPEESE"){ + }else if(is.element(parameter, c("PET", "PEESE", "PETPEESE")) && !individual){ + # special dispatching for visualizing the PET-PEESE regression plot_data <- .plot_data_samples.PETPEESE(samples, x_seq = NULL, x_range = xlim, x_range_quant = NULL, n_points = n_points, - transformation = transformation, transformation_arguments = transformation_arguments, transformation_settings = transformation_settings) + transformation = transformation, transformation_arguments = transformation_arguments, transformation_settings = transformation_settings, + effect_direction = effect_direction) # add priors, if requested if(prior){ - if(is.null(samples[["mu"]])) - stop("'mu' samples are required for plotting PET-PEESE.") - prior_list_mu <- attr(samples[["mu"]], "prior_list") - - # TODO: a bit of a hack - removing priors that were added as a fill for sampling - if(!is.null(samples[["PET"]]) & !is.null(samples[["PEESE"]])){ - prior_list_PET <- attr(samples[["PET"]], "prior_list") - prior_list_PEESE <- attr(samples[["PEESE"]], "prior_list") - prior_fill <- seq_along(prior_list_PET)[!sapply(prior_list_PET, is.prior.PET) & !sapply(prior_list_PEESE, is.prior.PEESE)] - prior_list <- c(prior_list_PET[sapply(prior_list_PET, is.prior.PET)], prior_list_PEESE[sapply(prior_list_PEESE, is.prior.PEESE)], - prior_list_PET[prior_fill]) - prior_list_mu <- prior_list_mu[c(c(1:length(prior_list_mu))[sapply(prior_list_PET, is.prior.PET)], c(1:length(prior_list_mu))[sapply(prior_list_PEESE, is.prior.PEESE)], c(1:length(prior_list_mu))[prior_fill])] - }else if(is.null(samples[["PET"]]) & !is.null(samples[["PEESE"]])){ - prior_list <- attr(samples[["PEESE"]], "prior_list") - }else if(!is.null(samples[["PET"]]) & is.null(samples[["PEESE"]])){ - prior_list <- attr(samples[["PET"]], "prior_list") - }else{ - stop("Either PET or PEESE samples need to be provided.") + if(is.null(samples[["mu"]]) && is.null(samples[["mu_intercept"]])) + stop("'mu' or 'mu_intercept' samples are required for plotting PET-PEESE.") + + if(!is.null(samples[["mu"]])){ + prior_list_mu <- attr(samples[["mu"]], "prior_list") + }else if(!is.null(samples[["mu_intercept"]])){ + prior_list_mu <- attr(samples[["mu_intercept"]], "prior_list") + } + if(is.prior.simple(prior_list_mu)){ + prior_list_mu <- list(prior_list_mu) } - # cannot simplify prior_list - it would break the dependency with mu + if (is.null(samples[["bias"]])){ + # TODO: a bit of a hack - removing priors that were added as a fill for sampling + if(!is.null(samples[["PET"]]) & !is.null(samples[["PEESE"]])){ + prior_list_PET <- attr(samples[["PET"]], "prior_list") + prior_list_PEESE <- attr(samples[["PEESE"]], "prior_list") + prior_fill <- seq_along(prior_list_PET)[!sapply(prior_list_PET, is.prior.PET) & !sapply(prior_list_PEESE, is.prior.PEESE)] + prior_list <- c(prior_list_PET[sapply(prior_list_PET, is.prior.PET)], prior_list_PEESE[sapply(prior_list_PEESE, is.prior.PEESE)], + prior_list_PET[prior_fill]) + prior_list_mu <- prior_list_mu[c(c(1:length(prior_list_mu))[sapply(prior_list_PET, is.prior.PET)], c(1:length(prior_list_mu))[sapply(prior_list_PEESE, is.prior.PEESE)], c(1:length(prior_list_mu))[prior_fill])] + }else if(is.null(samples[["PET"]]) & !is.null(samples[["PEESE"]])){ + prior_list <- attr(samples[["PEESE"]], "prior_list") + }else if(!is.null(samples[["PET"]]) & is.null(samples[["PEESE"]])){ + prior_list <- attr(samples[["PET"]], "prior_list") + }else{ + stop("Either PET or PEESE samples need to be provided.") + } + if(is.prior.simple(prior_list)){ + prior_list <- list(prior_list) + } + } else { + prior_list <- attr(samples[["bias"]], "prior_list") + prior_list <- prior_list[sapply(prior_list, \(x) is.prior.PET(x) || is.prior.PEESE(x) || is.prior.none(x) || is.prior.point(x))] + # make cross product of the mixture priors + priors_grid <- expand.grid( + "mu" = prior_list_mu, + "PP" = prior_list + ) + prior_list_mu <- priors_grid[["mu"]] + prior_list <- priors_grid[["PP"]] + } + + # cannot simplify prior_list - it would break the dependency with mu plot_data_prior <- .plot_data_prior_list.PETPEESE(prior_list, x_seq = NULL, x_range = xlim, x_range_quant = NULL, n_points = n_points, n_samples = n_samples, transformation = transformation, transformation_arguments = transformation_arguments, - transformation_settings = transformation_settings, prior_list_mu = prior_list_mu) + transformation_settings = transformation_settings, prior_list_mu = prior_list_mu, + effect_direction = effect_direction) # transplant common xlim and ylim plot_data_joined <- list(plot_data_prior, plot_data) @@ -1080,10 +1312,16 @@ plot_posterior <- function(samples, parameter, plot_type = "base", prior = FALSE }else{ + # regular prior distributions (or individual plots for parameters PET-PEESE) + # bias plot parameters require special extraction + if (is.element(parameter, c("PET", "PEESE", "PETPEESE")) && !is.null(samples[["bias"]]) && inherits(samples[["bias"]], "mixed_posteriors.bias")) { + samples <- .simplify_as_mixed_posterior_bias(samples, parameter) + } prior_list <- attr(samples[[parameter]], "prior_list") prior_list <- .simplify_prior_list(prior_list) + if(any(sapply(prior_list, is.prior.factor))){ plot_data <- .plot_data_samples.factor(samples, parameter = parameter, n_points = n_points, transformation = transformation, transformation_arguments = transformation_arguments, transformation_settings = transformation_settings) @@ -1097,10 +1335,24 @@ plot_posterior <- function(samples, parameter, plot_type = "base", prior = FALSE # add priors, if requested if(prior){ - plot_data_prior <- .plot_data_prior_list.simple(prior_list, x_seq = NULL, x_range = xlim, x_range_quant = NULL, - n_points = n_points, n_samples = n_samples, force_samples = force_samples, individual = individual, - transformation = transformation, transformation_arguments = transformation_arguments, - transformation_settings = transformation_settings) + # use transformed prior samples if available (from transform_scaled) + if(transform_scaled && !is.null(prior_samples_transformed) && parameter %in% colnames(prior_samples_transformed)){ + # Create plot data from transformed prior samples + plot_data_prior <- .plot_data_prior_samples_transformed( + prior_samples_transformed[, parameter], + prior_list = prior_list, + n_points = n_points, + x_range = xlim, + transformation = transformation, + transformation_arguments = transformation_arguments, + transformation_settings = transformation_settings + ) + }else{ + plot_data_prior <- .plot_data_prior_list.simple(prior_list, x_seq = NULL, x_range = xlim, x_range_quant = NULL, + n_points = n_points, n_samples = n_samples, force_samples = force_samples, individual = individual, + transformation = transformation, transformation_arguments = transformation_arguments, + transformation_settings = transformation_settings) + } # transplant common xlim and ylim plot_data_joined <- c(plot_data_prior, plot_data) @@ -1190,6 +1442,51 @@ plot_posterior <- function(samples, parameter, plot_type = "base", prior = FALSE } } +# Helper function to create plot data from transformed prior samples +# This is used when transform_scaled = TRUE to visualize priors on the original scale +.plot_data_prior_samples_transformed <- function(prior_samples, prior_list, n_points, x_range = NULL, + transformation = NULL, transformation_arguments = NULL, + transformation_settings = FALSE){ + + x_points <- NULL + y_points <- NULL + x_den <- NULL + y_den <- NULL + + # Handle the samples as a simple density + if(length(prior_samples) > 0){ + args <- list(x = prior_samples, n = n_points) + + # Set range if provided + if(!is.null(x_range) && length(x_range) == 2){ + args$from <- x_range[1] + args$to <- x_range[2] + } + + # Get the density estimate + density_estimate <- do.call(stats::density, args) + x_den <- density_estimate$x + y_den <- density_estimate$y + + # Apply transformation if specified (for additional user transformations) + if(!is.null(transformation)){ + x_den <- .density.prior_transformation_x(x_den, transformation, transformation_arguments) + y_den <- .density.prior_transformation_y(x_den, y_den, transformation, transformation_arguments) + } + } + + # Create output object matching density.prior.simple structure + out <- list( + x = x_den, + y = y_den + ) + attr(out, "x_range") <- range(x_den, na.rm = TRUE) + attr(out, "y_range") <- range(y_den, na.rm = TRUE) + class(out) <- c("density.prior.simple", "density.prior") + + return(list(out)) +} + .plot_data_samples.simple <- function(samples, parameter, n_points, transformation, transformation_arguments, transformation_settings){ check_list(samples, "samples", check_names = parameter, allow_other = TRUE) @@ -1325,24 +1622,41 @@ plot_posterior <- function(samples, parameter, plot_type = "base", prior = FALSE return(out) } -.plot_data_samples.PETPEESE <- function(samples, x_seq, x_range, x_range_quant, n_points, transformation, transformation_arguments, transformation_settings){ +.plot_data_samples.PETPEESE <- function(samples, x_seq, x_range, x_range_quant, n_points, transformation, transformation_arguments, transformation_settings, effect_direction = "positive"){ check_list(samples, "samples") + if (is.null(samples[["mu"]]) && is.null(samples[["mu_intercept"]])) + stop("'mu' or 'mu_intercept' samples need to be present.") - if(is.null(samples[["PET"]]) & is.null(samples[["PEESE"]])) - stop("At least one 'PET' or 'PEESE' model needs to be specified.") - if(is.null(samples[["mu"]])) - stop("'mu' samples need to be present.") + if (!is.null(samples[["bias"]])) { - # get the samples - if(!is.null(samples[["PET"]]) & !is.null(samples[["PEESE"]])){ - if(!all(attr(samples[["PET"]], "models_ind") == attr(samples[["PEESE"]], "models_ind"))) - stop("non-matching dimensions") - samples <- cbind(samples[["mu"]], samples[["PET"]], samples[["PEESE"]]) - }else if(is.null(samples[["PET"]])){ - samples <- cbind(samples[["mu"]], rep(0, length(samples[["PEESE"]])), samples[["PEESE"]]) - }else if(is.null(samples[["PEESE"]])){ - samples <- cbind(samples[["mu"]], samples[["PET"]], rep(0, length(samples[["PET"]]))) + if(length(c("PET", "PEESE") %in% samples[["bias"]]) == 0) + stop("At least one 'PET' or 'PEESE' model needs to be specified.") + + # create mu-PET-PEESE samples matrix + new_samples <- matrix(if(!is.null(samples[["mu"]])) samples[["mu"]] else samples[["mu_intercept"]], ncol = 1) + for (par in c("PET", "PEESE")) { + if (is.element(par, colnames(samples[["bias"]]))) { + new_samples <- cbind(new_samples, samples[["bias"]][,par]) + } else { + new_samples <- cbind(new_samples, 0) + } + } + + } else { + + if(is.null(samples[["PET"]]) & is.null(samples[["PEESE"]])) + stop("At least one 'PET' or 'PEESE' model needs to be specified.") + + # create mu-PET-PEESE samples matrix + new_samples <- matrix(if(!is.null(samples[["mu"]])) samples[["mu"]] else samples[["mu_intercept"]], ncol = 1) + for (par in c("PET", "PEESE")) { + if (!is.null(samples[[par]])) { + new_samples <- cbind(new_samples, samples[[par]]) + } else { + new_samples <- cbind(new_samples, 0) + } + } } # get the plotting range @@ -1354,10 +1668,12 @@ plot_posterior <- function(samples, parameter, plot_type = "base", prior = FALSE } - # compute PET-PEESE (mu + PET*se + PEESE*se^2) - x_sam <- matrix(samples[,1], nrow = length(samples), ncol = length(x_seq)) + - matrix(samples[,2], nrow = length(samples), ncol = length(x_seq)) * matrix(x_seq, nrow = length(samples), ncol = length(x_seq), byrow = TRUE) + - matrix(samples[,3], nrow = length(samples), ncol = length(x_seq)) * matrix(x_seq^2, nrow = length(samples), ncol = length(x_seq), byrow = TRUE) + # compute PET-PEESE (mu +/- PET*se +/- PEESE*se^2) + # effect_direction controls the sign: "positive" uses +, "negative" uses - + direction_sign <- if(effect_direction == "negative") -1 else 1 + x_sam <- matrix(new_samples[,1], nrow = length(new_samples), ncol = length(x_seq)) + + direction_sign * matrix(new_samples[,2], nrow = length(new_samples), ncol = length(x_seq)) * matrix(x_seq, nrow = length(new_samples), ncol = length(x_seq), byrow = TRUE) + + direction_sign * matrix(new_samples[,3], nrow = length(new_samples), ncol = length(x_seq)) * matrix(x_seq^2, nrow = length(new_samples), ncol = length(x_seq), byrow = TRUE) # transform the parameter if requested if(!is.null(transformation)){ @@ -1441,6 +1757,139 @@ plot_posterior <- function(samples, parameter, plot_type = "base", prior = FALSE return(out) } +.plot_data_samples.weightparameter<- function(samples, parameter, n_points){ + + check_list(samples, "samples", check_names = "omega", allow_other = TRUE) + if(!is.null(samples[["omega"]])){ + samples <- samples[["omega"]] + }else if(!is.null(samples[["bias"]])){ + samples <- samples[["bias"]] + }else{ + stop("No 'omega' or 'bias' samples found.") + } + + x_points <- NULL + y_points <- NULL + x_den <- NULL + y_den <- NULL + + # extract the relevant data + prior_list <- attr(samples, "prior_list") + models_ind <- attr(samples, "models_ind") + samples <- samples[,parameter] + if (!(is.prior.mixture(prior_list) || is.prior.spike_and_slab(prior_list)) && is.prior(prior_list)) + prior_list <- list(prior_list) + + # replace prior_none with spike(1) + for (i in seq_along(prior_list)) { + if (is.prior.none(prior_list[[i]])) { + temp_weight <- prior_list[[i]][["prior_weights"]] + prior_list[[i]] <- prior("spike", parameters = list(location = 1)) + prior_list[[i]][["prior_weights"]] <- temp_weight + } + } + + # deal with spikes + samples_is_1 <- abs(samples - 1) < 1e-6 + + if(any(samples_is_1)){ + x_points <- 1 + y_points <- mean(samples_is_1) + + # remove the used samples so they are not re-used in density + # (since they might be forced to one even in non-null models due to cummulativness) + models_ind <- models_ind[!samples_is_1] + samples <- samples[!samples_is_1] + + }else{ + x_points <- NULL + y_points <- NULL + } + + # deal with the densities + if (!all(sapply(prior_list, \(x) is.prior.point(x) || is.prior.none(x)))) { + + samples_density <- samples[models_ind %in% which(!sapply(prior_list, is.prior.point))] + + if(length(samples_density) > 0){ + + args <- list(x = samples_density, n = n_points) + + # set the endpoints for possible truncation + prior_list_simple <- prior_list[!sapply(prior_list, is.prior.point)] + prior_list_simple_lower <- 0 + prior_list_simple_upper <- 1 + + if(!is.infinite(prior_list_simple_lower)){ + args <- c(args, from = prior_list_simple_lower) + } + if(!is.infinite(prior_list_simple_upper)){ + args <- c(args, to = prior_list_simple_upper) + } + + # get the density estimate + density_continuous <- do.call(stats::density, args) + x_den <- density_continuous$x + y_den <- density_continuous$y * (length(samples_density) / length(samples)) + + # check for truncation + if(isTRUE(all.equal(prior_list_simple_lower, x_den[1])) | prior_list_simple_lower >= x_den[1]){ + y_den <- c(0, y_den) + x_den <- c(x_den[1], x_den) + } + if(isTRUE(all.equal(prior_list_simple_upper, x_den[length(x_den)])) | prior_list_simple_upper <= x_den[length(x_den)]){ + y_den <- c(y_den, 0) + x_den <- c(x_den, x_den[length(x_den)]) + } + } + } + + + # create the output object + out <- list() + + # add continuous densities + if(!is.null(y_den)){ + out_den <- list( + call = call("density", "mixed samples"), + bw = NULL, + n = n_points, + x = x_den, + y = y_den, + samples = samples_density + ) + + class(out_den) <- c("density", "density.prior", "density.prior.simple") + attr(out_den, "x_range") <- range(x_den) + attr(out_den, "y_range") <- c(0, max(y_den)) + attr(out_den, "parameter") <- parameter + + out[["density"]] <- out_den + } + + # add spikes + if(!is.null(y_points)){ + for(i in seq_along(y_points)){ + temp_points <- list( + call = call("density", paste0("point", i)), + bw = NULL, + n = n_points, + x = x_points[i], + y = y_points[i], + samples = NULL + ) + + class(temp_points) <- c("density", "density.prior", "density.prior.point") + attr(temp_points, "x_range") <- c(0, 1) + attr(temp_points, "y_range") <- c(0, max(y_points[i])) + attr(temp_points, "parameter") <- parameter + + out[[paste0("points",i)]] <- temp_points + } + } + + return(out) +} .plot_data_samples.factor <- function(samples, parameter, n_points, transformation, transformation_arguments, transformation_settings){ check_list(samples, "samples", check_names = parameter, allow_other = TRUE) @@ -1707,7 +2156,7 @@ plot_models <- function(model_list, samples, inference, parameter, plot_type = " }), y_lCI = sapply(1:length(models_summary), function(i){ if(any(attr(models_summary[[i]], "parameters") == parameter)){ - return(models_summary[[i]][attr(models_summary[[i]], "parameters") == parameter, "lCI"]) + return(models_summary[[i]][attr(models_summary[[i]], "parameters") == parameter, "0.025"]) }else if(is.prior.point(prior_list[[i]])){ return(prior_list[[i]]$parameters[["location"]]) }else{ @@ -1716,7 +2165,7 @@ plot_models <- function(model_list, samples, inference, parameter, plot_type = " }), y_uCI = sapply(1:length(models_summary), function(i){ if(any(attr(models_summary[[i]], "parameters") == parameter)){ - return(models_summary[[i]][attr(models_summary[[i]], "parameters") == parameter, "uCI"]) + return(models_summary[[i]][attr(models_summary[[i]], "parameters") == parameter, "0.975"]) }else if(is.prior.point(prior_list[[i]])){ return(prior_list[[i]]$parameters[["location"]]) }else{ @@ -1956,7 +2405,7 @@ plot_models <- function(model_list, samples, inference, parameter, plot_type = " return(plot) } -.simplify_spike_samples <- function(samples, prior_list){ +.simplify_spike_samples <- function(samples, prior_list){ # Check if we're dealing with spike_and_slab or mixture (which are single priors) vs list of priors is_spike_and_slab <- is.prior.spike_and_slab(prior_list) @@ -2016,7 +2465,50 @@ plot_models <- function(model_list, samples, inference, parameter, plot_type = " return(spike_probability) } +.simplify_as_mixed_posterior_bias <- function(samples, parameter) { + + ### replace all remaining priors by null prior + prior_list <- attr(samples[["bias"]], "prior_list") + if (parameter == "PET") { + prior_ind <- which(sapply(prior_list, \(x) !is.prior.PET(x))) + } else if (parameter == "PEESE") { + prior_ind <- which(sapply(prior_list, \(x) !is.prior.PEESE(x))) + } else if (parameter == "omega") { + prior_ind <- which(sapply(prior_list, \(x) !is.prior.weightfunction(x))) + } + if (length(prior_ind) > 0) { + for (i in prior_ind) { + temp_weight <- prior_list[[i]][["prior_weights"]] + prior_list[[i]] <- if (parameter == "omega") prior_none() else prior("point", parameters = list(0)) + prior_list[[i]][["prior_weights"]] <- temp_weight + } + } + ### create new samples + new_samples <- samples[["bias"]][, grepl(parameter, colnames(samples[["bias"]])),drop=FALSE] + + ### store attribute + std_attrs <- c("dim", "dimnames", "names", "prior_list", "mcpar") + all_attrs <- attributes(samples[["bias"]]) + to_restore <- setdiff(names(all_attrs), std_attrs) + + ### re-assign attributes + for (a in to_restore) { + attr(new_samples, a) <- all_attrs[[a]] + } + + # remove `mixed_posteriors.bias` class + class(new_samples) <- class(new_samples)[!class(new_samples) %in% "mixed_posteriors.bias"] + + ### assign prior list and model indicator + attr(new_samples, "prior_list") <- prior_list + + ### remove the old samples & store new samples + samples[["bias"]] <- NULL + samples[[parameter]] <- new_samples + + return(samples) +} #' @title Plot samples from the marginal posterior distributions #' diff --git a/R/model-averaging.R b/R/model-averaging.R index 31a073e6..756470d6 100644 --- a/R/model-averaging.R +++ b/R/model-averaging.R @@ -200,6 +200,12 @@ mix_posteriors <- function(model_list, parameters, is_null_list, conditional = F prior_weights <- sapply(model_list, function(m) m[["prior_weights"]]) inference <- ensemble_inference(model_list, parameters, is_null_list, conditional) + + # set seed only once at the beginning -- not in the individual draws as the priors will end up completely correlated + if(!is.null(seed)){ + set.seed(seed) + } + out <- list() for(p in seq_along(parameters)){ @@ -224,7 +230,7 @@ mix_posteriors <- function(model_list, parameters, is_null_list, conditional = F temp_priors[[i]][["prior_weights"]] <- temp_inference$prior_probs[i] } - out[[temp_parameter]] <- .mix_posteriors.weightfunction(fits, temp_priors, temp_parameter, temp_inference$post_probs, seed, n_samples) + out[[temp_parameter]] <- .mix_posteriors.weightfunction(fits, temp_priors, temp_parameter, temp_inference$post_probs, NULL, n_samples) }else if(any(sapply(temp_priors, is.prior.factor)) && all(sapply(temp_priors, is.prior.factor) | sapply(temp_priors, is.prior.point) | sapply(temp_priors, is.null))){ # factor priors @@ -241,7 +247,7 @@ mix_posteriors <- function(model_list, parameters, is_null_list, conditional = F temp_priors[[i]][["prior_weights"]] <- temp_inference$prior_probs[i] } - out[[temp_parameter]] <- .mix_posteriors.factor(fits, temp_priors, temp_parameter, temp_inference$post_probs, seed, n_samples) + out[[temp_parameter]] <- .mix_posteriors.factor(fits, temp_priors, temp_parameter, temp_inference$post_probs, NULL, n_samples) }else if(any(sapply(temp_priors, is.prior.vector)) && all(sapply(temp_priors, is.prior.vector) | sapply(temp_priors, is.prior.point) | sapply(temp_priors, is.null))){ # vector priors: @@ -275,7 +281,7 @@ mix_posteriors <- function(model_list, parameters, is_null_list, conditional = F temp_priors[[i]][["prior_weights"]] <- temp_inference$prior_probs[i] } - out[[temp_parameter]] <- .mix_posteriors.simple(fits, temp_priors, temp_parameter, temp_inference$post_probs, seed, n_samples) + out[[temp_parameter]] <- .mix_posteriors.simple(fits, temp_priors, temp_parameter, temp_inference$post_probs, NULL, n_samples) }else{ stop("The posterior samples cannot be mixed: unsupported mixture of prior distributions.") @@ -688,8 +694,14 @@ mix_posteriors <- function(model_list, parameters, is_null_list, conditional = F #' @param force_plots temporal argument allowing to generate conditional posterior samples #' suitable for prior and posterior plots. Only available when conditioning on a #' single parameter. +#' @param transform_scaled whether to transform samples from standardized (scaled) to +#' original (unscaled) scale. When \code{TRUE}, both posterior and prior samples are +#' transformed, and the result can be directly passed to [plot_posterior] which will +#' automatically detect the transformation and use the transformed prior samples. +#' Requires a model fitted with \code{formula_scale_list}. Defaults to \code{FALSE}. +#' @param n_prior_samples number of prior samples to generate when +#' \code{transform_scaled = TRUE}. Defaults to 10000. #' @inheritParams ensemble_inference -#' @inheritParams mix_posteriors #' #' @return \code{as_mix_posteriors} returns a named list of mixed posterior #' distributions (either a vector of matrix). @@ -698,7 +710,8 @@ mix_posteriors <- function(model_list, parameters, is_null_list, conditional = F #' #' @name as_mixed_posteriors #' @export -as_mixed_posteriors <- function(model, parameters, conditional = NULL, conditional_rule = "AND", force_plots = FALSE){ +as_mixed_posteriors <- function(model, parameters, conditional = NULL, conditional_rule = "AND", force_plots = FALSE, + transform_scaled = FALSE, n_prior_samples = 10000){ # check input if(!inherits(model, "BayesTools_fit")) @@ -706,6 +719,8 @@ as_mixed_posteriors <- function(model, parameters, conditional = NULL, condition check_char(parameters, "parameters", check_length = FALSE) check_char(conditional, "conditional", check_length = FALSE, allow_values = c(parameters, "PET", "PEESE", "PETPEESE", "omega"), allow_NULL = TRUE) check_char(conditional_rule, "conditional_rule", allow_values = c("AND", "OR")) + check_bool(transform_scaled, "transform_scaled") + check_int(n_prior_samples, "n_prior_samples", lower = 1) # extract the list of priors priors <- attr(model, "prior_list") @@ -724,23 +739,25 @@ as_mixed_posteriors <- function(model, parameters, conditional = NULL, condition # subset the posterior distribution conditioning_samples <- do.call(cbind, lapply(conditional, function(parameter){ - # special cases for PET / PEESE / PET-PEESE / weightfunctions - if(parameter == "PET" && !is.null(priors[["bias"]]) && is.prior.mixture(priors[["bias"]])){ - is_PET <- sapply(priors[["bias"]], is.prior.PET) - return(model_samples[, "bias_indicator"] %in% which(is_PET)) - } - if(parameter == "PEESE" && !is.null(priors[["bias"]]) && is.prior.mixture(priors[["bias"]])){ - is_PEESE <- sapply(priors[["bias"]], is.prior.PEESE) - return(model_samples[, "bias_indicator"] %in% which(is_PEESE)) - } - if(parameter == "PETPEESE" && !is.null(priors[["bias"]]) && is.prior.mixture(priors[["bias"]])){ - is_PET <- sapply(priors[["bias"]], is.prior.PET) - is_PEESE <- sapply(priors[["bias"]], is.prior.PEESE) - return(model_samples[, "bias_indicator"] %in% which(is_PET | is_PEESE)) - } - if(parameter == "omega" && !is.null(priors[["bias"]]) && is.prior.mixture(priors[["bias"]])){ - is_weightfunction <- sapply(priors[["bias"]], is.prior.weightfunction) - return(model_samples[, "bias_indicator"] %in% which(is_weightfunction)) + # special cases for PET / PEESE / PET-PEESE / weightfunctions within the bias parameter + if (!is.null(priors[["bias"]]) && is.prior.mixture(priors[["bias"]])) { + if(parameter == "PET"){ + is_PET <- sapply(priors[["bias"]], is.prior.PET) + return(model_samples[, "bias_indicator"] %in% which(is_PET)) + } + if(parameter == "PEESE"){ + is_PEESE <- sapply(priors[["bias"]], is.prior.PEESE) + return(model_samples[, "bias_indicator"] %in% which(is_PEESE)) + } + if(parameter == "PETPEESE"){ + is_PET <- sapply(priors[["bias"]], is.prior.PET) + is_PEESE <- sapply(priors[["bias"]], is.prior.PEESE) + return(model_samples[, "bias_indicator"] %in% which(is_PET | is_PEESE)) + } + if(parameter == "omega"){ + is_weightfunction <- sapply(priors[["bias"]], is.prior.weightfunction) + return(model_samples[, "bias_indicator"] %in% which(is_weightfunction)) + } } # normal cases @@ -776,54 +793,68 @@ as_mixed_posteriors <- function(model, parameters, conditional = NULL, condition model_samples <- model_samples[conditioning_samples,,drop=FALSE] # set prior weights to 0 for null distributions - # TODO: this needs to be implemented for enabling of the conditional mixture posterior distributions when more than one components is present - # (e.g., conditional marginal and posterior plots) - # the current workaround is suitable only for a single parameters (to produce averaged prior and posterior plots) - if(length(conditional) == 1 && length(parameters) == 1 && conditional == parameters && force_plots){ - - # special cases for PET / PEESE / PET-PEESE / weightfunctions - if(conditional == "PET" && !is.null(priors[["bias"]]) && is.prior.mixture(priors[["bias"]])){ - is_PET <- sapply(priors[["bias"]], is.prior.PET) - for(i in seq(along = is_PET)){ - if(!is_PET[i]){ - priors[[parameters]][[i]][["prior_weights"]] <- 0 + if(length(conditional) == 1){ + + if (conditional %in% c("bias", "PET", "PEESE", "PETPEESE", "omega") && !is.null(priors[["bias"]]) && is.prior.mixture(priors[["bias"]])) { + + # special cases for PET / PEESE / PET-PEESE / weightfunctions + if(conditional == "PET"){ + is_PET <- sapply(priors[["bias"]], is.prior.PET) + for(i in seq(along = is_PET)){ + if(!is_PET[i]){ + priors[["bias"]][[i]][["prior_weights"]] <- 0 + } } - } - }else if(conditional == "PEESE" && !is.null(priors[["bias"]]) && is.prior.mixture(priors[["bias"]])){ - for(i in seq(along = is_PEESE)){ - if(!is_PEESE[i]){ - priors[[parameters]][[i]][["prior_weights"]] <- 0 + }else if(conditional == "PEESE"){ + is_PEESE <- sapply(priors[["bias"]], is.prior.PEESE) + for(i in seq(along = is_PEESE)){ + if(!is_PEESE[i]){ + priors[["bias"]][[i]][["prior_weights"]] <- 0 + } } - } - }else if(conditional == "PETPEESE" && !is.null(priors[["bias"]]) && is.prior.mixture(priors[["bias"]])){ - is_PET <- sapply(priors[["bias"]], is.prior.PET) - is_PEESE <- sapply(priors[["bias"]], is.prior.PEESE) - for(i in seq(along = is_PET)){ - if(!(is_PET[i] || is_PEESE[i])){ - priors[[parameters]][[i]][["prior_weights"]] <- 0 + }else if(conditional == "PETPEESE"){ + is_PET <- sapply(priors[["bias"]], is.prior.PET) + is_PEESE <- sapply(priors[["bias"]], is.prior.PEESE) + for(i in seq(along = is_PET)){ + if(!(is_PET[i] || is_PEESE[i])){ + priors[["bias"]][[i]][["prior_weights"]] <- 0 + } } - } - }else if(conditional == "omega" && !is.null(priors[["bias"]]) && is.prior.mixture(priors[["bias"]])){ - is_weightfunction <- sapply(priors[["bias"]], is.prior.weightfunction) - for(i in seq(along = is_weightfunction)){ - if(!is_weightfunction[i]){ - priors[[parameters]][[i]][["prior_weights"]] <- 0 + }else if(conditional == "omega"){ + is_weightfunction <- sapply(priors[["bias"]], is.prior.weightfunction) + for(i in seq(along = is_weightfunction)){ + if(!is_weightfunction[i]){ + priors[["bias"]][[i]][["prior_weights"]] <- 0 + } } } - }else if(is.prior.mixture(priors[[parameters]])){ - components <- attr(priors[[parameters]], "components") + # propagate the prior weights to the mixture prior itself + attr(priors[["bias"]], "prior_weights") <- sapply(priors[["bias"]], \(x) x[["prior_weights"]]) + + }else if(is.prior.mixture(priors[[conditional]])){ - attr(priors[[parameters]], "prior_weights")[which(components == "null")] <- 0 + components <- attr(priors[[conditional]], "components") for(i in seq(along = components)){ if(components[i] == "null"){ - priors[[parameters]][[i]][["prior_weights"]] <- 0 + priors[[conditional]][[i]][["prior_weights"]] <- 0 } } + + # propagate the prior weights to the mixture prior itself + attr(priors[[conditional]], "prior_weights") <- sapply(priors[[conditional]], \(x) x[["prior_weights"]]) } + } } + # extract formula_scale early for transform_scaled support + formula_scale <- attr(model, "formula_scale") + + # apply scale transformation to posterior samples if requested + if(transform_scaled && !is.null(formula_scale) && length(formula_scale) > 0){ + model_samples <- transform_scale_samples(model_samples, formula_scale) + } out <- list() @@ -876,6 +907,19 @@ as_mixed_posteriors <- function(model, parameters, conditional = NULL, condition attr(out, "prior_list") <- priors attr(out, "conditional") <- conditional attr(out, "conditional_rule") <- conditional_rule + + # propagate formula_scale attribute for transform_scaled support + if(!is.null(formula_scale)){ + attr(out, "formula_scale") <- formula_scale + } + + # generate and store transformed prior samples if requested + if(transform_scaled && !is.null(formula_scale) && length(formula_scale) > 0){ + prior_samples <- transform_prior_samples(model, n_samples = n_prior_samples) + attr(out, "prior_samples") <- prior_samples + attr(out, "transform_scaled") <- TRUE + } + class(out) <- c(class(out), "as_mixed_posteriors", "mixed_posteriors") return(out) } @@ -1019,13 +1063,16 @@ as_mixed_posteriors <- function(model, parameters, conditional = NULL, condition # check input check_char(parameter, "parameter", check_length = FALSE) + # obtain mapping for the weight coefficients omega_mapping <- weightfunctions_mapping(list(prior)) omega_cuts <- weightfunctions_mapping(list(prior), cuts_only = TRUE) omega_names <- sapply(1:(length(omega_cuts)-1), function(i) paste0("omega[",omega_cuts[i],",",omega_cuts[i+1],"]")) + # need to reverse the order since JAGS stores omega in reverse order (from largest p-value to smallest) + omega_par <- rev(sapply(1:(length(omega_cuts)-1), function(i) paste0("omega[",i,"]"))) # prepare output objects - samples <- model_samples[, sapply(1:(length(omega_cuts)-1), function(i) paste0("omega[",i,"]"))] + samples <- model_samples[, omega_par, drop = FALSE] rownames(samples) <- NULL colnames(samples) <- omega_names @@ -1090,7 +1137,8 @@ as_mixed_posteriors <- function(model, parameters, conditional = NULL, condition out_names <- omega_names par_names <- omega_par }else if("PETPEESE" %in% conditional){ - out_names <- par_names <- c("PET", "PEESE") + # subset in case only PET/PEESE is supplied + out_names <- par_names <- colnames(model_samples)[colnames(model_samples) %in% c("PET", "PEESE")] }else if("PET" %in% conditional){ out_names <- par_names <- "PET" }else if("PEESE" %in% conditional){ diff --git a/R/posterior-extraction.R b/R/posterior-extraction.R new file mode 100644 index 00000000..e46e8e60 --- /dev/null +++ b/R/posterior-extraction.R @@ -0,0 +1,449 @@ +#' @title Helper functions for extracting and formatting posterior distributions +#' +#' @description Internal helper functions to extract posterior samples from JAGS +#' fits and reformat them for further processing (summary tables, diagnostics, plots). +#' These functions consolidate common logic that was duplicated across +#' \code{runjags_estimates_table}, \code{.diagnostics_plot_data}, and plotting functions. +#' +#' @name posterior_extraction_helpers +#' @keywords internal +NULL + + +#' @rdname posterior_extraction_helpers +#' @param fit a JAGS model fit object +#' @param as_list whether to return samples as mcmc.list (TRUE) or merged matrix (FALSE) +#' @return matrix or mcmc.list of posterior samples +.extract_posterior_samples <- function(fit, as_list = FALSE) { + + if (as_list) { + # Use generic function to allow S3 method dispatch (runjags has its own as.mcmc.list method) + model_samples <- coda::as.mcmc.list(fit) + } else { + # Use generic function to allow S3 method dispatch (runjags has its own as.mcmc method) + model_samples <- suppressWarnings(coda::as.mcmc(fit)) + } + + return(model_samples) +} + + +#' @rdname posterior_extraction_helpers +#' @param model_samples matrix of posterior samples +#' @param prior_list list of prior objects +#' @param remove_parameters character vector of parameter names to remove +#' @return list with cleaned model_samples and updated prior_list +.remove_auxiliary_parameters <- function(model_samples, prior_list, remove_parameters = NULL) { + + for (i in rev(seq_along(prior_list))) { + + par_name <- names(prior_list)[i] + + # invgamma support parameter + if (is.prior.simple(prior_list[[i]]) && prior_list[[i]][["distribution"]] == "invgamma") { + model_samples <- model_samples[, colnames(model_samples) != paste0("inv_", par_name), drop = FALSE] + } + + # weightfunction parameters + if (is.prior.weightfunction(prior_list[[i]])) { + # remove etas + if (prior_list[[i]][["distribution"]] %in% c("one.sided", "two.sided")) { + model_samples <- model_samples[, !grepl("eta", colnames(model_samples)), drop = FALSE] + } + + # rename the omegas + omega_cuts <- weightfunctions_mapping(prior_list[i], cuts_only = TRUE) + omega_names_old <- paste0("omega[", 1:(length(omega_cuts) - 1), "]") + omega_names <- sapply(1:(length(omega_cuts) - 1), function(j) paste0("omega[", omega_cuts[j], ",", omega_cuts[j + 1], "]")) + + # change the order of omegas + model_samples[, which(colnames(model_samples) %in% omega_names_old)] <- model_samples[, rev(which(colnames(model_samples) %in% omega_names_old)), drop = FALSE] + colnames(model_samples)[which(colnames(model_samples) %in% omega_names_old)] <- omega_names + + # remove omegas if requested + if ("omega" %in% remove_parameters) { + model_samples <- model_samples[, !colnames(model_samples) %in% omega_names, drop = FALSE] + prior_list[i] <- NULL + } + + } else if (par_name %in% remove_parameters) { + # remove parameters to be excluded (note: spike_0 removal is handled by caller) + model_samples <- .remove_parameter_columns(model_samples, prior_list[[i]], par_name) + prior_list[i] <- NULL + } + } + + return(list(model_samples = model_samples, prior_list = prior_list)) +} + + +#' @rdname posterior_extraction_helpers +#' @description Helper to remove all columns associated with a parameter +#' @param model_samples matrix of posterior samples +#' @param prior prior object for the parameter +#' @param par_name name of the parameter +#' @return updated model_samples matrix +.remove_parameter_columns <- function(model_samples, prior, par_name) { + + # collect all column patterns to remove + cols_to_remove <- character(0) + + if (is.prior.spike_and_slab(prior)) { + # spike and slab: remove main parameter, indicator, inclusion, variable + cols_to_remove <- c( + par_name, + paste0(par_name, "_indicator"), + paste0(par_name, "_inclusion"), + paste0(par_name, "_variable") + ) + # also handle factor spike and slab with indexed columns + cols_to_remove <- c(cols_to_remove, + colnames(model_samples)[grepl(paste0("^", par_name, "\\["), colnames(model_samples))], + colnames(model_samples)[grepl(paste0("^", par_name, "_variable\\["), colnames(model_samples))] + ) + + } else if (is.prior.mixture(prior)) { + # mixture: remove main parameter, indicator, and component-specific columns + cols_to_remove <- c( + par_name, + paste0(par_name, "_indicator") + ) + # handle factor mixture with indexed columns + cols_to_remove <- c(cols_to_remove, + colnames(model_samples)[grepl(paste0("^", par_name, "\\["), colnames(model_samples))] + ) + + # check for bias mixture (PET, PEESE, omega) + if (inherits(prior, "prior.bias_mixture")) { + # remove PET, PEESE, and all omega columns + cols_to_remove <- c(cols_to_remove, "PET", "PEESE") + cols_to_remove <- c(cols_to_remove, + colnames(model_samples)[grepl("^omega\\[", colnames(model_samples))] + ) + } + + } else if (is.prior.factor(prior)) { + # factor prior: remove all indexed columns + cols_to_remove <- .JAGS_prior_factor_names(par_name, prior) + + } else if (is.prior.PET(prior)) { + # PET prior: remove the PET column (samples are stored as "PET", not par_name) + cols_to_remove <- c(par_name, "PET") + + } else if (is.prior.PEESE(prior)) { + # PEESE prior: remove the PEESE column (samples are stored as "PEESE", not par_name) + cols_to_remove <- c(par_name, "PEESE") + + } else { + # simple prior: just remove the main column + cols_to_remove <- par_name + } + + # remove duplicates and filter to existing columns + cols_to_remove <- unique(cols_to_remove) + cols_to_remove <- cols_to_remove[cols_to_remove %in% colnames(model_samples)] + + model_samples <- model_samples[, !colnames(model_samples) %in% cols_to_remove, drop = FALSE] + + return(model_samples) +} + + +#' @rdname posterior_extraction_helpers +#' @param remove_parameters character vector of parameter names to remove, or TRUE to remove all non-formula parameters. +#' If "bias" is specified and the bias prior contains PET, PEESE, or weightfunction priors, +#' the corresponding parameters (PET, PEESE, omega) are also added to the removal list. +#' @param remove_formulas character vector of formula names whose parameters should be removed +#' @param keep_parameters character vector of parameter names to keep (all others removed unless in keep_formulas). +#' If "bias" is specified and the bias prior contains PET, PEESE, or weightfunction priors, +#' the corresponding parameters (PET, PEESE, omega) are also added to the keep list. +#' @param keep_formulas character vector of formula names whose parameters should be kept (all others removed unless in keep_parameters) +#' @param remove_spike_0 whether to remove spike at 0 priors +#' @return list with filtered model_samples and prior_list +.filter_parameters <- function(prior_list, remove_parameters = NULL, remove_formulas = NULL, + keep_parameters = NULL, keep_formulas = NULL, remove_spike_0 = TRUE) { + + # get formula parameter for each prior + prior_formulas <- sapply(prior_list, function(p) { + form <- attr(p, "parameter") + if (is.null(form)) "__none" else form + + }) + + # helper function to get bias-related parameters (PET, PEESE, omega) from a bias prior + .get_bias_params <- function(prior_list, bias_name = "bias") { + bias_params <- character(0) + if (bias_name %in% names(prior_list)) { + bias_prior <- prior_list[[bias_name]] + if (is.prior.mixture(bias_prior)) { + if (any(sapply(bias_prior, is.prior.PET))) { + bias_params <- c(bias_params, "PET") + } + if (any(sapply(bias_prior, is.prior.PEESE))) { + bias_params <- c(bias_params, "PEESE") + } + if (any(sapply(bias_prior, is.prior.weightfunction))) { + bias_params <- c(bias_params, "omega") + } + } else { + if (is.prior.PET(bias_prior)) { + bias_params <- c(bias_params, "PET") + } + if (is.prior.PEESE(bias_prior)) { + bias_params <- c(bias_params, "PEESE") + } + if (is.prior.weightfunction(bias_prior)) { + bias_params <- c(bias_params, "omega") + } + } + } + return(bias_params) + } + + # initialize parameters to remove + params_to_remove <- character(0) + + # handle remove_spike_0 + if (remove_spike_0) { + spike_0_params <- names(prior_list)[sapply(seq_along(prior_list), function(i) { + is.prior.point(prior_list[[i]]) && prior_list[[i]][["parameters"]][["location"]] == 0 + })] + params_to_remove <- c(params_to_remove, spike_0_params) + } + + # handle remove_parameters + if (is.logical(remove_parameters) && isTRUE(remove_parameters)) { + # remove all non-formula parameters + non_formula_params <- names(prior_list)[prior_formulas == "__none"] + params_to_remove <- c(params_to_remove, non_formula_params) + } else if (is.character(remove_parameters)) { + params_to_remove <- c(params_to_remove, remove_parameters) + # if "bias" is in remove_parameters, also add corresponding bias-related parameters + if ("bias" %in% remove_parameters) { + params_to_remove <- c(params_to_remove, .get_bias_params(prior_list, "bias")) + } + } + + # handle remove_formulas + if (!is.null(remove_formulas)) { + formula_params <- names(prior_list)[prior_formulas %in% remove_formulas] + params_to_remove <- c(params_to_remove, formula_params) + } + + # handle keep_parameters and keep_formulas (these define what to keep, everything else is removed) + if (!is.null(keep_parameters) || !is.null(keep_formulas)) { + # start with all parameters as candidates for removal + all_params <- names(prior_list) + + # determine which parameters to keep + params_to_keep <- character(0) + + if (!is.null(keep_parameters)) { + params_to_keep <- c(params_to_keep, keep_parameters) + # if "bias" is in keep_parameters, also add corresponding bias-related parameters + if ("bias" %in% keep_parameters) { + params_to_keep <- c(params_to_keep, .get_bias_params(prior_list, "bias")) + } + } + + if (!is.null(keep_formulas)) { + formula_params_to_keep <- names(prior_list)[prior_formulas %in% keep_formulas] + params_to_keep <- c(params_to_keep, formula_params_to_keep) + } + + # add parameters not in keep list to removal list + params_not_kept <- all_params[!all_params %in% params_to_keep] + params_to_remove <- c(params_to_remove, params_not_kept) + + # if "bias" is in params_not_kept, also add corresponding bias-related parameters + if ("bias" %in% params_not_kept) { + params_to_remove <- c(params_to_remove, .get_bias_params(prior_list, "bias")) + } + } + + # remove duplicates + + params_to_remove <- unique(params_to_remove) + + return(params_to_remove) +} + + +#' @rdname posterior_extraction_helpers +#' @param par parameter name +#' @param conditional whether to compute conditional summary +#' @param remove_inclusion whether to remove inclusion indicators +#' @param warnings character vector for collecting warnings +#' @return list with updated model_samples, prior_list, and warnings +.process_spike_and_slab <- function(model_samples, prior_list, par, conditional = FALSE, remove_inclusion = FALSE, warnings = NULL) { + + # prepare parameter names + if (is.prior.factor(.get_spike_and_slab_variable(prior_list[[par]]))) { + if (.get_prior_factor_levels(.get_spike_and_slab_variable(prior_list[[par]])) == 1) { + par_names <- par + } else { + par_names <- paste0(par, "[", 1:.get_prior_factor_levels(.get_spike_and_slab_variable(prior_list[[par]])), "]") + } + } else { + par_names <- par + } + + # change the samples between conditional/averaged based on the preferences + if (conditional) { + # compute the number of conditional samples + n_conditional_samples <- sum(model_samples[, colnames(model_samples) == paste0(par, "_indicator")] == 1) + + # replace null samples with NAs (important for later transformations) + model_samples[model_samples[, colnames(model_samples) == paste0(par, "_indicator")] != 1, par_names] <- NA + + # add warnings about conditional summary + warnings <- c(warnings, .runjags_conditional_warning(par_names, n_conditional_samples)) + } + + # remove the inclusion + model_samples <- model_samples[, colnames(model_samples) != paste0(par, "_inclusion"), drop = FALSE] + + # remove the latent variable + model_samples <- model_samples[, !colnames(model_samples) %in% gsub(par, paste0(par, "_variable"), par_names), drop = FALSE] + + # remove/rename the inclusions probabilities + if (remove_inclusion) { + model_samples <- model_samples[, colnames(model_samples) != paste0(par, "_indicator"), drop = FALSE] + } else { + colnames(model_samples)[colnames(model_samples) == paste0(par, "_indicator")] <- paste0(par, " (inclusion)") + } + + # modify the parameter list (forward the parameter attribute) + variable_component <- .get_spike_and_slab_variable(prior_list[[par]]) + attr(variable_component, "parameter") <- attr(prior_list[[par]], "parameter") + prior_list[[par]] <- variable_component + + return(list(model_samples = model_samples, prior_list = prior_list, warnings = warnings)) +} + + +#' @rdname posterior_extraction_helpers +#' @param transformations list of transformations to apply +#' @param transform_factors whether orthonormal/meandif will be transformed later +#' @return updated model_samples matrix +.apply_parameter_transformations <- function(model_samples, transformations, prior_list, transform_factors = FALSE) { + + if (is.null(transformations)) { + return(model_samples) + } + + for (par in names(transformations)) { + if (!is.prior.factor(prior_list[[par]])) { + # non-factor priors + model_samples[, par] <- do.call(transformations[[par]][["fun"]], c(list(model_samples[, par]), transformations[[par]][["arg"]])) + } else if ((!transform_factors && (is.prior.orthonormal(prior_list[[par]]) | is.prior.meandif(prior_list[[par]]))) || is.prior.treatment(prior_list[[par]])) { + # treatment priors, or orthonormal/meandif that won't be transformed to differences + par_names <- .JAGS_prior_factor_names(par, prior_list[[par]]) + + for (i in seq_along(par_names)) { + model_samples[, par_names[i]] <- do.call(transformations[[par]][["fun"]], c(list(model_samples[, par_names[i]]), transformations[[par]][["arg"]])) + } + } + } + + return(model_samples) +} + + +#' @rdname posterior_extraction_helpers +#' @param transform_factors whether to transform orthonormal/meandif to differences +#' @return updated model_samples matrix +.transform_factor_contrasts <- function(model_samples, prior_list, transform_factors = FALSE, transformations = NULL) { + + if (!transform_factors || !any(sapply(prior_list, function(x) is.prior.orthonormal(x) | is.prior.meandif(x)))) { + return(model_samples) + } + + message("The transformation was applied to the differences from the mean. Note that non-linear transformations do not map from the orthonormal/meandif contrasts to the differences from the mean.") + + for (par in names(prior_list)[sapply(prior_list, function(x) is.prior.orthonormal(x) | is.prior.meandif(x))]) { + + par_names <- .JAGS_prior_factor_names(par, prior_list[[par]]) + + temp_position <- min(which(colnames(model_samples) %in% par_names)) + temp_samples <- model_samples[, colnames(model_samples) %in% par_names, drop = FALSE] + model_samples <- model_samples[, !colnames(model_samples) %in% par_names, drop = FALSE] + + if (is.prior.orthonormal(prior_list[[par]])) { + transformed_samples <- temp_samples %*% t(contr.orthonormal(1:(.get_prior_factor_levels(prior_list[[par]]) + 1))) + } else if (is.prior.meandif(prior_list[[par]])) { + transformed_samples <- temp_samples %*% t(contr.meandif(1:(.get_prior_factor_levels(prior_list[[par]]) + 1))) + } + + # apply transformation if specified + if (!is.null(transformations[par])) { + for (i in 1:ncol(transformed_samples)) { + transformed_samples[, i] <- do.call(transformations[[par]][["fun"]], c(list(transformed_samples[, i]), transformations[[par]][["arg"]])) + } + } + + if (.is_prior_interaction(prior_list[[par]])) { + if (length(.get_prior_factor_level_names(prior_list[[par]])) == 1) { + transformed_names <- paste0(par, " [dif: ", .get_prior_factor_level_names(prior_list[[par]])[[1]], "]") + } else { + stop("orthonormal/meandif de-transformation for interaction of multiple factors is not implemented.") + } + } else { + transformed_names <- paste0(par, " [dif: ", .get_prior_factor_level_names(prior_list[[par]]), "]") + } + colnames(transformed_samples) <- transformed_names + + # place the transformed samples back + model_samples <- cbind( + if (temp_position > 1) model_samples[, 1:(temp_position - 1), drop = FALSE], + transformed_samples, + if (temp_position <= ncol(model_samples)) model_samples[, temp_position:ncol(model_samples), drop = FALSE] + ) + } + + return(model_samples) +} + + +#' @rdname posterior_extraction_helpers +#' @return updated model_samples matrix with renamed columns +.rename_factor_levels <- function(model_samples, prior_list) { + + # rename treatment factor levels + if (any(sapply(prior_list, is.prior.treatment))) { + for (par in names(prior_list)[sapply(prior_list, is.prior.treatment)]) { + if (!.is_prior_interaction(prior_list[[par]])) { + if (.get_prior_factor_levels(prior_list[[par]]) == 1) { + colnames(model_samples)[colnames(model_samples) == par] <- + paste0(par, "[", .get_prior_factor_level_names(prior_list[[par]])[-1], "]") + } else { + colnames(model_samples)[colnames(model_samples) %in% paste0(par, "[", 1:.get_prior_factor_levels(prior_list[[par]]), "]")] <- + paste0(par, "[", .get_prior_factor_level_names(prior_list[[par]])[-1], "]") + } + } else if (length(attr(prior_list[[par]], "levels")) == 1) { + colnames(model_samples)[colnames(model_samples) %in% paste0(par, "[", 1:.get_prior_factor_levels(prior_list[[par]]), "]")] <- + paste0(par, "[", .get_prior_factor_level_names(prior_list[[par]])[[1]][-1], "]") + } + } + } + + # rename independent factor levels + if (any(sapply(prior_list, is.prior.independent))) { + for (par in names(prior_list)[sapply(prior_list, is.prior.independent)]) { + if (!.is_prior_interaction(prior_list[[par]])) { + if (.get_prior_factor_levels(prior_list[[par]]) == 1) { + colnames(model_samples)[colnames(model_samples) == par] <- + paste0(par, "[", .get_prior_factor_level_names(prior_list[[par]]), "]") + } else { + colnames(model_samples)[colnames(model_samples) %in% paste0(par, "[", 1:.get_prior_factor_levels(prior_list[[par]]), "]")] <- + paste0(par, "[", .get_prior_factor_level_names(prior_list[[par]]), "]") + } + } else if (length(attr(prior_list[[par]], "levels")) == 1) { + colnames(model_samples)[colnames(model_samples) %in% paste0(par, "[", 1:.get_prior_factor_levels(prior_list[[par]]), "]")] <- + paste0(par, "[", .get_prior_factor_level_names(prior_list[[par]])[[1]], "]") + } + } + } + + return(model_samples) +} diff --git a/R/priors-density.R b/R/priors-density.R index f182c574..935023a0 100644 --- a/R/priors-density.R +++ b/R/priors-density.R @@ -81,7 +81,7 @@ density.prior <- function(x, }else if(!individual & is.prior.weightfunction(x)){ x_range <- c(0, 1) }else if(is.prior.spike_and_slab(x)){ - x_range <- range(.get_spike_and_slab_variable(x)[["truncation"]]["lower"], .get_spike_and_slab_variable(x)[["truncation"]]["upper"], 0) + x_range <- range(c(range(.get_spike_and_slab_variable(x), if(is.null(x_range_quant)) .range.prior_quantile_default(.get_spike_and_slab_variable(x)) else x_range_quant), 0)) }else if(is.prior.discrete(x)){ x_range <- c(x[["truncation"]]["lower"], x[["truncation"]]["upper"]) }else{ @@ -619,6 +619,14 @@ range.prior <- function(x, quantiles = NULL, ..., na.rm = FALSE){ inv = function(x, a = 0, b = 1)(x - a) / b, jac = function(x, a = 0, b = 1)1 / b ), + "exp_lin" = list( + # Exponential-linear transformation: exp(a + b * log(x)) + # Used for log-intercept unscaling where: intercept_orig = exp(log(intercept_z) * b + a) + # When a = 0 and b = 1, this is identity: exp(log(x)) = x + fun = function(x, a = 0, b = 1) exp(a + b * log(x)), + inv = function(x, a = 0, b = 1) exp((log(x) - a) / b), + jac = function(x, a = 0, b = 1) 1 / (b * x) + ), "tanh" = list( fun = tanh, inv = atanh, @@ -637,7 +645,7 @@ range.prior <- function(x, quantiles = NULL, ..., na.rm = FALSE){ }else{ - stop("Transformation must be either a character vector of length 1 corresponding to one of known transformations ('lin' = linear, 'tanh' = Fisher's z, 'exp' = exponential) or a list of three functions (fun = transformation function, inv = inverse transformation, jac = jacobian adjustment).") + stop("Transformation must be either a character vector of length 1 corresponding to one of known transformations ('lin' = linear, 'exp_lin' = exponential-linear for log-intercept, 'tanh' = Fisher's z, 'exp' = exponential) or a list of three functions (fun = transformation function, inv = inverse transformation, jac = jacobian adjustment).") } diff --git a/R/priors-print.R b/R/priors-print.R index ec2d7a00..f9e0a160 100644 --- a/R/priors-print.R +++ b/R/priors-print.R @@ -347,7 +347,8 @@ print.prior <- function(x, short_name = FALSE, parameter_names = FALSE, plot = F prior_components <- attr(x, "components") if(all(prior_components %in% c("null", "alternative"))){ - prior_components <- sort(prior_components) + prior_names <- prior_names[order(prior_components)] + prior_components <- prior_components[order(prior_components)] } if(!plot){ diff --git a/R/summary-tables.R b/R/summary-tables.R index 4d1967d0..c04c190d 100644 --- a/R/summary-tables.R +++ b/R/summary-tables.R @@ -34,6 +34,14 @@ #' @param transform_orthonormal (to be depreciated) whether factors #' with orthonormal prior distributions should be transformed to #' differences from the grand mean +#' @param transform_scaled whether coefficients from standardized +#' continuous predictors should be transformed back to the original +#' scale. Defaults to \code{FALSE}. +#' @param formula_scale named list containing standardization information +#' (mean and sd) for each standardized predictor. Required when +#' \code{transform_scaled = TRUE} for ensemble/marginal tables. For +#' \code{runjags_estimates_table}, this is automatically extracted from +#' the fit object's \code{formula_scale} attribute. #' @param title title to be added to the table #' @param footnotes footnotes to be added to the table #' @param warnings warnings to be added to the table @@ -65,7 +73,7 @@ NULL #' @rdname BayesTools_ensemble_tables -ensemble_estimates_table <- function(samples, parameters, probs = c(0.025, 0.95), title = NULL, footnotes = NULL, warnings = NULL, transform_factors = FALSE, transform_orthonormal = FALSE, formula_prefix = TRUE){ +ensemble_estimates_table <- function(samples, parameters, probs = c(0.025, 0.95), title = NULL, footnotes = NULL, warnings = NULL, transform_factors = FALSE, transform_orthonormal = FALSE, formula_prefix = TRUE, transform_scaled = FALSE, formula_scale = NULL){ # check input check_char(parameters, "parameters", check_length = 0) @@ -77,6 +85,8 @@ ensemble_estimates_table <- function(samples, parameters, probs = c(0.025, 0.95) check_bool(transform_factors, "transform_factors") check_bool(transform_orthonormal, "transform_orthonormal") check_bool(formula_prefix, "formula_prefix") + check_bool(transform_scaled, "transform_scaled") + check_list(formula_scale, "formula_scale", allow_NULL = TRUE) # depreciate transform_factors <- .depreciate.transform_orthonormal(transform_orthonormal, transform_factors) @@ -87,6 +97,11 @@ ensemble_estimates_table <- function(samples, parameters, probs = c(0.025, 0.95) samples <- transform_factor_samples(samples) } + # transform scaled coefficients back to original scale + if(transform_scaled && !is.null(formula_scale) && length(formula_scale) > 0){ + samples <- .transform_scale_samples_list(samples, formula_scale) + } + # extract values estimates_table <- NULL @@ -104,7 +119,7 @@ ensemble_estimates_table <- function(samples, parameters, probs = c(0.025, 0.95) } if(inherits(samples[[parameter]], "mixed_posteriors.formula")){ - parameter_name <- format_parameter_names(colnames(samples[[parameter]]), formula_parameters = attr(samples[[parameter]], "formula_parameter"), formula_prefix = formula_prefix) + parameter_name <- format_parameter_names(colnames(samples[[parameter]]), formula_parameters = attr(samples[[parameter]], "formula_parameter"), formula_prefix = formula_prefix, formula_scale = formula_scale) }else{ parameter_name <- colnames(samples[[parameter]]) } @@ -412,7 +427,7 @@ ensemble_diagnostics_empty_table <- function(title = NULL, footnotes = NULL, war } #' @rdname BayesTools_ensemble_tables -marginal_estimates_table <- function(samples, inference, parameters, probs = c(0.025, 0.95), logBF = FALSE, BF01 = FALSE, title = NULL, footnotes = NULL, warnings = NULL, formula_prefix = TRUE){ +marginal_estimates_table <- function(samples, inference, parameters, probs = c(0.025, 0.95), logBF = FALSE, BF01 = FALSE, title = NULL, footnotes = NULL, warnings = NULL, formula_prefix = TRUE, transform_scaled = FALSE, formula_scale = NULL){ # check input check_char(parameters, "parameters", check_length = 0) @@ -425,6 +440,13 @@ marginal_estimates_table <- function(samples, inference, parameters, probs = c(0 check_char(footnotes, "footnotes", check_length = 0, allow_NULL = TRUE) check_char(warnings, "warnings", check_length = 0, allow_NULL = TRUE) check_bool(formula_prefix, "formula_prefix") + check_bool(transform_scaled, "transform_scaled") + check_list(formula_scale, "formula_scale", allow_NULL = TRUE) + + # transform scaled coefficients back to original scale + if(transform_scaled && !is.null(formula_scale) && length(formula_scale) > 0){ + samples <- .transform_scale_samples_list(samples, formula_scale) + } # extract values @@ -473,7 +495,7 @@ marginal_estimates_table <- function(samples, inference, parameters, probs = c(0 }else{ parameter_name <- paste0(parameter, "[", names(samples[[parameter]]), "]") } - parameter_name <- format_parameter_names(parameter_name, formula_parameters = attr(samples[[parameter]], "formula_parameter"), formula_prefix = formula_prefix) + parameter_name <- format_parameter_names(parameter_name, formula_parameters = attr(samples[[parameter]], "formula_parameter"), formula_prefix = formula_prefix, formula_scale = formula_scale) }else{ parameter_name <- paste0(parameter, "[", names(samples[[parameter]]), "]") } @@ -586,10 +608,24 @@ marginal_estimates_table <- function(samples, inference, parameters, probs = c(0 #' to be added to the table #' @param remove_inclusion whether estimates of the inclusion probabilities #' should be excluded from the summary table. Defaults to \code{FALSE}. -#' @param remove_parameters parameters to be removed from the summary. Defaults -#' to \code{NULL}, i.e., including all parameters. +#' @param remove_parameters parameters to be removed from the summary. +#' Can be \code{NULL} (default, no removal), a character vector of parameter +#' names to remove, or \code{TRUE} to remove all parameters that are not +#' part of any formula. +#' @param remove_formulas character vector of formula names whose parameters +#' should be removed from the summary. Defaults to \code{NULL}. +#' @param keep_parameters character vector of parameter names to keep. +#' All other parameters will be removed unless they belong to formulas +#' specified in \code{keep_formulas}. Defaults to \code{NULL}. +#' @param keep_formulas character vector of formula names whose parameters +#' should be kept. All other parameters will be removed unless they are +#' specified in \code{keep_parameters}. Defaults to \code{NULL}. #' @param return_samples whether to return the transoformed and formated samples #' instead of the table. Defaults to \code{FALSE}. +#' @param remove_diagnostics whether to exclude MCMC diagnostics (MCMC error, +#' ESS, R-hat) from the output table. Defaults to \code{FALSE}. Setting to +#' \code{TRUE} will exclude diagnostics columns regardless of the +#' \code{conditional} setting. #' @inheritParams BayesTools_ensemble_tables #' #' @@ -712,8 +748,10 @@ model_summary_table <- function(model, model_description = NULL, title = NULL, f #' @rdname BayesTools_model_tables runjags_estimates_table <- function(fit, transformations = NULL, title = NULL, footnotes = NULL, warnings = NULL, conditional = FALSE, - remove_spike_0 = TRUE, transform_factors = FALSE, transform_orthonormal = FALSE, formula_prefix = TRUE, remove_inclusion = FALSE, remove_parameters = NULL, - return_samples = FALSE){ + probs = c(0.025, 0.5, 0.975), remove_spike_0 = TRUE, transform_factors = FALSE, transform_orthonormal = FALSE, + formula_prefix = TRUE, remove_inclusion = FALSE, remove_parameters = NULL, remove_formulas = NULL, + keep_parameters = NULL, keep_formulas = NULL, return_samples = FALSE, transform_scaled = FALSE, + remove_diagnostics = FALSE){ .check_runjags() # most of the code is shared with .diagnostics_plot_data function (keep them in sync on update) @@ -733,108 +771,53 @@ runjags_estimates_table <- function(fit, transformations = NULL, title = NULL, check_char(title, "title", allow_NULL = TRUE) check_char(footnotes, "footnotes", check_length = 0, allow_NULL = TRUE) check_char(warnings, "warnings", check_length = 0, allow_NULL = TRUE) + check_real(probs, "probs", lower = 0, upper = 1, check_length = 0) check_bool(remove_spike_0, "remove_spike_0") check_bool(conditional, "conditional") check_bool(transform_factors, "transform_factors") check_bool(transform_orthonormal, "transform_orthonormal") check_bool(formula_prefix, "formula_prefix") - check_char(remove_parameters, "remove_parameters", allow_NULL = TRUE, check_length = 0) + check_bool(transform_scaled, "transform_scaled") + check_bool(remove_diagnostics, "remove_diagnostics") + if(!is.null(remove_parameters) && !is.logical(remove_parameters)) + check_char(remove_parameters, "remove_parameters", allow_NULL = TRUE, check_length = 0) + if(is.logical(remove_parameters)) + check_bool(remove_parameters, "remove_parameters") + check_char(remove_formulas, "remove_formulas", allow_NULL = TRUE, check_length = 0) + check_char(keep_parameters, "keep_parameters", allow_NULL = TRUE, check_length = 0) + check_char(keep_formulas, "keep_formulas", allow_NULL = TRUE, check_length = 0) # depreciate transform_factors <- .depreciate.transform_orthonormal(transform_orthonormal, transform_factors) # get model samples - model_samples <- suppressWarnings(coda::as.mcmc(fit)) + model_samples <- .extract_posterior_samples(fit, as_list = FALSE) ### remove un-wanted estimates (or support values) - spike and slab priors already dealt with later (also remove the item from prior list) - for(i in rev(seq_along(prior_list))){ - - if(is.prior.simple(prior_list[[i]]) && prior_list[[i]][["distribution"]] == "invgamma"){ - ## invgamma support parameter - model_samples <- model_samples[,colnames(model_samples) != paste0("inv_",names(prior_list)[i]),drop=FALSE] - } - - if(is.prior.weightfunction(prior_list[[i]])){ - ## simple weight functions - # remove etas - if(prior_list[[i]][["distribution"]] %in% c("one.sided", "two.sided")){ - model_samples <- model_samples[,!grepl("eta", colnames(model_samples)),drop=FALSE] - } - - # rename the omegas - omega_cuts <- weightfunctions_mapping(prior_list[i], cuts_only = TRUE) - omega_names_old <- paste0("omega[", 1:(length(omega_cuts)-1),"]") - omega_names <- sapply(1:(length(omega_cuts)-1), function(i)paste0("omega[",omega_cuts[i],",",omega_cuts[i+1],"]")) - - # change the order of omegas - model_samples[,which(colnames(model_samples) %in% omega_names_old)] <- model_samples[,rev(which(colnames(model_samples) %in% omega_names_old)),drop=FALSE] - colnames(model_samples)[which(colnames(model_samples) %in% omega_names_old)] <- omega_names - - # remove omegas if requested - if("omega" %in% remove_parameters){ - model_samples <- model_samples[,!colnames(model_samples) %in% omega_names,drop=FALSE] - prior_list[i] <- NULL - } - - }else if((remove_spike_0 && is.prior.point(prior_list[[i]]) && prior_list[[i]][["parameters"]][["location"]] == 0) || (names(prior_list)[[i]] %in% remove_parameters)){ - ## zero spike priors or other parameters to be removed - if(is.prior.factor(prior_list[[i]])){ - model_samples <- model_samples[,!colnames(model_samples) %in% .JAGS_prior_factor_names(names(prior_list)[i], prior_list[[i]]),drop=FALSE] - }else{ - model_samples <- model_samples[,colnames(model_samples) != names(prior_list)[i],drop=FALSE] - } - prior_list[i] <- NULL - } + # compute filtered parameters using the helper function + remove_params_vec <- .filter_parameters( + prior_list = prior_list, + remove_parameters = remove_parameters, + remove_formulas = remove_formulas, + keep_parameters = keep_parameters, + keep_formulas = keep_formulas, + remove_spike_0 = remove_spike_0 + ) - } + cleaned <- .remove_auxiliary_parameters(model_samples, prior_list, remove_params_vec) + model_samples <- cleaned$model_samples + prior_list <- cleaned$prior_list # simplify mixture and spike and slab priors to simple priors # the samples and summary can be dealt with as any other prior (i.e., transformations later) for(par in names(prior_list)){ if(is.prior.spike_and_slab(prior_list[[par]])){ - # prepare parameter names - if(is.prior.factor(.get_spike_and_slab_variable(prior_list[[par]]))){ - if(.get_prior_factor_levels(.get_spike_and_slab_variable(prior_list[[par]])) == 1){ - par_names <- par - }else{ - par_names <- paste0(par, "[", 1:.get_prior_factor_levels(.get_spike_and_slab_variable(prior_list[[par]])), "]") - } - }else{ - par_names <- par - } - - # change the samples between conditional/averaged based on the preferences - if(conditional){ - - # compute the number of conditional samples - n_conditional_samples <- sum(model_samples[,colnames(model_samples) == paste0(par, "_indicator")] == 1) - - # replace null samples with NAs (important for later transformations) - model_samples[model_samples[,colnames(model_samples) == paste0(par, "_indicator")] != 1, par_names] <- NA - - # add warnings about conditional summary - warnings <- c(warnings, .runjags_conditional_warning(par_names, n_conditional_samples)) - } - - # remove the inclusion - model_samples <- model_samples[,colnames(model_samples) != paste0(par, "_inclusion"),drop=FALSE] - - # remove the latent variable - model_samples <- model_samples[,!colnames(model_samples) %in% gsub(par, paste0(par, "_variable"), par_names),drop=FALSE] - - # remove/rename the inclusions probabilities - if(remove_inclusion){ - model_samples <- model_samples[,colnames(model_samples) != paste0(par, "_indicator"),drop=FALSE] - }else{ - colnames(model_samples)[colnames(model_samples) == paste0(par, "_indicator")] <- paste0(par, " (inclusion)") - } - - # modify the parameter list (forward the parameter attribute) - variable_component <- .get_spike_and_slab_variable(prior_list[[par]]) - attr(variable_component, "parameter") <- attr(prior_list[[par]], "parameter") - prior_list[[par]] <- variable_component - + # process spike and slab using helper function + processed <- .process_spike_and_slab(model_samples, prior_list, par, conditional, remove_inclusion, warnings) + model_samples <- processed$model_samples + prior_list <- processed$prior_list + warnings <- processed$warnings }else if(is.prior.mixture(prior_list[[par]])){ @@ -919,7 +902,11 @@ runjags_estimates_table <- function(fit, transformations = NULL, title = NULL, }else{ # prepare parameter names - par_names <- par + if(inherits(prior_list[[par]], "prior.factor_mixture")){ + par_names <- .JAGS_prior_factor_names(par, prior_list[[par]]) + }else{ + par_names <- par + } # change the samples between conditional/averaged based on the preferences if(conditional){ @@ -1027,104 +1014,19 @@ runjags_estimates_table <- function(fit, transformations = NULL, title = NULL, } # apply transformations (not orthornormal if they are to be returned transformed to diffs) - if(!is.null(transformations)){ - for(par in names(transformations)){ - if(!is.prior.factor(prior_list[[par]])){ - - # non-factor priors - model_samples[,par] <- do.call(transformations[[par]][["fun"]], c(list(model_samples[,par]), transformations[[par]][["arg"]])) - - }else if((!transform_factors && (is.prior.orthonormal(prior_list[[par]]) | is.prior.meandif(prior_list[[par]]))) || is.prior.treatment(prior_list[[par]])){ - - # treatment priors - par_names <- .JAGS_prior_factor_names(par, prior_list[[par]]) - - for(i in seq_along(par_names)){ - model_samples[,par_names[i]] <- do.call(transformations[[par]][["fun"]], c(list(model_samples[,par_names[i]]), transformations[[par]][["arg"]])) - } - } - } - } + model_samples <- .apply_parameter_transformations(model_samples, transformations, prior_list, transform_factors) # transform orthonormal factors to differences from mean - if(transform_factors & any(sapply(prior_list, function(x) is.prior.orthonormal(x) | is.prior.meandif(x)))){ - message("The transformation was applied to the differences from the mean. Note that non-linear transformations do not map from the orthonormal/meandif contrasts to the differences from the mean.") - for(par in names(prior_list)[sapply(prior_list, function(x) is.prior.orthonormal(x) | is.prior.meandif(x))]){ - - par_names <- .JAGS_prior_factor_names(par, prior_list[[par]]) - - temp_position <- min(which(colnames(model_samples) %in% par_names)) - temp_samples <- model_samples[, colnames(model_samples) %in% par_names,drop=FALSE] - model_samples <- model_samples[,!colnames(model_samples) %in% par_names,drop=FALSE] - - if(is.prior.orthonormal(prior_list[[par]])){ - transformed_samples <- temp_samples %*% t(contr.orthonormal(1:(.get_prior_factor_levels(prior_list[[par]])+1))) - }else if(is.prior.meandif(prior_list[[par]])){ - transformed_samples <- temp_samples %*% t(contr.meandif(1:(.get_prior_factor_levels(prior_list[[par]])+1))) - } - - # apply transformation if specified - if(!is.null(transformations[par])){ - for(i in 1:ncol(transformed_samples)){ - transformed_samples[,i] <- do.call(transformations[[par]][["fun"]], c(list(transformed_samples[,i]), transformations[[par]][["arg"]])) - } - } - + model_samples <- .transform_factor_contrasts(model_samples, prior_list, transform_factors, transformations) - if(.is_prior_interaction(prior_list[[par]])){ - if(length(.get_prior_factor_level_names(prior_list[[par]])) == 1){ - transformed_names <- paste0(par, " [dif: ", .get_prior_factor_level_names(prior_list[[par]])[[1]],"]") - }else{ - stop("orthonormal/meandif de-transformation for interaction of multiple factors is not implemented.") - } - }else{ - transformed_names <- paste0(par, " [dif: ", .get_prior_factor_level_names(prior_list[[par]]),"]") - } - colnames(transformed_samples) <- transformed_names - - # place the transformed samples back - model_samples <- cbind( - if(temp_position > 1) model_samples[,1:(temp_position-1),drop=FALSE], - transformed_samples, - if(temp_position <= ncol(model_samples)) model_samples[,temp_position:ncol(model_samples),drop=FALSE] - ) + # rename factor levels + model_samples <- .rename_factor_levels(model_samples, prior_list) - } - } - - # rename treatment factor levels - if(any(sapply(prior_list, is.prior.treatment))){ - for(par in names(prior_list)[sapply(prior_list, is.prior.treatment)]){ - if(!.is_prior_interaction(prior_list[[par]])){ - if(.get_prior_factor_levels(prior_list[[par]]) == 1){ - colnames(model_samples)[colnames(model_samples) == par] <- - paste0(par,"[",.get_prior_factor_level_names(prior_list[[par]])[-1], "]") - }else{ - colnames(model_samples)[colnames(model_samples) %in% paste0(par,"[",1:.get_prior_factor_levels(prior_list[[par]]),"]")] <- - paste0(par,"[",.get_prior_factor_level_names(prior_list[[par]])[-1], "]") - } - }else if(length(attr(prior_list[[par]], "levels")) == 1){ - colnames(model_samples)[colnames(model_samples) %in% paste0(par,"[",1:.get_prior_factor_levels(prior_list[[par]]),"]")] <- - paste0(par,"[",.get_prior_factor_level_names(prior_list[[par]])[[1]][-1], "]") - } - } - } - - # rename independent factor levels - if(any(sapply(prior_list, is.prior.independent))){ - for(par in names(prior_list)[sapply(prior_list, is.prior.independent)]){ - if(!.is_prior_interaction(prior_list[[par]])){ - if(.get_prior_factor_levels(prior_list[[par]]) == 1){ - colnames(model_samples)[colnames(model_samples) == par] <- - paste0(par,"[",.get_prior_factor_level_names(prior_list[[par]]), "]") - }else{ - colnames(model_samples)[colnames(model_samples) %in% paste0(par,"[",1:.get_prior_factor_levels(prior_list[[par]]),"]")] <- - paste0(par,"[",.get_prior_factor_level_names(prior_list[[par]]), "]") - } - }else if(length(attr(prior_list[[par]], "levels")) == 1){ - colnames(model_samples)[colnames(model_samples) %in% paste0(par,"[",1:.get_prior_factor_levels(prior_list[[par]]),"]")] <- - paste0(par,"[",.get_prior_factor_level_names(prior_list[[par]])[[1]], "]") - } + # transform scaled coefficients back to original scale + if(transform_scaled){ + formula_scale <- attr(fit, "formula_scale") + if(!is.null(formula_scale) && length(formula_scale) > 0){ + model_samples <- transform_scale_samples(model_samples, formula_scale) } } @@ -1137,7 +1039,8 @@ runjags_estimates_table <- function(fit, transformations = NULL, title = NULL, parameters = colnames(model_samples), formula_parameters = unique(unlist(lapply(prior_list, attr, which = "parameter"))), formula_random = unique(unlist(lapply(prior_list, attr, which = "random_factor"))), - formula_prefix = formula_prefix) + formula_prefix = formula_prefix, + formula_scale = if(transform_scaled) formula_scale else NULL) } # return samples if requested @@ -1148,14 +1051,15 @@ runjags_estimates_table <- function(fit, transformations = NULL, title = NULL, # compute the summary if(ncol(model_samples) == 0){ - return(runjags_estimates_empty_table(title = title, footnotes = footnotes, warnings = warnings)) + return(runjags_estimates_empty_table(probs = probs, title = title, footnotes = footnotes, warnings = warnings)) }else{ - runjags_summary <- .runjags_summary_fast(model_samples, n_samples = fit$sample, n_chains = length(fit$mcmc), conditional = conditional) + runjags_summary <- .runjags_summary_fast(model_samples, n_samples = fit$sample, n_chains = length(fit$mcmc), conditional = conditional, probs = probs, remove_diagnostics = remove_diagnostics) } # prepare output + n_estimate_cols <- 2 + length(probs) # Mean, SD, quantiles class(runjags_summary) <- c("BayesTools_table", "BayesTools_runjags_summary", class(runjags_summary)) - attr(runjags_summary, "type") <- c(rep("estimate", 5), if(!conditional) c("MCMC_error", "MCMC_SD_error", "ESS", "R_hat")) + attr(runjags_summary, "type") <- c(rep("estimate", n_estimate_cols), if(!conditional && !remove_diagnostics) c("MCMC_error", "MCMC_SD_error", "ESS", "R_hat")) attr(runjags_summary, "parameters") <- parameter_names attr(runjags_summary, "rownames") <- TRUE attr(runjags_summary, "title") <- title @@ -1258,7 +1162,8 @@ runjags_inference_table <- function(fit, title = NULL, footnotes = NULL, warnin parameters = rownames(runjags_summary), formula_parameters = unique(unlist(lapply(prior_list, attr, which = "parameter"))), formula_random = unique(unlist(lapply(prior_list, attr, which = "random_factor"))), - formula_prefix = formula_prefix) + formula_prefix = formula_prefix, + formula_scale = NULL) } class(runjags_summary) <- c("BayesTools_table", "BayesTools_runjags_inference", class(runjags_summary)) @@ -1318,13 +1223,14 @@ model_summary_empty_table <- function(model_description = NULL, title = NULL, fo } #' @rdname BayesTools_model_tables -runjags_estimates_empty_table <- function(title = NULL, footnotes = NULL, warnings = NULL){ +runjags_estimates_empty_table <- function(probs = c(0.025, 0.5, 0.975), title = NULL, footnotes = NULL, warnings = NULL){ - empty_table <- data.frame(matrix(nrow = 0, ncol = 9)) - colnames(empty_table) <- c("Mean", "SD", "lCI", "Median", "uCI", "MCMC_error", "MCMC_SD_error", "ESS", "R_hat") + n_estimate_cols <- 2 + length(probs) # Mean, SD, quantiles + empty_table <- data.frame(matrix(nrow = 0, ncol = n_estimate_cols + 4), check.names = FALSE) + colnames(empty_table) <- c("Mean", "SD", as.character(probs), "MCMC_error", "MCMC_SD_error", "ESS", "R_hat") class(empty_table) <- c("BayesTools_table", "BayesTools_runjags_summary", class(empty_table)) - attr(empty_table, "type") <- c(rep("estimate", 5), "MCMC_error", "MCMC_SD_error", "ESS", "R_hat") + attr(empty_table, "type") <- c(rep("estimate", n_estimate_cols), "MCMC_error", "MCMC_SD_error", "ESS", "R_hat") attr(empty_table, "rownames") <- FALSE attr(empty_table, "title") <- title attr(empty_table, "footnotes") <- footnotes @@ -1406,15 +1312,16 @@ stan_estimates_table <- function(fit, transformations = NULL, title = NULL, foo # rename the rest - colnames(stan_summary)[colnames(stan_summary) == "Lower95"] <- "lCI" - colnames(stan_summary)[colnames(stan_summary) == "Upper95"] <- "uCI" + colnames(stan_summary)[colnames(stan_summary) == "Lower95"] <- "0.025" + colnames(stan_summary)[colnames(stan_summary) == "Median"] <- "0.5" + colnames(stan_summary)[colnames(stan_summary) == "Upper95"] <- "0.975" colnames(stan_summary)[colnames(stan_summary) == "MCerr"] <- "MCMC_error" colnames(stan_summary)[colnames(stan_summary) == "MC.ofSD"] <- "MCMC_SD_error" colnames(stan_summary)[colnames(stan_summary) == "SSeff"] <- "ESS" colnames(stan_summary)[colnames(stan_summary) == "psrf"] <- "R_hat" # reorder the columns - stan_summary <- stan_summary[,c("Mean", "SD", "lCI", "Median", "uCI", "MCMC_error", "MCMC_SD_error", "ESS", "R_hat"), drop = FALSE] + stan_summary <- stan_summary[,c("Mean", "SD", "0.025", "0.5", "0.975", "MCMC_error", "MCMC_SD_error", "ESS", "R_hat"), drop = FALSE] # store parameter names parameter_names <- rownames(stan_summary) @@ -1776,22 +1683,25 @@ update.BayesTools_table <- function(object, title = NULL, footnotes = NULL, warn "ESS", "R_hat", "MCMC_error", "MCMC_SD_error", "min_ESS", "max_R_hat", "max_MCMC_error", "max_MCMC_SD_error"), allow_NULL = allow_NULL) } -.runjags_summary_fast <- function(model_samples, n_samples, n_chains, conditional){ +.runjags_summary_fast <- function(model_samples, n_samples, n_chains, conditional, probs = c(0.025, 0.975), remove_diagnostics = FALSE){ + + # compute quantiles dynamically + quantile_cols <- lapply(probs, function(p) apply(model_samples, 2, stats::quantile, probs = p, na.rm = TRUE)) + names(quantile_cols) <- as.character(probs) # the chains needs to be kept merged for conditional summary (due to NAs in the chains) runjags_summary <- cbind.data.frame( - "Mean" = apply(model_samples, 2, mean, na.rm = TRUE), - "SD" = apply(model_samples, 2, stats::sd, na.rm = TRUE), - "lCI" = apply(model_samples, 2, stats::quantile, probs = 0.025, na.rm = TRUE), - "Median" = apply(model_samples, 2, stats::median, na.rm = TRUE), - "uCI" = apply(model_samples, 2, stats::quantile, probs = 0.975, na.rm = TRUE) + "Mean" = apply(model_samples, 2, mean, na.rm = TRUE), + "SD" = apply(model_samples, 2, stats::sd, na.rm = TRUE), + as.data.frame(quantile_cols, check.names = FALSE) ) # remove all but Mean for inclusions - runjags_summary[grepl("(inclusion)", rownames(runjags_summary)), c("SD", "lCI", "Median", "uCI")] <- NA + quantile_col_names <- as.character(probs) + runjags_summary[grepl("(inclusion)", rownames(runjags_summary)), c("SD", quantile_col_names)] <- NA - # don't produce fit diagnostics for conditional samples (different chain lengths etc...) - if(conditional){ + # don't produce fit diagnostics for conditional samples (different chain lengths etc...) or if remove_diagnostics is TRUE + if(conditional || remove_diagnostics){ return(runjags_summary) } @@ -1836,3 +1746,89 @@ update.BayesTools_table <- function(object, title = NULL, footnotes = NULL, warn return() } } + +# Helper function to transform scaled samples in list format (for ensemble/marginal tables) +# Uses the combinatorial unscaling algorithm via the helper in JAGS-formula.R +.transform_scale_samples_list <- function(samples, formula_scale){ + + if(is.null(formula_scale) || length(formula_scale) == 0){ + return(samples) + } + + # Get all parameter names that have samples + sample_names <- names(samples) + + # Identify which samples are numeric or matrix (can be transformed) + transformable <- sapply(samples, function(x) is.numeric(x) || is.matrix(x)) + transformable_names <- sample_names[transformable] + + if(length(transformable_names) == 0){ + return(samples) + } + + # Determine the structure of each sample element + # (matrix with multiple columns for factors, or simple numeric/matrix for continuous) + # We need to handle each structure appropriately + + # For simplicity, we'll process each parameter individually using its structure + # But the combinatorial algorithm needs all parameters together + + # Approach: Build a single matrix with all parameters, apply transformation, extract back + # This requires handling the case where some parameters are matrices (factor levels) + + # First, identify simple (non-factor) parameters that can use the matrix approach + simple_params <- character(0) + factor_params <- character(0) + + for(name in transformable_names){ + if(is.matrix(samples[[name]]) && ncol(samples[[name]]) > 1){ + # Multi-column matrix - likely factor levels, skip for now + factor_params <- c(factor_params, name) + }else{ + simple_params <- c(simple_params, name) + } + } + + if(length(simple_params) > 0){ + # Build a matrix from simple parameters + # Each parameter becomes a column, samples are rows + n_samples <- if(is.matrix(samples[[simple_params[1]]])){ + nrow(samples[[simple_params[1]]]) + }else{ + length(samples[[simple_params[1]]]) + } + + posterior_matrix <- matrix(NA, nrow = n_samples, ncol = length(simple_params)) + colnames(posterior_matrix) <- simple_params + + for(i in seq_along(simple_params)){ + name <- simple_params[i] + if(is.matrix(samples[[name]])){ + posterior_matrix[, i] <- samples[[name]][, 1] + }else{ + posterior_matrix[, i] <- samples[[name]] + } + } + + # Apply the combinatorial unscaling transformation + posterior_matrix <- .apply_unscale_transform(posterior_matrix, formula_scale) + + # Extract back to list, preserving class and attributes + for(i in seq_along(simple_params)){ + name <- simple_params[i] + if(is.matrix(samples[[name]])){ + samples[[name]][, 1] <- posterior_matrix[, i] + }else{ + # Preserve class and attributes + old_attrs <- attributes(samples[[name]]) + samples[[name]] <- posterior_matrix[, i] + # Restore attributes (except names which may have changed) + for(attr_name in setdiff(names(old_attrs), "names")){ + attr(samples[[name]], attr_name) <- old_attrs[[attr_name]] + } + } + } + } + + return(samples) +} \ No newline at end of file diff --git a/R/tools.R b/R/tools.R index ecb1cbc0..c754652a 100644 --- a/R/tools.R +++ b/R/tools.R @@ -189,7 +189,7 @@ check_list <- function(x, name, check_length = 0, check_names = NULL, all_obje # helper functions .is.wholenumber <- function(x, na.rm = FALSE, tol = .Machine$double.eps^0.5){ if(na.rm){ - return(abs(x - round(stats::na.omit(x))) < tol) + return(stats::na.omit(abs(x - round(x))) < tol) }else{ return(abs(x - round(x)) < tol) } diff --git a/README.md b/README.md index 4707cd28..9023d62a 100644 --- a/README.md +++ b/README.md @@ -196,8 +196,8 @@ summary for the fitted model. ``` r # formatted summary tables runjags_estimates_table(fit1, priors_list1) -#> Mean SD lCI Median uCI error(MCMC) error(MCMC)/SD ESS R-hat -#> mu 0.116 0.304 -0.469 0.117 0.715 0.00242 0.008 15748 1.000 +#> Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +#> mu 0.116 0.304 -0.469 0.117 0.715 0.00242 0.008 15748 1.000 ``` We create a `log_posterior` function that defines the log likelihood of diff --git a/man/BayesTools_ensemble_tables.Rd b/man/BayesTools_ensemble_tables.Rd index 0eafbea1..1f8ec83d 100644 --- a/man/BayesTools_ensemble_tables.Rd +++ b/man/BayesTools_ensemble_tables.Rd @@ -22,7 +22,9 @@ ensemble_estimates_table( warnings = NULL, transform_factors = FALSE, transform_orthonormal = FALSE, - formula_prefix = TRUE + formula_prefix = TRUE, + transform_scaled = FALSE, + formula_scale = NULL ) ensemble_inference_table( @@ -84,7 +86,9 @@ marginal_estimates_table( title = NULL, footnotes = NULL, warnings = NULL, - formula_prefix = TRUE + formula_prefix = TRUE, + transform_scaled = FALSE, + formula_scale = NULL ) } \arguments{ @@ -114,6 +118,16 @@ differences from the grand mean} \item{formula_prefix}{whether the parameter prefix from formula should be printed. Defaults to \code{TRUE}.} +\item{transform_scaled}{whether coefficients from standardized +continuous predictors should be transformed back to the original +scale. Defaults to \code{FALSE}.} + +\item{formula_scale}{named list containing standardization information +(mean and sd) for each standardized predictor. Required when +\code{transform_scaled = TRUE} for ensemble/marginal tables. For +\code{runjags_estimates_table}, this is automatically extracted from +the fit object's \code{formula_scale} attribute.} + \item{inference}{model inference created by \link{ensemble_inference}} \item{logBF}{whether the Bayes factor should be on log scale} diff --git a/man/BayesTools_model_tables.Rd b/man/BayesTools_model_tables.Rd index 4836c9d6..bd04bd27 100644 --- a/man/BayesTools_model_tables.Rd +++ b/man/BayesTools_model_tables.Rd @@ -35,13 +35,19 @@ runjags_estimates_table( footnotes = NULL, warnings = NULL, conditional = FALSE, + probs = c(0.025, 0.5, 0.975), remove_spike_0 = TRUE, transform_factors = FALSE, transform_orthonormal = FALSE, formula_prefix = TRUE, remove_inclusion = FALSE, remove_parameters = NULL, - return_samples = FALSE + remove_formulas = NULL, + keep_parameters = NULL, + keep_formulas = NULL, + return_samples = FALSE, + transform_scaled = FALSE, + remove_diagnostics = FALSE ) runjags_inference_table( @@ -59,13 +65,19 @@ JAGS_estimates_table( footnotes = NULL, warnings = NULL, conditional = FALSE, + probs = c(0.025, 0.5, 0.975), remove_spike_0 = TRUE, transform_factors = FALSE, transform_orthonormal = FALSE, formula_prefix = TRUE, remove_inclusion = FALSE, remove_parameters = NULL, - return_samples = FALSE + remove_formulas = NULL, + keep_parameters = NULL, + keep_formulas = NULL, + return_samples = FALSE, + transform_scaled = FALSE, + remove_diagnostics = FALSE ) JAGS_inference_table( @@ -95,11 +107,21 @@ model_summary_empty_table( warnings = NULL ) -runjags_estimates_empty_table(title = NULL, footnotes = NULL, warnings = NULL) +runjags_estimates_empty_table( + probs = c(0.025, 0.5, 0.975), + title = NULL, + footnotes = NULL, + warnings = NULL +) runjags_inference_empty_table(title = NULL, footnotes = NULL, warnings = NULL) -JAGS_estimates_empty_table(title = NULL, footnotes = NULL, warnings = NULL) +JAGS_estimates_empty_table( + probs = c(0.025, 0.5, 0.975), + title = NULL, + footnotes = NULL, + warnings = NULL +) JAGS_inference_empty_table(title = NULL, footnotes = NULL, warnings = NULL) @@ -137,8 +159,10 @@ shortened. Defaults to \code{FALSE}.} \item{formula_prefix}{whether the parameter prefix from formula should be printed. Defaults to \code{TRUE}.} -\item{remove_parameters}{parameters to be removed from the summary. Defaults -to \code{NULL}, i.e., including all parameters.} +\item{remove_parameters}{parameters to be removed from the summary. +Can be \code{NULL} (default, no removal), a character vector of parameter +names to remove, or \code{TRUE} to remove all parameters that are not +part of any formula.} \item{fit}{runjags model fit} @@ -148,6 +172,8 @@ to specific parameters} \item{conditional}{summarizes estimates conditional on being included in the model for spike and slab priors. Defaults to \code{FALSE}.} +\item{probs}{quantiles for parameter estimates} + \item{transform_factors}{whether factors with orthonormal/meandif prior distribution should be transformed to differences from the grand mean} @@ -159,8 +185,28 @@ differences from the grand mean} \item{remove_inclusion}{whether estimates of the inclusion probabilities should be excluded from the summary table. Defaults to \code{FALSE}.} +\item{remove_formulas}{character vector of formula names whose parameters +should be removed from the summary. Defaults to \code{NULL}.} + +\item{keep_parameters}{character vector of parameter names to keep. +All other parameters will be removed unless they belong to formulas +specified in \code{keep_formulas}. Defaults to \code{NULL}.} + +\item{keep_formulas}{character vector of formula names whose parameters +should be kept. All other parameters will be removed unless they are +specified in \code{keep_parameters}. Defaults to \code{NULL}.} + \item{return_samples}{whether to return the transoformed and formated samples instead of the table. Defaults to \code{FALSE}.} + +\item{transform_scaled}{whether coefficients from standardized +continuous predictors should be transformed back to the original +scale. Defaults to \code{FALSE}.} + +\item{remove_diagnostics}{whether to exclude MCMC diagnostics (MCMC error, +ESS, R-hat) from the output table. Defaults to \code{FALSE}. Setting to +\code{TRUE} will exclude diagnostics columns regardless of the +\code{conditional} setting.} } \value{ \code{model_summary_table} returns a table with diff --git a/man/JAGS_bridgesampling.Rd b/man/JAGS_bridgesampling.Rd index a758061b..7e782e3d 100644 --- a/man/JAGS_bridgesampling.Rd +++ b/man/JAGS_bridgesampling.Rd @@ -12,6 +12,7 @@ JAGS_bridgesampling( formula_list = NULL, formula_data_list = NULL, formula_prior_list = NULL, + formula_scale_list = NULL, add_parameters = NULL, add_bounds = NULL, maxiter = 10000, @@ -44,6 +45,11 @@ returns the log of the unnormalized posterior density of the model part} the names of the prior distribution correspond to the parameter names) of parameters specified within the \code{formula}} +\item{formula_scale_list}{named list of named lists for standardizing continuous predictors +(names of the lists correspond to the parameter name created by each of the formula). +Each entry should be a named list where continuous predictors with \code{TRUE} values will +be standardized. Defaults to \code{NULL} (no standardization).} + \item{add_parameters}{vector of additional parameter names that should be used in bridgesampling but were not specified in the \code{prior_list}} diff --git a/man/JAGS_evaluate_formula.Rd b/man/JAGS_evaluate_formula.Rd index a6f8b7e1..e066d4dc 100644 --- a/man/JAGS_evaluate_formula.Rd +++ b/man/JAGS_evaluate_formula.Rd @@ -11,7 +11,9 @@ JAGS_evaluate_formula(fit, formula, parameter, data, prior_list) samples obtained with \link[rjags]{rjags-package}} \item{formula}{formula specifying the right hand side of the assignment (the -left hand side is ignored)} +left hand side is ignored). If the formula has a \code{"log(intercept)"} +attribute set to \code{TRUE}, the intercept values will be log-transformed +before computing the linear predictor.} \item{parameter}{name of the parameter created with the formula} diff --git a/man/JAGS_fit.Rd b/man/JAGS_fit.Rd index 4fcb018d..39b87367 100644 --- a/man/JAGS_fit.Rd +++ b/man/JAGS_fit.Rd @@ -12,6 +12,7 @@ JAGS_fit( formula_list = NULL, formula_data_list = NULL, formula_prior_list = NULL, + formula_scale_list = NULL, chains = 4, adapt = 500, burnin = 1000, @@ -61,6 +62,11 @@ JAGS_extend( the names of the prior distribution correspond to the parameter names) of parameters specified within the \code{formula}} +\item{formula_scale_list}{named list of named lists for standardizing continuous predictors +(names of the lists correspond to the parameter name created by each of the formula). +Each entry should be a named list where continuous predictors with \code{TRUE} values will +be standardized. Defaults to \code{NULL} (no standardization).} + \item{chains}{number of chains to be run, defaults to \code{4}} \item{adapt}{number of samples used for adapting the MCMC chains, defaults to \code{500}} diff --git a/man/JAGS_formula.Rd b/man/JAGS_formula.Rd index e0dfe2d0..8c263eab 100644 --- a/man/JAGS_formula.Rd +++ b/man/JAGS_formula.Rd @@ -4,12 +4,15 @@ \alias{JAGS_formula} \title{Create JAGS formula syntax and data object} \usage{ -JAGS_formula(formula, parameter, data, prior_list) +JAGS_formula(formula, parameter, data, prior_list, formula_scale = NULL) } \arguments{ \item{formula}{formula specifying the right hand side of the assignment (the left hand side is ignored). If the formula contains \code{-1}, it will be -automatically converted to include an intercept with a spike(0) prior.} +automatically converted to include an intercept with a spike(0) prior. +The formula can also have a \code{"log(intercept)"} attribute set to \code{TRUE} +to generate syntax of the form \code{log(intercept) + sum(beta_i * x_i)}, which +is useful for parameters that must be positive (e.g., standard deviation).} \item{parameter}{name of the parameter to be created with the formula} @@ -18,11 +21,26 @@ automatically converted to include an intercept with a spike(0) prior.} \item{prior_list}{named list of prior distribution of parameters specified within the \code{formula}. When using \code{-1} in the formula, an "intercept" prior can be explicitly specified; otherwise, \code{prior("spike", list(0))} is -automatically added.} +automatically added. The list can also include two special entries:} + +\item{formula_scale}{named list specifying whether to standardize continuous predictors. +If \code{NULL} (default), no standardization is applied. If a named list is provided, +continuous predictors with \code{TRUE} values will be standardized (mean-centered and +scaled by standard deviation). The intercept is never standardized. +\describe{ +\item{\code{"__default_continuous"}}{A prior to use for any continuous predictors +(including the intercept) that are not explicitly specified in the prior list.} +\item{\code{"__default_factor"}}{A prior to use for any factor predictors +(including interactions involving factors) that are not explicitly specified +in the prior list.} +} +These default priors allow for more concise specification when many predictors +share the same prior distribution.} } \value{ \code{JAGS_formula} returns a list containing the formula JAGS syntax, -JAGS data object, and modified prior_list. +JAGS data object, modified prior_list, and (if standardization was applied) a +\code{formula_scale} list with standardization information for back-transformation. } \description{ Creates a JAGS formula syntax, prepares data input, and @@ -34,6 +52,10 @@ When a formula with \code{-1} (no intercept) is specified, the function automatically removes the \code{-1}, adds an intercept back to the formula, and includes a spike(0) prior for the intercept to ensure equivalent model behavior while maintaining consistent formula parsing. + +When using default priors (\code{"__default_continuous"} or \code{"__default_factor"}), +explicitly specified priors for individual terms take precedence over the defaults. +The defaults are only applied to terms that are not already in the prior list. } \examples{ # simulate data @@ -69,6 +91,17 @@ formula_no_intercept <- JAGS_formula( parameter = "mu", data = df, prior_list = prior_list_no_intercept) # Equivalent to specifying intercept = prior("spike", list(0)) +# using default priors for simpler specification +prior_list_defaults <- list( + "__default_continuous" = prior("normal", list(0, 1)), + "__default_factor" = prior_factor("normal", list(0, 0.5), contrast = "treatment") +) +formula_defaults <- JAGS_formula( + formula = ~ x_cont + x_fac3, + parameter = "mu", data = df, prior_list = prior_list_defaults) +# intercept and x_cont get the default continuous prior +# x_fac3 gets the default factor prior + } \seealso{ \code{\link[=JAGS_fit]{JAGS_fit()}} diff --git a/man/JAGS_marglik_parameters.Rd b/man/JAGS_marglik_parameters.Rd index fa7f224a..5bb37e16 100644 --- a/man/JAGS_marglik_parameters.Rd +++ b/man/JAGS_marglik_parameters.Rd @@ -9,6 +9,7 @@ JAGS_marglik_parameters(samples, prior_list) JAGS_marglik_parameters_formula( samples, + formula_list, formula_data_list, formula_prior_list, prior_list_parameters @@ -22,6 +23,9 @@ function} (names correspond to the parameter names) of parameters not specified within the \code{formula_list}} +\item{formula_list}{named list of formulas to be added to the model +(names correspond to the parameter name created by each of the formula)} + \item{formula_data_list}{named list of data frames containing data for each formula (names of the lists correspond to the parameter name created by each of the formula)} diff --git a/man/as_mixed_posteriors.Rd b/man/as_mixed_posteriors.Rd index 77bb85c3..e772cb5a 100644 --- a/man/as_mixed_posteriors.Rd +++ b/man/as_mixed_posteriors.Rd @@ -9,7 +9,9 @@ as_mixed_posteriors( parameters, conditional = NULL, conditional_rule = "AND", - force_plots = FALSE + force_plots = FALSE, + transform_scaled = FALSE, + n_prior_samples = 10000 ) } \arguments{ @@ -26,6 +28,15 @@ Either "AND" or "OR". Defaults to "AND".} \item{force_plots}{temporal argument allowing to generate conditional posterior samples suitable for prior and posterior plots. Only available when conditioning on a single parameter.} + +\item{transform_scaled}{whether to transform samples from standardized (scaled) to +original (unscaled) scale. When \code{TRUE}, both posterior and prior samples are +transformed, and the result can be directly passed to \link{plot_posterior} which will +automatically detect the transformation and use the transformed prior samples. +Requires a model fitted with \code{formula_scale_list}. Defaults to \code{FALSE}.} + +\item{n_prior_samples}{number of prior samples to generate when +\code{transform_scaled = TRUE}. Defaults to 10000.} } \value{ \code{as_mix_posteriors} returns a named list of mixed posterior diff --git a/man/geom_prior_list.Rd b/man/geom_prior_list.Rd index 0894e021..2b97aa80 100644 --- a/man/geom_prior_list.Rd +++ b/man/geom_prior_list.Rd @@ -12,12 +12,15 @@ geom_prior_list( n_points = 500, n_samples = 10000, force_samples = FALSE, + individual = FALSE, + show_figures = if (individual) 1 else NULL, transformation = NULL, transformation_arguments = NULL, transformation_settings = FALSE, rescale_x = FALSE, scale_y2 = NULL, prior_list_mu = NULL, + effect_direction = "positive", ... ) } @@ -47,6 +50,13 @@ analytically (or if samples are forced with \item{force_samples}{should prior be sampled instead of obtaining analytic solution whenever possible} +\item{individual}{should individual densities be returned +(e.g., in case of weightfunction)} + +\item{show_figures}{which figures should be returned in case of +multiple plots are generated. Useful when priors for the omega +parameter are plotted and \code{individual = TRUE}.} + \item{transformation}{transformation to be applied to the prior distribution. Either a character specifying one of the prepared transformations: @@ -73,6 +83,11 @@ weightfunction is plotted.} \item{prior_list_mu}{list of priors for the mu parameter required when plotting PET-PEESE} +\item{effect_direction}{direction of the effect for PET-PEESE +regression. Use \code{"positive"} (default) for +\code{mu + PET*se + PEESE*se^2} or \code{"negative"} for +\code{mu - PET*se - PEESE*se^2}.} + \item{...}{additional arguments} } \value{ diff --git a/man/lines_prior_list.Rd b/man/lines_prior_list.Rd index 33157a72..c8b358b3 100644 --- a/man/lines_prior_list.Rd +++ b/man/lines_prior_list.Rd @@ -12,12 +12,15 @@ lines_prior_list( n_points = 500, n_samples = 10000, force_samples = FALSE, + individual = FALSE, + show_figures = if (individual) 1 else NULL, transformation = NULL, transformation_arguments = NULL, transformation_settings = FALSE, rescale_x = FALSE, scale_y2 = NULL, prior_list_mu = NULL, + effect_direction = "positive", ... ) } @@ -47,6 +50,13 @@ analytically (or if samples are forced with \item{force_samples}{should prior be sampled instead of obtaining analytic solution whenever possible} +\item{individual}{should individual densities be returned +(e.g., in case of weightfunction)} + +\item{show_figures}{which figures should be returned in case of +multiple plots are generated. Useful when priors for the omega +parameter are plotted and \code{individual = TRUE}.} + \item{transformation}{transformation to be applied to the prior distribution. Either a character specifying one of the prepared transformations: @@ -73,6 +83,11 @@ weightfunction is plotted.} \item{prior_list_mu}{list of priors for the mu parameter required when plotting PET-PEESE} +\item{effect_direction}{direction of the effect for PET-PEESE +regression. Use \code{"positive"} (default) for +\code{mu + PET*se + PEESE*se^2} or \code{"negative"} for +\code{mu - PET*se - PEESE*se^2}.} + \item{...}{additional arguments} } \value{ diff --git a/man/parameter_names.Rd b/man/parameter_names.Rd index 57b34b85..7e1fcf25 100644 --- a/man/parameter_names.Rd +++ b/man/parameter_names.Rd @@ -10,7 +10,8 @@ format_parameter_names( parameters, formula_parameters = NULL, formula_random = NULL, - formula_prefix = TRUE + formula_prefix = TRUE, + formula_scale = NULL ) JAGS_parameter_names(parameters, formula_parameter = NULL) @@ -25,6 +26,10 @@ JAGS_parameter_names(parameters, formula_parameter = NULL) \item{formula_prefix}{whether the \code{formula_parameters} names should be kept. Defaults to \code{TRUE}.} +\item{formula_scale}{optional nested list containing scaling info. When provided, +intercepts from parameters with \code{log_intercept = TRUE} attribute will be +renamed to \code{exp(intercept)}.} + \item{formula_parameter}{a formula parameter prefix name} } \value{ diff --git a/man/plot_marginal.Rd b/man/plot_marginal.Rd index 3d01bf75..372e4030 100644 --- a/man/plot_marginal.Rd +++ b/man/plot_marginal.Rd @@ -28,7 +28,9 @@ parameter generated by \link{marginal_inference}.} \item{plot_type}{whether to use a base plot \code{"base"} or ggplot2 \code{"ggplot"} for plotting.} -\item{prior}{whether prior distribution should be added to the figure} +\item{prior}{whether prior distribution should be added to the figure. +When samples were prepared with \code{as_mixed_posteriors(..., transform_scaled = TRUE)}, +the transformed prior samples are automatically used.} \item{n_points}{number of equally spaced points in the \code{x_range} if \code{x_seq} is unspecified} diff --git a/man/plot_models.Rd b/man/plot_models.Rd index 2ba29a55..e35be9a3 100644 --- a/man/plot_models.Rd +++ b/man/plot_models.Rd @@ -27,7 +27,7 @@ likelihood estimated with bridge sampling \code{marglik} and prior model odds \code{prior_weights}} \item{samples}{samples from a posterior distribution for a -parameter generated by \link{mix_posteriors}.} +parameter generated by \link{mix_posteriors} or \link{as_mixed_posteriors}.} \item{inference}{object created by \link{ensemble_inference} function} @@ -37,7 +37,9 @@ PET-PEESE and weightfunction.} \item{plot_type}{whether to use a base plot \code{"base"} or ggplot2 \code{"ggplot"} for plotting.} -\item{prior}{whether prior distribution should be added to the figure} +\item{prior}{whether prior distribution should be added to the figure. +When samples were prepared with \code{as_mixed_posteriors(..., transform_scaled = TRUE)}, +the transformed prior samples are automatically used.} \item{conditional}{whether conditional models should be displayed} diff --git a/man/plot_posterior.Rd b/man/plot_posterior.Rd index c70338b5..a1280071 100644 --- a/man/plot_posterior.Rd +++ b/man/plot_posterior.Rd @@ -12,18 +12,21 @@ plot_posterior( n_points = 1000, n_samples = 10000, force_samples = FALSE, + individual = FALSE, + show_figures = NULL, transformation = NULL, transformation_arguments = NULL, transformation_settings = FALSE, rescale_x = FALSE, par_name = NULL, + effect_direction = "positive", dots_prior = list(), ... ) } \arguments{ \item{samples}{samples from a posterior distribution for a -parameter generated by \link{mix_posteriors}.} +parameter generated by \link{mix_posteriors} or \link{as_mixed_posteriors}.} \item{parameter}{parameter name to be plotted. Use \code{"PETPEESE"} for PET-PEESE plot with parameters \code{"PET"} and \code{"PEESE"}, @@ -33,7 +36,9 @@ parameters \code{"omega"}.} \item{plot_type}{whether to use a base plot \code{"base"} or ggplot2 \code{"ggplot"} for plotting.} -\item{prior}{whether prior distribution should be added to the figure} +\item{prior}{whether prior distribution should be added to the figure. +When samples were prepared with \code{as_mixed_posteriors(..., transform_scaled = TRUE)}, +the transformed prior samples are automatically used.} \item{n_points}{number of equally spaced points in the \code{x_range} if \code{x_seq} is unspecified} @@ -46,6 +51,13 @@ analytically (or if samples are forced with \item{force_samples}{should prior be sampled instead of obtaining analytic solution whenever possible} +\item{individual}{should individual densities be returned +(e.g., in case of weightfunction)} + +\item{show_figures}{which figures should be returned in case of +multiple plots are generated. Useful when priors for the omega +parameter are plotted and \code{individual = TRUE}.} + \item{transformation}{transformation to be applied to the prior distribution. Either a character specifying one of the prepared transformations: @@ -71,6 +83,11 @@ weightfunction is plotted.} specified. Only relevant if the prior corresponds to a mu parameter that needs to be transformed.} +\item{effect_direction}{direction of the effect for PET-PEESE +regression. Use \code{"positive"} (default) for +\code{mu + PET*se + PEESE*se^2} or \code{"negative"} for +\code{mu - PET*se - PEESE*se^2}.} + \item{dots_prior}{additional arguments for the prior distribution plot} \item{...}{additional arguments} @@ -82,6 +99,12 @@ an object of class 'ggplot' if plot_type is \code{plot_type = "ggplot"}. \description{ Plot samples from the mixed posterior distributions } +\details{ +When using scaled predictors (via \code{formula_scale_list} in \link{JAGS_fit}), +you can plot posteriors on the original (unscaled) scale by preparing samples with +\code{as_mixed_posteriors(..., transform_scaled = TRUE)}. The function automatically +detects this and uses the pre-computed transformed prior samples when \code{prior = TRUE}. +} \seealso{ \code{\link[=prior]{prior()}} \code{\link[=lines_prior_list]{lines_prior_list()}} \code{\link[=geom_prior_list]{geom_prior_list()}} } diff --git a/man/plot_prior_list.Rd b/man/plot_prior_list.Rd index 8b19630d..b3abbbf6 100644 --- a/man/plot_prior_list.Rd +++ b/man/plot_prior_list.Rd @@ -13,12 +13,15 @@ plot_prior_list( n_points = 500, n_samples = 10000, force_samples = FALSE, + individual = FALSE, + show_figures = if (individual) 1 else NULL, transformation = NULL, transformation_arguments = NULL, transformation_settings = FALSE, rescale_x = FALSE, par_name = NULL, prior_list_mu = NULL, + effect_direction = "positive", ... ) } @@ -51,6 +54,13 @@ analytically (or if samples are forced with \item{force_samples}{should prior be sampled instead of obtaining analytic solution whenever possible} +\item{individual}{should individual densities be returned +(e.g., in case of weightfunction)} + +\item{show_figures}{which figures should be returned in case of +multiple plots are generated. Useful when priors for the omega +parameter are plotted and \code{individual = TRUE}.} + \item{transformation}{transformation to be applied to the prior distribution. Either a character specifying one of the prepared transformations: @@ -79,6 +89,11 @@ parameter that needs to be transformed.} \item{prior_list_mu}{list of priors for the mu parameter required when plotting PET-PEESE} +\item{effect_direction}{direction of the effect for PET-PEESE +regression. Use \code{"positive"} (default) for +\code{mu + PET*se + PEESE*se^2} or \code{"negative"} for +\code{mu - PET*se - PEESE*se^2}.} + \item{...}{additional arguments} } \value{ diff --git a/man/posterior_extraction_helpers.Rd b/man/posterior_extraction_helpers.Rd new file mode 100644 index 00000000..92df5bf8 --- /dev/null +++ b/man/posterior_extraction_helpers.Rd @@ -0,0 +1,123 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/posterior-extraction.R +\name{posterior_extraction_helpers} +\alias{posterior_extraction_helpers} +\alias{.extract_posterior_samples} +\alias{.remove_auxiliary_parameters} +\alias{.remove_parameter_columns} +\alias{.filter_parameters} +\alias{.process_spike_and_slab} +\alias{.apply_parameter_transformations} +\alias{.transform_factor_contrasts} +\alias{.rename_factor_levels} +\title{Helper functions for extracting and formatting posterior distributions} +\usage{ +.extract_posterior_samples(fit, as_list = FALSE) + +.remove_auxiliary_parameters( + model_samples, + prior_list, + remove_parameters = NULL +) + +.remove_parameter_columns(model_samples, prior, par_name) + +.filter_parameters( + prior_list, + remove_parameters = NULL, + remove_formulas = NULL, + keep_parameters = NULL, + keep_formulas = NULL, + remove_spike_0 = TRUE +) + +.process_spike_and_slab( + model_samples, + prior_list, + par, + conditional = FALSE, + remove_inclusion = FALSE, + warnings = NULL +) + +.apply_parameter_transformations( + model_samples, + transformations, + prior_list, + transform_factors = FALSE +) + +.transform_factor_contrasts( + model_samples, + prior_list, + transform_factors = FALSE, + transformations = NULL +) + +.rename_factor_levels(model_samples, prior_list) +} +\arguments{ +\item{fit}{a JAGS model fit object} + +\item{as_list}{whether to return samples as mcmc.list (TRUE) or merged matrix (FALSE)} + +\item{model_samples}{matrix of posterior samples} + +\item{prior_list}{list of prior objects} + +\item{remove_parameters}{character vector of parameter names to remove, or TRUE to remove all non-formula parameters. +If "bias" is specified and the bias prior contains PET, PEESE, or weightfunction priors, +the corresponding parameters (PET, PEESE, omega) are also added to the removal list.} + +\item{prior}{prior object for the parameter} + +\item{par_name}{name of the parameter} + +\item{remove_formulas}{character vector of formula names whose parameters should be removed} + +\item{keep_parameters}{character vector of parameter names to keep (all others removed unless in keep_formulas). +If "bias" is specified and the bias prior contains PET, PEESE, or weightfunction priors, +the corresponding parameters (PET, PEESE, omega) are also added to the keep list.} + +\item{keep_formulas}{character vector of formula names whose parameters should be kept (all others removed unless in keep_parameters)} + +\item{remove_spike_0}{whether to remove spike at 0 priors} + +\item{par}{parameter name} + +\item{conditional}{whether to compute conditional summary} + +\item{remove_inclusion}{whether to remove inclusion indicators} + +\item{warnings}{character vector for collecting warnings} + +\item{transformations}{list of transformations to apply} + +\item{transform_factors}{whether to transform orthonormal/meandif to differences} +} +\value{ +matrix or mcmc.list of posterior samples + +list with cleaned model_samples and updated prior_list + +updated model_samples matrix + +list with filtered model_samples and prior_list + +list with updated model_samples, prior_list, and warnings + +updated model_samples matrix + +updated model_samples matrix + +updated model_samples matrix with renamed columns +} +\description{ +Internal helper functions to extract posterior samples from JAGS +fits and reformat them for further processing (summary tables, diagnostics, plots). +These functions consolidate common logic that was duplicated across +\code{runjags_estimates_table}, \code{.diagnostics_plot_data}, and plotting functions. + +Helper to remove all columns associated with a parameter +} +\keyword{internal} diff --git a/man/transform_prior_samples.Rd b/man/transform_prior_samples.Rd new file mode 100644 index 00000000..d2abcbe6 --- /dev/null +++ b/man/transform_prior_samples.Rd @@ -0,0 +1,56 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/JAGS-formula.R +\name{transform_prior_samples} +\alias{transform_prior_samples} +\title{Transform prior samples to original scale} +\usage{ +transform_prior_samples( + fit, + n_samples = 10000, + seed = NULL, + formula_scale = NULL +) +} +\arguments{ +\item{fit}{a fitted model object with \code{prior_list} and optionally +\code{formula_scale} attributes} + +\item{n_samples}{number of samples to generate (default: 10000)} + +\item{seed}{random seed for reproducibility (optional)} + +\item{formula_scale}{optional nested list containing standardization information. +If not provided, extracted from \code{fit} attribute.} +} +\value{ +A matrix of prior samples on the original (unscaled) scale, with +columns matching the structure of posterior samples. +} +\description{ +Generate prior samples and transform them using the same +matrix transformation as posterior samples. This is the correct approach for +visualizing priors on the original (unscaled) scale, especially for the intercept +which depends on contributions from multiple coefficient priors. +} +\details{ +When models use auto-scaling (standardizing predictors), the posterior +samples are on the standardized scale. To correctly visualize priors on the +original scale, we cannot simply apply a linear transformation to individual +priors because the intercept on the original scale is a weighted sum of +multiple priors: + +\deqn{\beta_0^{orig} = \beta_0^* - \sum_i \frac{\mu_i}{\sigma_i} \beta_i^*} + +This function generates samples from ALL priors simultaneously and applies +the same matrix transformation used for posterior samples, which correctly +handles the intercept and all other parameters. +} +\examples{ +# With a fitted model that used formula_scale: +# prior_samples <- transform_prior_samples(fit, n_samples = 10000) +# This can then be used with density() or for custom plotting + +} +\seealso{ +\code{\link[=transform_scale_samples]{transform_scale_samples()}} \code{\link[=plot_posterior]{plot_posterior()}} +} diff --git a/man/transform_scale_samples.Rd b/man/transform_scale_samples.Rd new file mode 100644 index 00000000..2b036981 --- /dev/null +++ b/man/transform_scale_samples.Rd @@ -0,0 +1,41 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/JAGS-formula.R +\name{transform_scale_samples} +\alias{transform_scale_samples} +\title{Transform standardized posterior samples back to original scale} +\usage{ +transform_scale_samples(fit, formula_scale = NULL) +} +\arguments{ +\item{fit}{a fitted model object with \code{formula_scale} attribute, or +a matrix of posterior samples} + +\item{formula_scale}{nested list containing standardization information keyed by +parameter name. Each parameter entry contains scaling info (mean and sd) for +each standardized predictor, e.g., \code{list(mu = list(mu_x1 = list(mean = 0, sd = 1)))}. +If \code{fit} is provided and has a \code{formula_scale} attribute, this will be used automatically.} +} +\value{ +\code{transform_scale_samples} returns posterior samples transformed +back to the original predictor scale. +} +\description{ +Transforms posterior samples from standardized continuous +predictors back to the original scale. This function is used when predictors +were standardized during model fitting via the \code{formula_scale} parameter. +} +\details{ +The function transforms regression coefficients and intercepts +to account for predictor standardization using a combinatorial approach that +correctly handles interactions of any order. + +For a k-way interaction between standardized predictors, the expansion of +\eqn{\prod_{i} (x_i - \mu_i)/\sigma_i} contributes to all lower-order terms. +The contribution to a target term T from a source term S (where T is a subset +of S's scaled components) is: +\deqn{(-1)^{|extra|} \cdot \prod_{i \in extra} \mu_i / \prod_{i \in S_{scaled}} \sigma_i} +where \eqn{extra = S_{scaled} \setminus T_{scaled}}. +} +\seealso{ +\code{\link[=JAGS_formula]{JAGS_formula()}} \code{\link[=JAGS_fit]{JAGS_fit()}} +} diff --git a/tests/results/JAGS-ensemble-tables/as_mixed_posteriors_complex_estimates.txt b/tests/results/JAGS-ensemble-tables/as_mixed_posteriors_complex_estimates.txt new file mode 100644 index 00000000..6dbd96be --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/as_mixed_posteriors_complex_estimates.txt @@ -0,0 +1,12 @@ + Mean Median 0.025 0.95 +(mu) intercept -0.103 -0.118 -0.222 0.000 +(mu) x_cont1 0.273 0.273 0.150 0.375 +(mu) x_fac2t[1] 0.009 0.000 0.000 0.079 +(mu) x_fac3t[1] 0.218 0.261 0.000 0.407 +(mu) x_fac3t[2] -0.010 0.000 -0.147 0.101 +sigma 0.803 0.803 0.736 0.859 +omega[0,0.025] 1.000 1.000 1.000 1.000 +omega[0.025,0.05] 0.871 1.000 0.100 1.000 +omega[0.05,0.975] 0.815 1.000 0.047 1.000 +omega[0.975,1] 0.895 1.000 0.091 1.000 +PET 0.105 0.000 0.000 0.812 diff --git a/tests/results/JAGS-ensemble-tables/as_mixed_posteriors_simple_estimates.txt b/tests/results/JAGS-ensemble-tables/as_mixed_posteriors_simple_estimates.txt new file mode 100644 index 00000000..d3ce3a66 --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/as_mixed_posteriors_simple_estimates.txt @@ -0,0 +1,7 @@ + Mean Median 0.025 0.95 +(mu) intercept -0.151 -0.150 -0.235 -0.078 +(mu) x_cont1 0.285 0.289 0.166 0.397 +(mu) x_fac2t[1] 0.064 0.066 -0.071 0.177 +(mu) x_fac3t[1] 0.236 0.240 0.102 0.350 +(mu) x_fac3t[2] -0.014 -0.016 -0.127 0.095 +sigma 0.796 0.796 0.739 0.850 diff --git a/tests/results/JAGS-ensemble-tables/complex_ensemble_diagnostics.txt b/tests/results/JAGS-ensemble-tables/complex_ensemble_diagnostics.txt new file mode 100644 index 00000000..1f29c1ab --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/complex_ensemble_diagnostics.txt @@ -0,0 +1,8 @@ + Model Prior (mu) x_cont1 Prior (mu) x_fac2t Prior (mu) x_fac3o max[error(MCMC)] max[error(MCMC)/SD] min(ESS) + 1 Normal(0, 1) 0.00317 0.039 674 + 2 Normal(0, 1) treatment contrast: Normal(0, 1) 0.00950 0.052 371 + 3 Normal(0, 1) orthonormal contrast: mNormal(0, 1) 0.00604 0.044 505 + max(R-hat) + 1.016 + 1.005 + 1.008 diff --git a/tests/results/JAGS-ensemble-tables/complex_ensemble_estimates.txt b/tests/results/JAGS-ensemble-tables/complex_ensemble_estimates.txt new file mode 100644 index 00000000..fd01ce04 --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/complex_ensemble_estimates.txt @@ -0,0 +1,5 @@ + Mean Median 0.025 0.95 +(mu) x_cont1 0.392 0.395 0.179 0.573 +(mu) x_fac2t[B] 0.004 0.000 -0.153 0.099 +(mu) x_fac3o[1] 0.010 0.000 0.000 0.000 +(mu) x_fac3o[2] 0.006 0.000 0.000 0.000 diff --git a/tests/results/JAGS-ensemble-tables/complex_ensemble_inference.txt b/tests/results/JAGS-ensemble-tables/complex_ensemble_inference.txt new file mode 100644 index 00000000..541ab2f6 --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/complex_ensemble_inference.txt @@ -0,0 +1,4 @@ + Models Prior prob. Post. prob. Inclusion BF +(mu) x_cont1 3/3 1.000 1.000 Inf +(mu) x_fac2t 1/3 0.333 0.153 0.361 +(mu) x_fac3o 1/3 0.333 0.054 0.115 diff --git a/tests/results/JAGS-ensemble-tables/complex_ensemble_summary.txt b/tests/results/JAGS-ensemble-tables/complex_ensemble_summary.txt new file mode 100644 index 00000000..cfae5795 --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/complex_ensemble_summary.txt @@ -0,0 +1,4 @@ + Model Prior (mu) x_cont1 Prior (mu) x_fac2t Prior (mu) x_fac3o Prior prob. log(marglik) Post. prob. Inclusion BF + 1 Normal(0, 1) 0.333 -146.01 0.793 7.649 + 2 Normal(0, 1) treatment contrast: Normal(0, 1) 0.333 -147.65 0.153 0.361 + 3 Normal(0, 1) orthonormal contrast: mNormal(0, 1) 0.333 -148.68 0.054 0.115 diff --git a/tests/results/JAGS-ensemble-tables/empty_ensemble_diagnostics.txt b/tests/results/JAGS-ensemble-tables/empty_ensemble_diagnostics.txt new file mode 100644 index 00000000..7332672b --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/empty_ensemble_diagnostics.txt @@ -0,0 +1,2 @@ +[1] Model max[error(MCMC)] max[error(MCMC)/SD] min(ESS) max(R-hat) +<0 rows> (or 0-length row.names) diff --git a/tests/results/JAGS-ensemble-tables/empty_ensemble_estimates.txt b/tests/results/JAGS-ensemble-tables/empty_ensemble_estimates.txt new file mode 100644 index 00000000..2d49d4b8 --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/empty_ensemble_estimates.txt @@ -0,0 +1,2 @@ +[1] Mean Median 0.025 0.95 +<0 rows> (or 0-length row.names) diff --git a/tests/results/JAGS-ensemble-tables/empty_ensemble_inference.txt b/tests/results/JAGS-ensemble-tables/empty_ensemble_inference.txt new file mode 100644 index 00000000..a6e4ded2 --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/empty_ensemble_inference.txt @@ -0,0 +1,2 @@ +[1] Models Prior prob. Post. prob. Inclusion BF +<0 rows> (or 0-length row.names) diff --git a/tests/results/JAGS-ensemble-tables/fixed_wf_estimates.txt b/tests/results/JAGS-ensemble-tables/fixed_wf_estimates.txt new file mode 100644 index 00000000..076bb964 --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/fixed_wf_estimates.txt @@ -0,0 +1,7 @@ + Mean Median 0.025 0.95 +m 0.149 0.149 -0.219 0.455 +omega[0,0.05] 0.788 1.000 0.300 1.000 +omega[0.05,0.1] 0.563 0.497 0.082 1.000 +omega[0.1,0.5] 0.775 0.936 0.082 1.000 +omega[0.5,0.9] 0.677 0.836 0.033 1.000 +omega[0.9,1] 0.465 0.300 0.033 1.000 diff --git a/tests/results/JAGS-ensemble-tables/fixed_wf_inference.txt b/tests/results/JAGS-ensemble-tables/fixed_wf_inference.txt new file mode 100644 index 00000000..37592689 --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/fixed_wf_inference.txt @@ -0,0 +1,3 @@ + Models Prior prob. Post. prob. Inclusion BF +m 4/4 1.000 1.000 Inf +omega 3/4 0.750 0.859 2.028 diff --git a/tests/results/JAGS-ensemble-tables/interaction_ensemble_estimates.txt b/tests/results/JAGS-ensemble-tables/interaction_ensemble_estimates.txt new file mode 100644 index 00000000..5c13b45b --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/interaction_ensemble_estimates.txt @@ -0,0 +1,6 @@ + Mean Median 0.025 0.95 +(mu) x_cont1 0.453 0.450 0.213 0.652 +(mu) x_fac3o[1] 0.022 0.022 -0.348 0.336 +(mu) x_fac3o[2] -0.096 -0.090 -0.461 0.207 +(mu) x_cont1:x_fac3o[1] -0.192 -0.181 -0.651 0.118 +(mu) x_cont1:x_fac3o[2] -0.053 -0.023 -0.434 0.260 diff --git a/tests/results/JAGS-ensemble-tables/interaction_ensemble_inference.txt b/tests/results/JAGS-ensemble-tables/interaction_ensemble_inference.txt new file mode 100644 index 00000000..468fdcba --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/interaction_ensemble_inference.txt @@ -0,0 +1,4 @@ + Models Prior prob. Post. prob. Inclusion BF +(mu) x_cont1 2/2 1.000 1.000 Inf +(mu) x_fac3o 2/2 1.000 1.000 Inf +(mu) x_cont1:x_fac3o 1/2 0.500 0.881 7.389 diff --git a/tests/results/JAGS-ensemble-tables/interaction_ensemble_summary.txt b/tests/results/JAGS-ensemble-tables/interaction_ensemble_summary.txt new file mode 100644 index 00000000..8ffa3c06 --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/interaction_ensemble_summary.txt @@ -0,0 +1,3 @@ + Model Prior (mu) x_cont1 Prior (mu) x_fac3o Prior (mu) x_cont1:x_fac3o Prior prob. log(marglik) Post. prob. Inclusion BF + 1 Normal(0, 1) orthonormal contrast: mNormal(0, 1) 0.500 -22.00 0.119 0.135 + 2 Normal(0, 1) orthonormal contrast: mNormal(0, 1) orthonormal contrast: mNormal(0, 1) 0.500 -20.00 0.881 7.389 diff --git a/tests/results/JAGS-ensemble-tables/simple_ensemble_diagnostics.txt b/tests/results/JAGS-ensemble-tables/simple_ensemble_diagnostics.txt new file mode 100644 index 00000000..2c34499a --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/simple_ensemble_diagnostics.txt @@ -0,0 +1,4 @@ + Model Prior m Prior omega max[error(MCMC)] max[error(MCMC)/SD] min(ESS) max(R-hat) + 1 Normal(0, 1) 0.01019 0.048 434 NA + 2 Normal(0, 0.5) omega[one-sided: .05] ~ CumDirichlet(1, 1) 0.01348 0.047 461 NA + 3 Normal(0, 0.3) omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1) 0.01061 0.045 500 NA diff --git a/tests/results/JAGS-ensemble-tables/simple_ensemble_diagnostics_trimmed.txt b/tests/results/JAGS-ensemble-tables/simple_ensemble_diagnostics_trimmed.txt new file mode 100644 index 00000000..d74066ab --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/simple_ensemble_diagnostics_trimmed.txt @@ -0,0 +1,4 @@ + Model max[error(MCMC)] max[error(MCMC)/SD] min(ESS) max(R-hat) + 1 0.01019 0.048 434 NA + 2 0.01348 0.047 461 NA + 3 0.01061 0.045 500 NA diff --git a/tests/results/JAGS-ensemble-tables/simple_ensemble_estimates.txt b/tests/results/JAGS-ensemble-tables/simple_ensemble_estimates.txt new file mode 100644 index 00000000..8d734cbc --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/simple_ensemble_estimates.txt @@ -0,0 +1,5 @@ + Mean Median 0.025 0.95 +m 0.153 0.153 -0.220 0.461 +omega[0,0.05] 1.000 1.000 1.000 1.000 +omega[0.05,0.5] 0.674 0.739 0.061 1.000 +omega[0.5,1] 0.535 0.497 0.023 1.000 diff --git a/tests/results/JAGS-ensemble-tables/simple_ensemble_inference.txt b/tests/results/JAGS-ensemble-tables/simple_ensemble_inference.txt new file mode 100644 index 00000000..0ab5fa69 --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/simple_ensemble_inference.txt @@ -0,0 +1,3 @@ + Models Prior prob. Post. prob. Inclusion BF +m 3/3 1.000 1.000 Inf +omega 2/3 0.667 0.797 1.968 diff --git a/tests/results/JAGS-ensemble-tables/simple_ensemble_summary.txt b/tests/results/JAGS-ensemble-tables/simple_ensemble_summary.txt new file mode 100644 index 00000000..faa7e9b3 --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/simple_ensemble_summary.txt @@ -0,0 +1,4 @@ + Model Prior m Prior omega Prior prob. log(marglik) Post. prob. Inclusion BF + 1 Normal(0, 1) 0.333 -1.11 0.203 0.508 + 2 Normal(0, 0.5) omega[one-sided: .05] ~ CumDirichlet(1, 1) 0.333 -0.54 0.356 1.107 + 3 Normal(0, 0.3) omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1) 0.333 -0.33 0.441 1.578 diff --git a/tests/results/JAGS-ensemble-tables/simple_interpretation.txt b/tests/results/JAGS-ensemble-tables/simple_interpretation.txt new file mode 100644 index 00000000..85e726b1 --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/simple_interpretation.txt @@ -0,0 +1 @@ +Test found strong evidence in favor of the effect, BF_10 = Inf, with mean model-averaged estimate y = 0.153, 95% CI [-0.220, 0.525]. diff --git a/tests/results/JAGS-ensemble-tables/simple_interpretation2.txt b/tests/results/JAGS-ensemble-tables/simple_interpretation2.txt new file mode 100644 index 00000000..e4c080d1 --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/simple_interpretation2.txt @@ -0,0 +1 @@ +Test2 found moderate evidence against the effect, BF_10 = 0.200, with mean conditional estimate y = 0.153 mm, 95% CI [-0.220, 0.525]. Test2 found weak evidence in favor of the bias, BF_pb = 1.97. diff --git a/tests/results/JAGS-ensemble-tables/simple_ma_estimates.txt b/tests/results/JAGS-ensemble-tables/simple_ma_estimates.txt new file mode 100644 index 00000000..82fde6bd --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/simple_ma_estimates.txt @@ -0,0 +1,3 @@ + Mean Median 0.025 0.95 +m 0.004 0.000 0.000 0.024 +s 0.424 0.422 0.353 0.503 diff --git a/tests/results/JAGS-ensemble-tables/simple_ma_inference.txt b/tests/results/JAGS-ensemble-tables/simple_ma_inference.txt new file mode 100644 index 00000000..cf2b1d80 --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/simple_ma_inference.txt @@ -0,0 +1,3 @@ + Models Prior prob. Post. prob. Inclusion BF +m 1/2 0.500 0.079 0.085 +s 2/2 1.000 1.000 Inf diff --git a/tests/results/JAGS-ensemble-tables/spike_factors_estimates.txt b/tests/results/JAGS-ensemble-tables/spike_factors_estimates.txt new file mode 100644 index 00000000..835a8f5a --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/spike_factors_estimates.txt @@ -0,0 +1,3 @@ + Mean Median 0.025 0.95 +(mu) x_fac3md[1] 0.001 0.000 0.000 0.000 +(mu) x_fac3md[2] 0.004 0.000 0.000 0.000 diff --git a/tests/results/JAGS-ensemble-tables/spike_factors_inference.txt b/tests/results/JAGS-ensemble-tables/spike_factors_inference.txt new file mode 100644 index 00000000..dc65de0c --- /dev/null +++ b/tests/results/JAGS-ensemble-tables/spike_factors_inference.txt @@ -0,0 +1,2 @@ + Models Prior prob. Post. prob. Inclusion BF +(mu) x_fac3md 1/2 0.500 0.027 0.027 diff --git a/tests/results/JAGS-fit-edge-cases/fit_jasp.txt b/tests/results/JAGS-fit-edge-cases/fit_jasp.txt new file mode 100644 index 00000000..0882ad28 --- /dev/null +++ b/tests/results/JAGS-fit-edge-cases/fit_jasp.txt @@ -0,0 +1 @@ +Test: Adapting and burnin the model(1),.Test: Sampling the model(5),.....,JAGS model with 101 samples (adapt+burnin = 100),,Full summary statistics have not been pre-calculated - use either the summary method or add.summary to calculate summary statistics, diff --git a/tests/results/JAGS-fit/JAGS_add_priors_factor.txt b/tests/results/JAGS-fit/JAGS_add_priors_factor.txt new file mode 100644 index 00000000..675529ff --- /dev/null +++ b/tests/results/JAGS-fit/JAGS_add_priors_factor.txt @@ -0,0 +1,14 @@ +model{ + prior_par1_p1 = rep(0,2) +for(i in 1:2){ + prior_par2_p1[i,i] <- 1 + for(j in 1:(i-1)){ + prior_par2_p1[i,j] <- 0 + } + for (j in (i+1):2){ + prior_par2_p1[i,j] <- 0 + } +} +p1 ~ dmnorm(prior_par1_p1,prior_par2_p1) + +} diff --git a/tests/results/JAGS-fit/JAGS_add_priors_peese_mixture.txt b/tests/results/JAGS-fit/JAGS_add_priors_peese_mixture.txt new file mode 100644 index 00000000..ff93da58 --- /dev/null +++ b/tests/results/JAGS-fit/JAGS_add_priors_peese_mixture.txt @@ -0,0 +1,6 @@ +model{ + bias_indicator ~ dcat(c(1, 1)) + PEESE_1 ~ dnorm(0,1)T(0,) + PEESE = PEESE_1 * (bias_indicator == 2) + +} diff --git a/tests/results/JAGS-fit/JAGS_add_priors_pet_mixture.txt b/tests/results/JAGS-fit/JAGS_add_priors_pet_mixture.txt new file mode 100644 index 00000000..e9d1c105 --- /dev/null +++ b/tests/results/JAGS-fit/JAGS_add_priors_pet_mixture.txt @@ -0,0 +1,6 @@ +model{ + bias_indicator ~ dcat(c(1, 1)) + PET_1 ~ dnorm(0,1)T(0,) + PET = PET_1 * (bias_indicator == 2) + +} diff --git a/tests/results/JAGS-fit/JAGS_add_priors_point.txt b/tests/results/JAGS-fit/JAGS_add_priors_point.txt new file mode 100644 index 00000000..ce1c63f3 --- /dev/null +++ b/tests/results/JAGS-fit/JAGS_add_priors_point.txt @@ -0,0 +1,4 @@ +model{ + mu = 0 + +} diff --git a/tests/results/JAGS-fit/JAGS_add_priors_simple.txt b/tests/results/JAGS-fit/JAGS_add_priors_simple.txt new file mode 100644 index 00000000..2a3fead0 --- /dev/null +++ b/tests/results/JAGS-fit/JAGS_add_priors_simple.txt @@ -0,0 +1,5 @@ +model{ + mu ~ dnorm(0,1) + sigma ~ dgamma(2,1) + +} diff --git a/tests/results/JAGS-fit/JAGS_add_priors_truncated.txt b/tests/results/JAGS-fit/JAGS_add_priors_truncated.txt new file mode 100644 index 00000000..c0b3005c --- /dev/null +++ b/tests/results/JAGS-fit/JAGS_add_priors_truncated.txt @@ -0,0 +1,4 @@ +model{ + mu ~ dnorm(0,1)T(0,) + +} diff --git a/tests/results/JAGS-fit/JAGS_add_priors_weightfunction.txt b/tests/results/JAGS-fit/JAGS_add_priors_weightfunction.txt new file mode 100644 index 00000000..f2abcf7e --- /dev/null +++ b/tests/results/JAGS-fit/JAGS_add_priors_weightfunction.txt @@ -0,0 +1,9 @@ +model{ + eta[1] ~ dgamma(1, 1) +eta[2] ~ dgamma(1, 1) +for(j in 1:2){ + std_eta[j] = eta[j] / sum(eta) + omega[j] = sum(std_eta[1:j]) +} + +} diff --git a/tests/results/JAGS-fit/JAGS_to_monitor_factor.txt b/tests/results/JAGS-fit/JAGS_to_monitor_factor.txt new file mode 100644 index 00000000..171d04eb --- /dev/null +++ b/tests/results/JAGS-fit/JAGS_to_monitor_factor.txt @@ -0,0 +1 @@ +p1 diff --git a/tests/results/JAGS-fit/JAGS_to_monitor_point.txt b/tests/results/JAGS-fit/JAGS_to_monitor_point.txt new file mode 100644 index 00000000..d3e80ff8 --- /dev/null +++ b/tests/results/JAGS-fit/JAGS_to_monitor_point.txt @@ -0,0 +1 @@ +mu, sigma diff --git a/tests/results/JAGS-fit/JAGS_to_monitor_simple.txt b/tests/results/JAGS-fit/JAGS_to_monitor_simple.txt new file mode 100644 index 00000000..bd3e5ce6 --- /dev/null +++ b/tests/results/JAGS-fit/JAGS_to_monitor_simple.txt @@ -0,0 +1 @@ +mu,sigma diff --git a/tests/results/JAGS-fit/runjags_estimates_param_m.txt b/tests/results/JAGS-fit/runjags_estimates_param_m.txt new file mode 100644 index 00000000..89e8cca4 --- /dev/null +++ b/tests/results/JAGS-fit/runjags_estimates_param_m.txt @@ -0,0 +1,2 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +s 0.425 0.046 0.348 0.421 0.525 0.00199 0.043 540 1.005 diff --git a/tests/results/JAGS-fit/runjags_estimates_simple.txt b/tests/results/JAGS-fit/runjags_estimates_simple.txt new file mode 100644 index 00000000..a19f99d1 --- /dev/null +++ b/tests/results/JAGS-fit/runjags_estimates_simple.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +m 0.047 0.059 -0.070 0.047 0.161 0.00185 0.032 1000 1.003 +s 0.425 0.046 0.348 0.421 0.525 0.00199 0.043 540 1.005 diff --git a/tests/results/JAGS-marginal-distributions/marginal_estimates_table_model_avg.txt b/tests/results/JAGS-marginal-distributions/marginal_estimates_table_model_avg.txt new file mode 100644 index 00000000..a34abf23 --- /dev/null +++ b/tests/results/JAGS-marginal-distributions/marginal_estimates_table_model_avg.txt @@ -0,0 +1,37 @@ + Mean Median 0.025 0.95 Inclusion BF +(mu) intercept 0.614 0.614 0.515 0.688 Inf +(mu) x_cont1[-1SD] 0.430 0.430 0.296 0.537 Inf +(mu) x_cont1[0SD] 0.614 0.614 0.515 0.688 Inf +(mu) x_cont1[1SD] 0.798 0.799 0.676 0.896 Inf +(mu) x_fac2t[A] 0.611 0.613 0.500 0.699 Inf +(mu) x_fac2t[B] 0.621 0.618 0.519 0.708 Inf +(mu) x_fac3md[A] 0.765 0.768 0.600 0.896 Inf +(mu) x_fac3md[B] 0.517 0.519 0.365 0.639 Inf +(mu) x_fac3md[C] 0.550 0.548 0.404 0.673 Inf +(mu) x_cont1:x_fac3md[-1SD, A] 0.550 0.553 0.326 0.743 Inf +(mu) x_cont1:x_fac3md[0SD, A] 0.765 0.768 0.600 0.896 Inf +(mu) x_cont1:x_fac3md[1SD, A] 0.980 0.982 0.770 1.143 Inf +(mu) x_cont1:x_fac3md[-1SD, B] 0.371 0.373 0.136 0.553 Inf +(mu) x_cont1:x_fac3md[0SD, B] 0.517 0.519 0.365 0.639 Inf +(mu) x_cont1:x_fac3md[1SD, B] 0.664 0.666 0.453 0.826 Inf +(mu) x_cont1:x_fac3md[-1SD, C] 0.374 0.373 0.183 0.535 Inf +(mu) x_cont1:x_fac3md[0SD, C] 0.550 0.548 0.404 0.673 Inf +(mu) x_cont1:x_fac3md[1SD, C] 0.727 0.727 0.529 0.901 Inf +mu_intercept: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1[-1SD]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1[0SD]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1[1SD]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_fac2t[A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_fac2t[B]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_fac3md[A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_fac3md[B]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_fac3md[C]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1__xXx__x_fac3md[-1SD, A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1__xXx__x_fac3md[0SD, A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1__xXx__x_fac3md[1SD, A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1__xXx__x_fac3md[-1SD, B]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1__xXx__x_fac3md[0SD, B]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1__xXx__x_fac3md[1SD, B]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1__xXx__x_fac3md[-1SD, C]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1__xXx__x_fac3md[0SD, C]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1__xXx__x_fac3md[1SD, C]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. diff --git a/tests/results/JAGS-marginal-distributions/marginal_estimates_table_spike_slab.txt b/tests/results/JAGS-marginal-distributions/marginal_estimates_table_spike_slab.txt new file mode 100644 index 00000000..ad5ce21e --- /dev/null +++ b/tests/results/JAGS-marginal-distributions/marginal_estimates_table_spike_slab.txt @@ -0,0 +1,37 @@ + Mean Median 0.025 0.95 Inclusion BF +(mu) intercept 0.618 0.618 0.545 0.678 Inf +(mu) x_cont1[-1SD] 0.436 0.434 0.323 0.535 Inf +(mu) x_cont1[0SD] 0.618 0.618 0.545 0.678 Inf +(mu) x_cont1[1SD] 0.799 0.799 0.696 0.885 Inf +(mu) x_fac2t[A] 0.618 0.618 0.545 0.678 Inf +(mu) x_fac2t[B] 0.618 0.618 0.546 0.680 Inf +(mu) x_fac3md[A] 0.781 0.780 0.656 0.891 Inf +(mu) x_fac3md[B] 0.519 0.519 0.390 0.621 Inf +(mu) x_fac3md[C] 0.553 0.553 0.425 0.659 Inf +(mu) x_cont1:x_fac3md[-1SD, A] 0.593 0.594 0.406 0.744 Inf +(mu) x_cont1:x_fac3md[0SD, A] 0.776 0.778 0.632 0.890 Inf +(mu) x_cont1:x_fac3md[1SD, A] 0.959 0.964 0.812 1.075 Inf +(mu) x_cont1:x_fac3md[-1SD, B] 0.345 0.342 0.178 0.487 Inf +(mu) x_cont1:x_fac3md[0SD, B] 0.522 0.521 0.391 0.625 Inf +(mu) x_cont1:x_fac3md[1SD, B] 0.698 0.701 0.543 0.822 Inf +(mu) x_cont1:x_fac3md[-1SD, C] 0.375 0.374 0.215 0.503 Inf +(mu) x_cont1:x_fac3md[0SD, C] 0.555 0.555 0.426 0.662 Inf +(mu) x_cont1:x_fac3md[1SD, C] 0.735 0.736 0.577 0.865 Inf +mu_intercept: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1[-1SD]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1[0SD]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1[1SD]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_fac2t[A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_fac2t[B]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_fac3md[A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_fac3md[B]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_fac3md[C]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1__xXx__x_fac3md[-1SD, A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1__xXx__x_fac3md[0SD, A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1__xXx__x_fac3md[1SD, A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1__xXx__x_fac3md[-1SD, B]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1__xXx__x_fac3md[0SD, B]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1__xXx__x_fac3md[1SD, B]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1__xXx__x_fac3md[-1SD, C]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1__xXx__x_fac3md[0SD, C]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. +mu_x_cont1__xXx__x_fac3md[1SD, C]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated. diff --git a/tests/results/JAGS-summary-tables/.txt b/tests/results/JAGS-summary-tables/.txt new file mode 100644 index 00000000..ceaffe23 --- /dev/null +++ b/tests/results/JAGS-summary-tables/.txt @@ -0,0 +1,10 @@ + Mean SD lCI Median uCI error(MCMC) error(MCMC)/SD ESS R-hat +sigma (inclusion: normal) 0.422 NA NA NA NA NA NA NA NA +sigma (inclusion: lognormal) 0.578 NA NA NA NA NA NA NA NA +sigma 0.803 0.034 0.736 0.803 0.876 0.00263 0.078 166 NA +bias (inclusion) 0.476 NA NA NA NA NA NA NA NA +PET 0.105 0.332 0.000 0.000 1.346 0.01484 0.045 500 NA +omega[0,0.025] 1.000 0.000 1.000 1.000 1.000 NA NA NA NA +omega[0.025,0.05] 0.871 0.249 0.100 1.000 1.000 0.01112 0.045 500 NA +omega[0.05,0.975] 0.815 0.314 0.047 1.000 1.000 0.01406 0.045 500 NA +omega[0.975,1] 0.895 0.261 0.091 1.000 1.000 0.01169 0.045 500 NA diff --git a/tests/results/JAGS-summary-tables/advanced_conditional.txt b/tests/results/JAGS-summary-tables/advanced_conditional.txt new file mode 100644 index 00000000..13e8edcd --- /dev/null +++ b/tests/results/JAGS-summary-tables/advanced_conditional.txt @@ -0,0 +1,6 @@ + Mean SD 0.025 0.5 0.975 +(mu) intercept 0.035 0.104 -0.178 0.040 0.243 +(mu) x_cont1 0.361 0.123 0.124 0.365 0.587 +(mu) x_cont2 -0.029 0.109 -0.241 -0.028 0.190 +(mu) x_cont1:x_cont2 -0.391 0.149 -0.685 -0.390 -0.104 +sigma 1.040 0.077 0.905 1.033 1.203 diff --git a/tests/results/JAGS-summary-tables/advanced_custom_transform.txt b/tests/results/JAGS-summary-tables/advanced_custom_transform.txt new file mode 100644 index 00000000..620b4003 --- /dev/null +++ b/tests/results/JAGS-summary-tables/advanced_custom_transform.txt @@ -0,0 +1,2 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +p1[2] 0.497 0.291 0.022 0.491 0.973 0.00921 0.032 1000 1.000 diff --git a/tests/results/JAGS-summary-tables/advanced_factor_treatment.txt b/tests/results/JAGS-summary-tables/advanced_factor_treatment.txt new file mode 100644 index 00000000..620b4003 --- /dev/null +++ b/tests/results/JAGS-summary-tables/advanced_factor_treatment.txt @@ -0,0 +1,2 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +p1[2] 0.497 0.291 0.022 0.491 0.973 0.00921 0.032 1000 1.000 diff --git a/tests/results/JAGS-summary-tables/advanced_formula_prefix_false.txt b/tests/results/JAGS-summary-tables/advanced_formula_prefix_false.txt new file mode 100644 index 00000000..b208fc27 --- /dev/null +++ b/tests/results/JAGS-summary-tables/advanced_formula_prefix_false.txt @@ -0,0 +1,6 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +intercept 0.035 0.104 -0.178 0.040 0.243 0.00350 0.034 902 1.001 +x_cont1 0.361 0.123 0.124 0.365 0.587 0.00384 0.031 1027 1.000 +x_cont2 -0.029 0.109 -0.241 -0.028 0.190 0.00357 0.033 931 1.000 +x_cont1:x_cont2 -0.391 0.149 -0.685 -0.390 -0.104 0.00495 0.033 914 1.003 +sigma 1.040 0.077 0.905 1.033 1.203 0.00364 0.047 451 1.000 diff --git a/tests/results/JAGS-summary-tables/advanced_formula_prefix_true.txt b/tests/results/JAGS-summary-tables/advanced_formula_prefix_true.txt new file mode 100644 index 00000000..29fb99da --- /dev/null +++ b/tests/results/JAGS-summary-tables/advanced_formula_prefix_true.txt @@ -0,0 +1,6 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept 0.035 0.104 -0.178 0.040 0.243 0.00350 0.034 902 1.001 +(mu) x_cont1 0.361 0.123 0.124 0.365 0.587 0.00384 0.031 1027 1.000 +(mu) x_cont2 -0.029 0.109 -0.241 -0.028 0.190 0.00357 0.033 931 1.000 +(mu) x_cont1:x_cont2 -0.391 0.149 -0.685 -0.390 -0.104 0.00495 0.033 914 1.003 +sigma 1.040 0.077 0.905 1.033 1.203 0.00364 0.047 451 1.000 diff --git a/tests/results/JAGS-summary-tables/advanced_orthonormal_transform.txt b/tests/results/JAGS-summary-tables/advanced_orthonormal_transform.txt new file mode 100644 index 00000000..fd09626b --- /dev/null +++ b/tests/results/JAGS-summary-tables/advanced_orthonormal_transform.txt @@ -0,0 +1,4 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +p1 [dif: 1] 0.041 0.818 -1.631 0.060 1.605 0.02589 0.032 1000 1.001 +p1 [dif: 2] -0.033 0.796 -1.612 -0.029 1.527 0.02517 0.032 1000 0.999 +p1 [dif: 3] -0.008 0.811 -1.550 -0.009 1.564 0.02565 0.032 1000 1.002 diff --git a/tests/results/JAGS-summary-tables/advanced_orthonormal_transform2.txt b/tests/results/JAGS-summary-tables/advanced_orthonormal_transform2.txt new file mode 100644 index 00000000..8413e44e --- /dev/null +++ b/tests/results/JAGS-summary-tables/advanced_orthonormal_transform2.txt @@ -0,0 +1,4 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +p1 [dif: 1] 1.437 1.295 0.196 1.062 4.979 0.04097 0.032 1000 1.009 +p1 [dif: 2] 1.326 1.230 0.199 0.972 4.605 0.03891 0.032 1000 1.000 +p1 [dif: 3] 1.376 1.289 0.212 0.991 4.776 0.04076 0.032 1000 1.002 diff --git a/tests/results/JAGS-summary-tables/advanced_remove_inclusion.txt b/tests/results/JAGS-summary-tables/advanced_remove_inclusion.txt new file mode 100644 index 00000000..ce43bdf8 --- /dev/null +++ b/tests/results/JAGS-summary-tables/advanced_remove_inclusion.txt @@ -0,0 +1,4 @@ + Mean SD 0.025 0.5 0.975 +beta [dif: 1] 0.008 0.811 -1.606 0.010 1.632 +beta [dif: 2] -0.049 0.842 -1.669 -0.084 1.618 +beta [dif: 3] 0.041 0.823 -1.482 0.029 1.751 diff --git a/tests/results/JAGS-summary-tables/advanced_spike_slab_estimates.txt b/tests/results/JAGS-summary-tables/advanced_spike_slab_estimates.txt new file mode 100644 index 00000000..0ea7f34d --- /dev/null +++ b/tests/results/JAGS-summary-tables/advanced_spike_slab_estimates.txt @@ -0,0 +1,4 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +beta (inclusion) 0.527 NA NA NA NA NA NA NA NA +beta[1] 0.034 0.747 -1.569 0.000 1.759 0.02357 0.032 1000 1.006 +beta[2] 0.005 0.721 -1.680 0.000 1.639 0.02281 0.032 1000 1.000 diff --git a/tests/results/JAGS-summary-tables/advanced_spike_slab_inference.txt b/tests/results/JAGS-summary-tables/advanced_spike_slab_inference.txt new file mode 100644 index 00000000..a335067e --- /dev/null +++ b/tests/results/JAGS-summary-tables/advanced_spike_slab_inference.txt @@ -0,0 +1,2 @@ + Prior prob. Post. prob. Inclusion BF +beta 0.500 0.527 1.114 diff --git a/tests/results/JAGS-summary-tables/advanced_transform.txt b/tests/results/JAGS-summary-tables/advanced_transform.txt new file mode 100644 index 00000000..f253e8f9 --- /dev/null +++ b/tests/results/JAGS-summary-tables/advanced_transform.txt @@ -0,0 +1,6 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept 1.041 0.109 0.837 1.040 1.276 0.00365 0.034 901 1.001 +(mu) x_cont1 0.361 0.123 0.124 0.365 0.587 0.00384 0.031 1027 1.000 +(mu) x_cont2 -0.029 0.109 -0.241 -0.028 0.190 0.00357 0.033 931 1.000 +(mu) x_cont1:x_cont2 -0.391 0.149 -0.685 -0.390 -0.104 0.00495 0.033 914 1.003 +sigma 1.040 0.077 0.905 1.033 1.203 0.00364 0.047 451 1.000 diff --git a/tests/results/JAGS-summary-tables/advanced_unconditional.txt b/tests/results/JAGS-summary-tables/advanced_unconditional.txt new file mode 100644 index 00000000..29fb99da --- /dev/null +++ b/tests/results/JAGS-summary-tables/advanced_unconditional.txt @@ -0,0 +1,6 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept 0.035 0.104 -0.178 0.040 0.243 0.00350 0.034 902 1.001 +(mu) x_cont1 0.361 0.123 0.124 0.365 0.587 0.00384 0.031 1027 1.000 +(mu) x_cont2 -0.029 0.109 -0.241 -0.028 0.190 0.00357 0.033 931 1.000 +(mu) x_cont1:x_cont2 -0.391 0.149 -0.685 -0.390 -0.104 0.00495 0.033 914 1.003 +sigma 1.040 0.077 0.905 1.033 1.203 0.00364 0.047 451 1.000 diff --git a/tests/results/JAGS-summary-tables/empty_runjags_estimates.txt b/tests/results/JAGS-summary-tables/empty_runjags_estimates.txt new file mode 100644 index 00000000..ef02455d --- /dev/null +++ b/tests/results/JAGS-summary-tables/empty_runjags_estimates.txt @@ -0,0 +1,2 @@ +[1] Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +<0 rows> (or 0-length row.names) diff --git a/tests/results/JAGS-summary-tables/fit_add_parameters_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_add_parameters_runjags_estimates.txt new file mode 100644 index 00000000..dfadb67d --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_add_parameters_runjags_estimates.txt @@ -0,0 +1,4 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +m 0.168 0.209 -0.252 0.164 0.570 0.00854 0.041 600 1.004 +s 0.951 0.157 0.696 0.925 1.318 0.00861 0.055 332 1.002 +g 0.027 0.980 -1.774 -0.001 1.967 0.04004 0.041 600 1.001 diff --git a/tests/results/JAGS-summary-tables/fit_autofit_error_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_autofit_error_runjags_estimates.txt new file mode 100644 index 00000000..b0f9afd0 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_autofit_error_runjags_estimates.txt @@ -0,0 +1,2 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +m -0.034 1.003 -1.932 -0.060 2.012 0.04937 0.049 452 1.001 diff --git a/tests/results/JAGS-summary-tables/fit_autofit_ess_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_autofit_ess_runjags_estimates.txt new file mode 100644 index 00000000..a0bd9b43 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_autofit_ess_runjags_estimates.txt @@ -0,0 +1,2 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +m 0.025 1.002 -1.701 -0.012 2.039 0.06445 0.064 249 0.998 diff --git a/tests/results/JAGS-summary-tables/fit_complex_bias_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_complex_bias_runjags_estimates.txt new file mode 100644 index 00000000..63f32c75 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_complex_bias_runjags_estimates.txt @@ -0,0 +1,9 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +mu 1.019 0.588 0.243 0.891 2.460 0.02375 0.040 613 NA +bias (inclusion) 0.526 NA NA NA NA NA NA NA NA +PET 0.097 0.328 0.000 0.000 1.229 0.01465 0.045 500 NA +PEESE 0.166 0.583 0.000 0.000 2.134 0.02609 0.045 500 NA +omega[0,0.025] 1.000 0.000 1.000 1.000 1.000 NA NA NA NA +omega[0.025,0.05] 0.887 0.229 0.127 1.000 1.000 0.01024 0.045 500 NA +omega[0.05,0.975] 0.836 0.297 0.070 1.000 1.000 0.01328 0.045 500 NA +omega[0.975,1] 0.896 0.256 0.099 1.000 1.000 0.01146 0.045 500 NA diff --git a/tests/results/JAGS-summary-tables/fit_complex_mixed_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_complex_mixed_runjags_estimates.txt new file mode 100644 index 00000000..24306651 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_complex_mixed_runjags_estimates.txt @@ -0,0 +1,19 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept (inclusion) 0.738 NA NA NA NA NA NA NA NA +(mu) intercept -0.103 0.075 -0.222 -0.118 0.000 0.01088 0.146 47 NA +(mu) x_cont1 (inclusion) 1.000 NA NA NA NA NA NA NA NA +(mu) x_cont1 0.273 0.063 0.150 0.273 0.398 0.00330 0.053 361 NA +(mu) x_fac2t (inclusion) 0.128 NA NA NA NA NA NA NA NA +(mu) x_fac2t 0.009 0.032 0.000 0.000 0.129 0.00217 0.069 211 NA +(mu) x_fac3t (inclusion) 0.732 NA NA NA NA NA NA NA NA +(mu) x_fac3t[1] 0.218 0.147 0.000 0.261 0.427 0.04047 0.275 13 NA +(mu) x_fac3t[2] -0.010 0.065 -0.147 0.000 0.129 0.00292 0.045 500 NA +sigma (inclusion: normal) 0.422 NA NA NA NA NA NA NA NA +sigma (inclusion: lognormal) 0.578 NA NA NA NA NA NA NA NA +sigma 0.803 0.034 0.736 0.803 0.876 0.00263 0.078 166 NA +bias (inclusion) 0.476 NA NA NA NA NA NA NA NA +PET 0.105 0.332 0.000 0.000 1.346 0.01484 0.045 500 NA +omega[0,0.025] 1.000 0.000 1.000 1.000 1.000 NA NA NA NA +omega[0.025,0.05] 0.871 0.249 0.100 1.000 1.000 0.01112 0.045 500 NA +omega[0.05,0.975] 0.815 0.314 0.047 1.000 1.000 0.01406 0.045 500 NA +omega[0.975,1] 0.895 0.261 0.091 1.000 1.000 0.01169 0.045 500 NA diff --git a/tests/results/JAGS-summary-tables/fit_dual_param_regression_model_summary.txt b/tests/results/JAGS-summary-tables/fit_dual_param_regression_model_summary.txt new file mode 100644 index 00000000..c45e7552 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_dual_param_regression_model_summary.txt @@ -0,0 +1,6 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 (mu) intercept ~ Normal(0, 2) + log(marglik) -180.58 (mu) x_mu ~ Normal(0, 1) + Post. prob. 1.000 (log_sigma) intercept ~ Lognormal(0, 0.5) + Inclusion BF Inf (log_sigma) x_sigma ~ Normal(0, 0.5) diff --git a/tests/results/JAGS-summary-tables/fit_dual_param_regression_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_dual_param_regression_runjags_estimates.txt new file mode 100644 index 00000000..07bea704 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_dual_param_regression_runjags_estimates.txt @@ -0,0 +1,5 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept 2.498 0.008 2.482 2.498 2.514 0.00026 0.032 1000 1.009 +(mu) x_mu 0.631 0.008 0.617 0.631 0.646 0.00025 0.033 925 1.000 +(log_sigma) intercept 0.285 0.006 0.273 0.285 0.298 0.00025 0.039 669 1.003 +(log_sigma) x_sigma -0.324 0.024 -0.370 -0.325 -0.280 0.00106 0.045 524 1.001 diff --git a/tests/results/JAGS-summary-tables/fit_expression_mixture_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_expression_mixture_runjags_estimates.txt new file mode 100644 index 00000000..590659ea --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_expression_mixture_runjags_estimates.txt @@ -0,0 +1,4 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +x (inclusion) 0.466 NA NA NA NA NA NA NA NA +x 3457.005 110604.018 -62.527 -0.100 65.334 3497.63310 0.032 1000 1.291 +x_sigma 2009.580 44689.794 0.233 2.394 1843.054 1809.67684 0.040 804 1.287 diff --git a/tests/results/JAGS-summary-tables/fit_expression_simple_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_expression_simple_runjags_estimates.txt new file mode 100644 index 00000000..dfcc7e4d --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_expression_simple_runjags_estimates.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +x 6283.001 208274.534 -129.928 -0.009 144.604 5394.96951 0.026 1307 1.192 +x_sigma 14992.279 321938.407 0.235 2.202 508.311 10181.93310 0.032 1000 1.236 diff --git a/tests/results/JAGS-summary-tables/fit_expression_spike_slab_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_expression_spike_slab_runjags_estimates.txt new file mode 100644 index 00000000..97710708 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_expression_spike_slab_runjags_estimates.txt @@ -0,0 +1,4 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +x (inclusion) 0.530 NA NA NA NA NA NA NA NA +x 542.626 17568.812 -99.394 0.000 34.736 555.57478 0.032 1000 1.290 +x_sigma 3306.894 89013.203 0.158 2.367 831.086 2815.16476 0.032 1000 1.279 diff --git a/tests/results/JAGS-summary-tables/fit_factor_independent_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_factor_independent_runjags_estimates.txt new file mode 100644 index 00000000..73ceb84e --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_factor_independent_runjags_estimates.txt @@ -0,0 +1,4 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +p1[1] 0.652 0.486 0.071 0.537 1.886 0.01604 0.033 925 0.999 +p1[2] 0.694 0.476 0.090 0.594 1.922 0.01442 0.030 1105 1.000 +p1[3] 0.685 0.483 0.085 0.595 1.948 0.01527 0.032 1000 1.002 diff --git a/tests/results/JAGS-summary-tables/fit_factor_meandif_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_factor_meandif_runjags_estimates.txt new file mode 100644 index 00000000..3ab5d225 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_factor_meandif_runjags_estimates.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +p1[1] 0.039 0.496 -0.986 0.060 0.937 0.01570 0.032 1000 1.000 +p1[2] -0.012 0.503 -0.967 -0.001 1.026 0.01590 0.032 1000 1.001 diff --git a/tests/results/JAGS-summary-tables/fit_factor_orthonormal_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_factor_orthonormal_runjags_estimates.txt new file mode 100644 index 00000000..988cc6ca --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_factor_orthonormal_runjags_estimates.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +p1[1] 0.017 0.977 -1.862 0.031 1.936 0.03092 0.032 1000 1.000 +p1[2] 0.050 1.002 -1.998 0.074 1.966 0.03171 0.032 1000 1.001 diff --git a/tests/results/JAGS-summary-tables/fit_factor_treatment_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_factor_treatment_runjags_estimates.txt new file mode 100644 index 00000000..620b4003 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_factor_treatment_runjags_estimates.txt @@ -0,0 +1,2 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +p1[2] 0.497 0.291 0.022 0.491 0.973 0.00921 0.032 1000 1.000 diff --git a/tests/results/JAGS-summary-tables/fit_formula_auto_scaled_model_summary.txt b/tests/results/JAGS-summary-tables/fit_formula_auto_scaled_model_summary.txt new file mode 100644 index 00000000..cb052c0e --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_formula_auto_scaled_model_summary.txt @@ -0,0 +1,7 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 (mu) intercept ~ Normal(0, 5) + log(marglik) -100363.19 (mu) x_cont1 ~ Normal(0, 1) + Post. prob. 1.000 (mu) x_cont2 ~ Normal(0, 1) + Inclusion BF Inf (mu) x_cont1:x_cont2 ~ Normal(0, 1) + sigma ~ Lognormal(0, 1) diff --git a/tests/results/JAGS-summary-tables/fit_formula_auto_scaled_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_formula_auto_scaled_runjags_estimates.txt new file mode 100644 index 00000000..5c3642cd --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_formula_auto_scaled_runjags_estimates.txt @@ -0,0 +1,6 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept 5.734 7.794 -6.822 4.648 25.392 1.94241 0.249 26 1.218 +(mu) x_cont1 0.231 1.061 -1.824 0.266 2.261 0.05771 0.054 598 1.025 +(mu) x_cont2 -0.012 1.016 -1.886 -0.035 2.115 0.03337 0.033 935 1.000 +(mu) x_cont1:x_cont2 0.069 0.996 -1.820 0.057 1.998 0.03152 0.032 1000 1.000 +sigma 19647.945 8335.195 6848.831 18798.352 37249.294 4048.13587 0.486 3 1.824 diff --git a/tests/results/JAGS-summary-tables/fit_formula_factor_mixture_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_formula_factor_mixture_runjags_estimates.txt new file mode 100644 index 00000000..292028ec --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_formula_factor_mixture_runjags_estimates.txt @@ -0,0 +1,7 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept -0.004 0.120 -0.218 -0.008 0.260 0.00568 0.047 447 1.000 +(mu) x_cont 0.195 0.112 -0.039 0.195 0.410 0.00354 0.032 1000 1.001 +(mu) x_fac3t (inclusion) 0.398 NA NA NA NA NA NA NA NA +(mu) x_fac3t[B] -0.082 0.154 -0.493 0.000 0.076 0.00716 0.046 465 1.001 +(mu) x_fac3t[C] -0.001 0.114 -0.260 0.000 0.278 0.00453 0.040 633 1.005 +sigma 0.972 0.072 0.847 0.967 1.128 0.00308 0.043 549 1.003 diff --git a/tests/results/JAGS-summary-tables/fit_formula_interaction_cont_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_formula_interaction_cont_runjags_estimates.txt new file mode 100644 index 00000000..29fb99da --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_formula_interaction_cont_runjags_estimates.txt @@ -0,0 +1,6 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept 0.035 0.104 -0.178 0.040 0.243 0.00350 0.034 902 1.001 +(mu) x_cont1 0.361 0.123 0.124 0.365 0.587 0.00384 0.031 1027 1.000 +(mu) x_cont2 -0.029 0.109 -0.241 -0.028 0.190 0.00357 0.033 931 1.000 +(mu) x_cont1:x_cont2 -0.391 0.149 -0.685 -0.390 -0.104 0.00495 0.033 914 1.003 +sigma 1.040 0.077 0.905 1.033 1.203 0.00364 0.047 451 1.000 diff --git a/tests/results/JAGS-summary-tables/fit_formula_interaction_fac_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_formula_interaction_fac_runjags_estimates.txt new file mode 100644 index 00000000..ec6341a4 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_formula_interaction_fac_runjags_estimates.txt @@ -0,0 +1,8 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept 0.060 0.164 -0.251 0.062 0.400 0.00840 0.051 392 1.001 +(mu) x_fac2t[B] 0.046 0.228 -0.414 0.048 0.476 0.01168 0.051 381 0.999 +(mu) x_fac3o[1] -0.077 0.263 -0.585 -0.074 0.458 0.01262 0.048 437 0.999 +(mu) x_fac3o[2] -0.046 0.256 -0.544 -0.043 0.443 0.01299 0.051 389 1.002 +(mu) x_fac2t:x_fac3o[1] -0.002 0.358 -0.678 0.004 0.714 0.01623 0.045 487 1.000 +(mu) x_fac2t:x_fac3o[2] -0.122 0.367 -0.835 -0.134 0.596 0.01924 0.052 369 1.005 +sigma 1.167 0.087 1.007 1.162 1.358 0.00365 0.042 579 1.006 diff --git a/tests/results/JAGS-summary-tables/fit_formula_interaction_mix_main_model_summary.txt b/tests/results/JAGS-summary-tables/fit_formula_interaction_mix_main_model_summary.txt new file mode 100644 index 00000000..16528ae7 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_formula_interaction_mix_main_model_summary.txt @@ -0,0 +1,6 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 (mu) intercept ~ Normal(0, 5) + log(marglik) -158.89 (mu) x_cont1 ~ Normal(0, 1) + Post. prob. 1.000 (mu) x_fac3o ~ orthonormal contrast: mNormal(0, 1) + Inclusion BF Inf sigma ~ Lognormal(0, 1) diff --git a/tests/results/JAGS-summary-tables/fit_formula_interaction_mix_main_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_formula_interaction_mix_main_runjags_estimates.txt new file mode 100644 index 00000000..0872820d --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_formula_interaction_mix_main_runjags_estimates.txt @@ -0,0 +1,6 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept 0.025 0.110 -0.199 0.025 0.228 0.00338 0.031 1071 1.001 +(mu) x_cont1 0.448 0.124 0.207 0.449 0.689 0.00392 0.032 1000 1.000 +(mu) x_fac3o[1] -0.007 0.185 -0.378 -0.006 0.362 0.00584 0.032 1000 1.005 +(mu) x_fac3o[2] -0.112 0.189 -0.489 -0.108 0.251 0.00549 0.029 1225 0.999 +sigma 1.085 0.082 0.941 1.077 1.264 0.00378 0.046 476 0.999 diff --git a/tests/results/JAGS-summary-tables/fit_formula_interaction_mix_model_summary.txt b/tests/results/JAGS-summary-tables/fit_formula_interaction_mix_model_summary.txt new file mode 100644 index 00000000..4ab4d0bc --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_formula_interaction_mix_model_summary.txt @@ -0,0 +1,7 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 (mu) intercept ~ Normal(0, 5) + log(marglik) -161.46 (mu) x_cont1 ~ Normal(0, 1) + Post. prob. 1.000 (mu) x_fac3o ~ orthonormal contrast: mNormal(0, 1) + Inclusion BF Inf (mu) x_cont1:x_fac3o ~ orthonormal contrast: mNormal(0, 1) + sigma ~ Lognormal(0, 1) diff --git a/tests/results/JAGS-summary-tables/fit_formula_interaction_mix_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_formula_interaction_mix_runjags_estimates.txt new file mode 100644 index 00000000..bc1d3568 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_formula_interaction_mix_runjags_estimates.txt @@ -0,0 +1,8 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept 0.013 0.113 -0.197 0.016 0.234 0.00359 0.032 1000 1.002 +(mu) x_cont1 0.457 0.125 0.215 0.457 0.710 0.00411 0.033 928 1.000 +(mu) x_fac3o[1] 0.029 0.189 -0.334 0.026 0.417 0.00585 0.031 1045 1.000 +(mu) x_fac3o[2] -0.095 0.193 -0.465 -0.088 0.273 0.00610 0.032 1000 1.002 +(mu) x_cont1:x_fac3o[1] -0.221 0.220 -0.667 -0.211 0.216 0.00696 0.032 1000 1.000 +(mu) x_cont1:x_fac3o[2] -0.062 0.200 -0.442 -0.060 0.319 0.00655 0.033 938 1.000 +sigma 1.082 0.080 0.945 1.075 1.249 0.00294 0.037 726 1.019 diff --git a/tests/results/JAGS-summary-tables/fit_formula_manual_scaled_model_summary.txt b/tests/results/JAGS-summary-tables/fit_formula_manual_scaled_model_summary.txt new file mode 100644 index 00000000..cb052c0e --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_formula_manual_scaled_model_summary.txt @@ -0,0 +1,7 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 (mu) intercept ~ Normal(0, 5) + log(marglik) -100363.19 (mu) x_cont1 ~ Normal(0, 1) + Post. prob. 1.000 (mu) x_cont2 ~ Normal(0, 1) + Inclusion BF Inf (mu) x_cont1:x_cont2 ~ Normal(0, 1) + sigma ~ Lognormal(0, 1) diff --git a/tests/results/JAGS-summary-tables/fit_formula_manual_scaled_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_formula_manual_scaled_runjags_estimates.txt new file mode 100644 index 00000000..5c3642cd --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_formula_manual_scaled_runjags_estimates.txt @@ -0,0 +1,6 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept 5.734 7.794 -6.822 4.648 25.392 1.94241 0.249 26 1.218 +(mu) x_cont1 0.231 1.061 -1.824 0.266 2.261 0.05771 0.054 598 1.025 +(mu) x_cont2 -0.012 1.016 -1.886 -0.035 2.115 0.03337 0.033 935 1.000 +(mu) x_cont1:x_cont2 0.069 0.996 -1.820 0.057 1.998 0.03152 0.032 1000 1.000 +sigma 19647.945 8335.195 6848.831 18798.352 37249.294 4048.13587 0.486 3 1.824 diff --git a/tests/results/JAGS-summary-tables/fit_formula_multi_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_formula_multi_runjags_estimates.txt new file mode 100644 index 00000000..ddd8ea30 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_formula_multi_runjags_estimates.txt @@ -0,0 +1,5 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept -0.014 0.039 -0.097 -0.013 0.061 0.00115 0.029 1187 1.001 +(mu) x_cont1 0.194 0.044 0.110 0.194 0.282 0.00142 0.032 1005 1.000 +(sigma_exp) x_fac2t 0.511 0.072 0.368 0.508 0.652 0.00533 0.074 183 1.002 +sigma 0.491 0.035 0.428 0.489 0.564 0.00148 0.043 550 1.002 diff --git a/tests/results/JAGS-summary-tables/fit_formula_orthonormal_model_summary.txt b/tests/results/JAGS-summary-tables/fit_formula_orthonormal_model_summary.txt new file mode 100644 index 00000000..16379db6 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_formula_orthonormal_model_summary.txt @@ -0,0 +1,6 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 (mu) intercept ~ Normal(0, 5) + log(marglik) -148.68 (mu) x_cont1 ~ Normal(0, 1) + Post. prob. 1.000 (mu) x_fac3o ~ orthonormal contrast: mNormal(0, 1) + Inclusion BF Inf sigma ~ Lognormal(0, 1) diff --git a/tests/results/JAGS-summary-tables/fit_formula_orthonormal_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_formula_orthonormal_runjags_estimates.txt new file mode 100644 index 00000000..fa464a3e --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_formula_orthonormal_runjags_estimates.txt @@ -0,0 +1,6 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept -0.041 0.097 -0.226 -0.041 0.150 0.00306 0.032 1000 1.001 +(mu) x_cont1 0.401 0.107 0.193 0.405 0.600 0.00364 0.034 883 0.999 +(mu) x_fac3o[1] 0.187 0.168 -0.148 0.186 0.518 0.00604 0.036 818 1.008 +(mu) x_fac3o[2] 0.118 0.170 -0.217 0.127 0.431 0.00491 0.029 1284 1.003 +sigma 0.970 0.068 0.840 0.966 1.109 0.00303 0.044 505 1.002 diff --git a/tests/results/JAGS-summary-tables/fit_formula_simple_model_summary.txt b/tests/results/JAGS-summary-tables/fit_formula_simple_model_summary.txt new file mode 100644 index 00000000..99967c5b --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_formula_simple_model_summary.txt @@ -0,0 +1,6 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 (mu) intercept ~ Normal(0, 5) + log(marglik) -146.01 (mu) x_cont1 ~ Normal(0, 1) + Post. prob. 1.000 sigma ~ Lognormal(0, 1) + Inclusion BF Inf diff --git a/tests/results/JAGS-summary-tables/fit_formula_simple_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_formula_simple_runjags_estimates.txt new file mode 100644 index 00000000..5de91978 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_formula_simple_runjags_estimates.txt @@ -0,0 +1,4 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept -0.042 0.097 -0.242 -0.040 0.149 0.00308 0.032 1000 1.001 +(mu) x_cont1 0.391 0.108 0.183 0.393 0.603 0.00317 0.029 1218 1.000 +sigma 0.970 0.069 0.846 0.964 1.124 0.00267 0.039 674 1.016 diff --git a/tests/results/JAGS-summary-tables/fit_formula_treatment_model_summary.txt b/tests/results/JAGS-summary-tables/fit_formula_treatment_model_summary.txt new file mode 100644 index 00000000..c45bd272 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_formula_treatment_model_summary.txt @@ -0,0 +1,6 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 (mu) intercept ~ Normal(0, 5) + log(marglik) -147.65 (mu) x_cont1 ~ Normal(0, 1) + Post. prob. 1.000 (mu) x_fac2t ~ treatment contrast: Normal(0, 1) + Inclusion BF Inf sigma ~ Lognormal(0, 1) diff --git a/tests/results/JAGS-summary-tables/fit_formula_treatment_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_formula_treatment_runjags_estimates.txt new file mode 100644 index 00000000..9bda0a38 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_formula_treatment_runjags_estimates.txt @@ -0,0 +1,5 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept -0.049 0.130 -0.299 -0.048 0.206 0.00679 0.052 371 1.004 +(mu) x_cont1 0.394 0.111 0.177 0.399 0.613 0.00351 0.032 1000 0.999 +(mu) x_fac2t[B] 0.019 0.182 -0.355 0.021 0.350 0.00950 0.052 380 1.005 +sigma 0.979 0.073 0.855 0.972 1.133 0.00346 0.047 457 1.000 diff --git a/tests/results/JAGS-summary-tables/fit_joint_complex_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_joint_complex_runjags_estimates.txt new file mode 100644 index 00000000..bc04710f --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_joint_complex_runjags_estimates.txt @@ -0,0 +1,10 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept (inclusion) 0.027 NA NA NA NA NA NA NA NA +(mu) intercept -0.001 0.017 0.000 0.000 0.000 0.00052 0.030 1085 1.062 +(mu) x_cont1 (inclusion) 0.363 NA NA NA NA NA NA NA NA +(mu) x_cont1 0.077 0.124 0.000 0.000 0.390 0.00569 0.046 471 1.009 +(mu) x_fac3t (inclusion) 0.066 NA NA NA NA NA NA NA NA +(mu) x_fac3t[1] 0.007 0.049 0.000 0.000 0.126 0.00261 0.053 467 1.000 +(mu) x_fac3t[2] 0.008 0.051 0.000 0.000 0.167 0.00216 0.042 638 1.034 +sigma (inclusion) 0.495 NA NA NA NA NA NA NA NA +sigma 0.972 0.070 0.843 0.969 1.125 0.00250 0.036 815 1.005 diff --git a/tests/results/JAGS-summary-tables/fit_marginal_0_model_summary.txt b/tests/results/JAGS-summary-tables/fit_marginal_0_model_summary.txt new file mode 100644 index 00000000..90f83f5f --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_marginal_0_model_summary.txt @@ -0,0 +1,6 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 (mu) intercept ~ Normal(0, 1) + log(marglik) -144.46 (mu) x_cont1 ~ Normal(0, 1) + Post. prob. 1.000 sigma ~ Cauchy(0, 1)[0, 5] + Inclusion BF Inf diff --git a/tests/results/JAGS-summary-tables/fit_marginal_0_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_marginal_0_runjags_estimates.txt new file mode 100644 index 00000000..58406d64 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_marginal_0_runjags_estimates.txt @@ -0,0 +1,4 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept 0.616 0.037 0.543 0.615 0.689 0.00117 0.032 1000 1.001 +(mu) x_cont1 0.367 0.083 0.205 0.366 0.531 0.00262 0.032 1000 1.002 +sigma 0.517 0.029 0.465 0.515 0.582 0.00114 0.039 646 1.010 diff --git a/tests/results/JAGS-summary-tables/fit_marginal_1_model_summary.txt b/tests/results/JAGS-summary-tables/fit_marginal_1_model_summary.txt new file mode 100644 index 00000000..4c8186c3 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_marginal_1_model_summary.txt @@ -0,0 +1,8 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 (mu) intercept ~ Normal(0, 1) + log(marglik) -148.06 (mu) x_cont1 ~ Normal(0, 1) + Post. prob. 1.000 (mu) x_fac2t ~ treatment contrast: Normal(0, 1) + Inclusion BF Inf (mu) x_fac3md ~ mean difference contrast: mNormal(0, 0.25) + (mu) x_cont1:x_fac3md ~ mean difference contrast: mNormal(0, 0.25) + sigma ~ Cauchy(0, 1)[0, 5] diff --git a/tests/results/JAGS-summary-tables/fit_marginal_1_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_marginal_1_runjags_estimates.txt new file mode 100644 index 00000000..a5dad31d --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_marginal_1_runjags_estimates.txt @@ -0,0 +1,9 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept 0.612 0.055 0.500 0.614 0.724 0.00291 0.053 382 1.000 +(mu) x_cont1 0.354 0.086 0.185 0.354 0.515 0.00298 0.035 824 1.000 +(mu) x_fac2t[B] 0.009 0.077 -0.134 0.006 0.170 0.00422 0.055 341 1.001 +(mu) x_fac3md[1] 0.019 0.053 -0.087 0.020 0.124 0.00150 0.028 1423 1.003 +(mu) x_fac3md[2] 0.154 0.054 0.047 0.155 0.258 0.00180 0.033 916 1.006 +(mu) x_cont1:x_fac3md[1] 0.018 0.058 -0.094 0.018 0.136 0.00192 0.033 928 0.999 +(mu) x_cont1:x_fac3md[2] 0.036 0.058 -0.081 0.037 0.150 0.00197 0.034 871 1.001 +sigma 0.509 0.028 0.458 0.508 0.566 0.00136 0.049 442 1.022 diff --git a/tests/results/JAGS-summary-tables/fit_marginal_ss_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_marginal_ss_runjags_estimates.txt new file mode 100644 index 00000000..4a5dd6ae --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_marginal_ss_runjags_estimates.txt @@ -0,0 +1,13 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept 0.618 0.038 0.545 0.618 0.690 0.00132 0.035 853 0.999 +(mu) x_cont1 (inclusion) 1.000 NA NA NA NA NA NA NA NA +(mu) x_cont1 0.358 0.084 0.197 0.357 0.518 0.00282 0.034 897 1.003 +(mu) x_fac2t (inclusion) 0.056 NA NA NA NA NA NA NA NA +(mu) x_fac2t[B] 0.000 0.018 -0.009 0.000 0.009 0.00086 0.048 469 1.003 +(mu) x_fac3md (inclusion) 0.827 NA NA NA NA NA NA NA NA +(mu) x_fac3md[1] 0.016 0.050 -0.083 0.004 0.129 0.00161 0.032 958 1.002 +(mu) x_fac3md[2] 0.135 0.078 0.000 0.150 0.262 0.00520 0.067 225 1.004 +(mu) x_cont1:x_fac3md (inclusion) 0.075 NA NA NA NA NA NA NA NA +(mu) x_cont1:x_fac3md[1] 0.002 0.016 0.000 0.000 0.039 0.00048 0.031 1092 1.046 +(mu) x_cont1:x_fac3md[2] 0.003 0.018 0.000 0.000 0.062 0.00056 0.031 1107 1.027 +sigma 0.509 0.028 0.459 0.507 0.564 0.00119 0.043 535 1.001 diff --git a/tests/results/JAGS-summary-tables/fit_missing_model_summary.txt b/tests/results/JAGS-summary-tables/fit_missing_model_summary.txt new file mode 100644 index 00000000..ca05355d --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_missing_model_summary.txt @@ -0,0 +1,6 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 mu ~ Normal(0.2, 0.2) + log(marglik) 0.00 + Post. prob. 1.000 + Inclusion BF Inf diff --git a/tests/results/JAGS-summary-tables/fit_missing_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_missing_runjags_estimates.txt new file mode 100644 index 00000000..116f13d3 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_missing_runjags_estimates.txt @@ -0,0 +1,2 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +mu 0.201 0.198 -0.181 0.200 0.591 0.00442 0.022 2000 NA diff --git a/tests/results/JAGS-summary-tables/fit_mixture_components_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_mixture_components_runjags_estimates.txt new file mode 100644 index 00000000..0aa6d2c9 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_mixture_components_runjags_estimates.txt @@ -0,0 +1,4 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +beta (inclusion: b) 0.171 NA NA NA NA NA NA NA NA +beta (inclusion: a) 0.829 NA NA NA NA NA NA NA NA +beta -2.532 1.502 -4.985 -2.755 0.888 0.04607 0.031 1073 1.002 diff --git a/tests/results/JAGS-summary-tables/fit_mixture_simple_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_mixture_simple_runjags_estimates.txt new file mode 100644 index 00000000..e10b28f1 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_mixture_simple_runjags_estimates.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +mu (inclusion) 0.721 NA NA NA NA NA NA NA NA +mu -2.094 1.720 -4.668 -2.463 1.020 0.05747 0.033 908 1.000 diff --git a/tests/results/JAGS-summary-tables/fit_mixture_spike_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_mixture_spike_runjags_estimates.txt new file mode 100644 index 00000000..60a820fb --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_mixture_spike_runjags_estimates.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +gamma (inclusion) 1.000 NA NA NA NA NA NA NA NA +gamma -0.489 2.545 -4.498 -0.843 2.000 0.08051 0.032 1000 0.999 diff --git a/tests/results/JAGS-summary-tables/fit_no_autofit_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_no_autofit_runjags_estimates.txt new file mode 100644 index 00000000..10fc5472 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_no_autofit_runjags_estimates.txt @@ -0,0 +1,2 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +m 0.046 0.990 -1.728 0.000 2.114 0.09487 0.096 112 1.024 diff --git a/tests/results/JAGS-summary-tables/fit_orthonormal_0_model_summary.txt b/tests/results/JAGS-summary-tables/fit_orthonormal_0_model_summary.txt new file mode 100644 index 00000000..47f747e4 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_orthonormal_0_model_summary.txt @@ -0,0 +1,6 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 (mu) intercept ~ Normal(0, 5) + log(marglik) -165.93 sigma ~ Lognormal(0, 1) + Post. prob. 1.000 + Inclusion BF Inf diff --git a/tests/results/JAGS-summary-tables/fit_orthonormal_0_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_orthonormal_0_runjags_estimates.txt new file mode 100644 index 00000000..c80a6c80 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_orthonormal_0_runjags_estimates.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept 0.510 0.084 0.345 0.510 0.675 0.00067 0.008 15675 1.000 +sigma 0.922 0.060 0.812 0.919 1.049 0.00060 0.010 10076 1.000 diff --git a/tests/results/JAGS-summary-tables/fit_orthonormal_1_model_summary.txt b/tests/results/JAGS-summary-tables/fit_orthonormal_1_model_summary.txt new file mode 100644 index 00000000..06b973ec --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_orthonormal_1_model_summary.txt @@ -0,0 +1,6 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 (mu) intercept ~ Normal(0, 5) + log(marglik) -163.22 (mu) x_fac3o ~ orthonormal contrast: mNormal(0, 0.5) + Post. prob. 1.000 sigma ~ Lognormal(0, 1) + Inclusion BF Inf diff --git a/tests/results/JAGS-summary-tables/fit_orthonormal_1_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_orthonormal_1_runjags_estimates.txt new file mode 100644 index 00000000..f21425e1 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_orthonormal_1_runjags_estimates.txt @@ -0,0 +1,5 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept 0.512 0.081 0.352 0.512 0.667 0.00063 0.008 16217 1.000 +(mu) x_fac3o[1] 0.445 0.135 0.181 0.445 0.709 0.00107 0.008 15913 1.000 +(mu) x_fac3o[2] 0.024 0.136 -0.247 0.027 0.289 0.00109 0.008 15629 1.000 +sigma 0.885 0.058 0.781 0.882 1.005 0.00058 0.010 9955 1.000 diff --git a/tests/results/JAGS-summary-tables/fit_parallel_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_parallel_runjags_estimates.txt new file mode 100644 index 00000000..14d18551 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_parallel_runjags_estimates.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +m 0.189 0.206 -0.229 0.185 0.621 0.00838 0.041 600 1.012 +s 0.946 0.157 0.689 0.928 1.309 0.00859 0.055 332 1.008 diff --git a/tests/results/JAGS-summary-tables/fit_peese_model_summary.txt b/tests/results/JAGS-summary-tables/fit_peese_model_summary.txt new file mode 100644 index 00000000..224e4d61 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_peese_model_summary.txt @@ -0,0 +1,6 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 PEESE ~ Normal(0, 0.8)[0, Inf] + log(marglik) -0.01 + Post. prob. 1.000 + Inclusion BF Inf diff --git a/tests/results/JAGS-summary-tables/fit_peese_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_peese_runjags_estimates.txt new file mode 100644 index 00000000..f3e6e5b4 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_peese_runjags_estimates.txt @@ -0,0 +1,2 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +PEESE 0.644 0.489 0.025 0.545 1.774 0.01094 0.022 2000 NA diff --git a/tests/results/JAGS-summary-tables/fit_pet_model_summary.txt b/tests/results/JAGS-summary-tables/fit_pet_model_summary.txt new file mode 100644 index 00000000..d519338c --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_pet_model_summary.txt @@ -0,0 +1,6 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 PET ~ Normal(0, 0.2)[0, Inf] + log(marglik) -0.01 + Post. prob. 1.000 + Inclusion BF Inf diff --git a/tests/results/JAGS-summary-tables/fit_pet_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_pet_runjags_estimates.txt new file mode 100644 index 00000000..df01a716 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_pet_runjags_estimates.txt @@ -0,0 +1,2 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +PET 0.157 0.120 0.005 0.130 0.444 0.00269 0.022 2000 NA diff --git a/tests/results/JAGS-summary-tables/fit_random_factor_slope2_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_random_factor_slope2_runjags_estimates.txt new file mode 100644 index 00000000..c3674246 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_random_factor_slope2_runjags_estimates.txt @@ -0,0 +1,7 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept 0.286 0.138 0.016 0.282 0.560 0.00610 0.044 513 1.007 +(mu) x_fac3[1] -0.345 0.185 -0.726 -0.340 0.013 0.00773 0.042 573 1.002 +(mu) x_fac3[2] 0.134 0.179 -0.228 0.136 0.493 0.00750 0.042 687 1.001 +sd((mu) intercept|id) 0.207 0.161 0.007 0.174 0.617 0.00938 0.058 294 1.004 +sd((mu) x_fac3|id) 0.257 0.165 0.013 0.239 0.615 0.01080 0.065 233 1.001 +sigma 1.105 0.086 0.952 1.098 1.276 0.00421 0.049 441 1.017 diff --git a/tests/results/JAGS-summary-tables/fit_random_factor_slope3_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_random_factor_slope3_runjags_estimates.txt new file mode 100644 index 00000000..360dbd8c --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_random_factor_slope3_runjags_estimates.txt @@ -0,0 +1,9 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) x_fac3[A] 0.433 0.190 0.060 0.433 0.824 0.00666 0.035 856 1.005 +(mu) x_fac3[B] 0.517 0.215 0.071 0.523 0.932 0.00901 0.042 608 1.002 +(mu) x_fac3[C] -0.081 0.248 -0.559 -0.074 0.396 0.00942 0.038 693 1.004 +(mu) _xREx__id_x_fac3 (inclusion) 0.339 NA NA NA NA NA NA NA NA +(mu) _xREx__id_x_fac3[A] 0.141 0.236 0.000 0.000 0.761 0.02420 0.102 92 1.101 +(mu) _xREx__id_x_fac3[B] 0.114 0.213 0.000 0.000 0.728 0.01809 0.085 134 1.078 +(mu) _xREx__id_x_fac3[C] 0.116 0.215 0.000 0.000 0.751 0.02141 0.099 101 1.013 +sigma 1.128 0.086 0.974 1.121 1.316 0.00407 0.048 443 1.002 diff --git a/tests/results/JAGS-summary-tables/fit_random_factor_slope_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_random_factor_slope_runjags_estimates.txt new file mode 100644 index 00000000..95759ef4 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_random_factor_slope_runjags_estimates.txt @@ -0,0 +1,7 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept 0.298 0.134 0.024 0.300 0.545 0.00668 0.050 419 1.014 +(mu) x_cont1 0.495 0.120 0.261 0.496 0.734 0.00379 0.032 1005 1.003 +sd((mu) intercept|id) 0.185 0.141 0.007 0.155 0.530 0.00690 0.049 418 1.001 +sd((mu) x_fac3[B]|id) 0.336 0.226 0.021 0.294 0.837 0.01145 0.051 395 1.002 +sd((mu) x_fac3[C]|id) 0.343 0.235 0.013 0.317 0.870 0.01117 0.048 444 1.000 +sigma 1.043 0.084 0.895 1.033 1.222 0.00365 0.044 544 1.009 diff --git a/tests/results/JAGS-summary-tables/fit_random_intercept_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_random_intercept_runjags_estimates.txt new file mode 100644 index 00000000..c4fbb545 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_random_intercept_runjags_estimates.txt @@ -0,0 +1,4 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept 0.331 0.145 0.054 0.328 0.619 0.00590 0.041 603 1.027 +sd((mu) intercept|id) 0.195 0.148 0.008 0.166 0.578 0.00811 0.055 335 0.999 +sigma 1.152 0.081 1.004 1.149 1.319 0.00355 0.044 519 1.002 diff --git a/tests/results/JAGS-summary-tables/fit_random_slope_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_random_slope_runjags_estimates.txt new file mode 100644 index 00000000..fd15e789 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_random_slope_runjags_estimates.txt @@ -0,0 +1,4 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept 0.326 0.109 0.117 0.327 0.536 0.00358 0.033 933 1.005 +sd((mu) x_cont1|id) 0.513 0.191 0.156 0.501 0.923 0.01198 0.063 256 1.000 +sigma 1.068 0.079 0.922 1.065 1.231 0.00408 0.052 377 1.000 diff --git a/tests/results/JAGS-summary-tables/fit_simple_formula_mixed_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_simple_formula_mixed_runjags_estimates.txt new file mode 100644 index 00000000..14962c23 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_simple_formula_mixed_runjags_estimates.txt @@ -0,0 +1,7 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept -0.151 0.044 -0.235 -0.150 -0.065 0.00198 0.045 500 NA +(mu) x_cont1 0.285 0.064 0.166 0.289 0.412 0.00285 0.045 500 NA +(mu) x_fac2t 0.064 0.066 -0.071 0.066 0.194 0.00297 0.045 500 NA +(mu) x_fac3t[1] 0.236 0.069 0.102 0.240 0.375 0.00306 0.045 500 NA +(mu) x_fac3t[2] -0.014 0.064 -0.127 -0.016 0.117 0.00287 0.045 500 NA +sigma 0.796 0.032 0.739 0.796 0.861 0.00175 0.055 331 NA diff --git a/tests/results/JAGS-summary-tables/fit_simple_normal_model_summary.txt b/tests/results/JAGS-summary-tables/fit_simple_normal_model_summary.txt new file mode 100644 index 00000000..6db29295 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_simple_normal_model_summary.txt @@ -0,0 +1,6 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 m ~ Normal(0, 1) + log(marglik) -31.95 s ~ Normal(0, 1)[0, Inf] + Post. prob. 1.000 + Inclusion BF Inf diff --git a/tests/results/JAGS-summary-tables/fit_simple_normal_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_simple_normal_runjags_estimates.txt new file mode 100644 index 00000000..a19f99d1 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_simple_normal_runjags_estimates.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +m 0.047 0.059 -0.070 0.047 0.161 0.00185 0.032 1000 1.003 +s 0.425 0.046 0.348 0.421 0.525 0.00199 0.043 540 1.005 diff --git a/tests/results/JAGS-summary-tables/fit_simple_pub_bias_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_simple_pub_bias_runjags_estimates.txt new file mode 100644 index 00000000..f83ba589 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_simple_pub_bias_runjags_estimates.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +PET 0.819 0.625 0.041 0.671 2.263 0.01978 0.032 1000 0.999 +PEESE 1.031 1.020 0.034 0.735 3.787 0.03222 0.032 1000 1.013 diff --git a/tests/results/JAGS-summary-tables/fit_simple_spike_model_summary.txt b/tests/results/JAGS-summary-tables/fit_simple_spike_model_summary.txt new file mode 100644 index 00000000..acc0bb88 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_simple_spike_model_summary.txt @@ -0,0 +1,6 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 s ~ Normal(0, 1)[0, Inf] + log(marglik) -29.49 + Post. prob. 1.000 + Inclusion BF Inf diff --git a/tests/results/JAGS-summary-tables/fit_simple_spike_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_simple_spike_runjags_estimates.txt new file mode 100644 index 00000000..1c5ca835 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_simple_spike_runjags_estimates.txt @@ -0,0 +1,2 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +s 0.423 0.043 0.352 0.420 0.514 0.00191 0.044 509 1.002 diff --git a/tests/results/JAGS-summary-tables/fit_simple_thin_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_simple_thin_runjags_estimates.txt new file mode 100644 index 00000000..017c74e9 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_simple_thin_runjags_estimates.txt @@ -0,0 +1,2 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +mu 0.033 1.031 -1.956 0.080 2.016 0.04722 0.046 496 1.001 diff --git a/tests/results/JAGS-summary-tables/fit_simple_various_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_simple_various_runjags_estimates.txt new file mode 100644 index 00000000..1f9e1181 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_simple_various_runjags_estimates.txt @@ -0,0 +1,11 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +p1 0.038 1.023 -1.996 0.021 2.135 0.03234 0.032 1000 1.002 +p2 1.117 0.632 0.372 0.965 2.706 0.02082 0.033 919 1.005 +p3 -0.010 0.666 -1.270 -0.022 1.408 0.02265 0.034 886 1.001 +p4 -1.753 2.080 -7.718 -0.889 -0.032 0.06345 0.031 1126 1.001 +p5 2.009 1.378 0.235 1.755 5.459 0.04426 0.032 1000 1.003 +p6 1.547 0.492 1.011 1.399 2.785 0.01558 0.032 1000 0.999 +p7 0.657 0.606 0.013 0.497 2.274 0.01915 0.032 1000 1.008 +p8 0.608 0.194 0.223 0.615 0.939 0.00614 0.032 1000 1.000 +p9 3.018 1.158 1.103 3.006 4.904 0.03956 0.034 877 0.999 +p10 1.000 0.000 1.000 1.000 1.000 0.00000 NA 0 NA diff --git a/tests/results/JAGS-summary-tables/fit_spike_factors_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_spike_factors_runjags_estimates.txt new file mode 100644 index 00000000..fa026934 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_spike_factors_runjags_estimates.txt @@ -0,0 +1,6 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) x_fac2i[A] 1.000 0.000 1.000 1.000 1.000 0.00000 NA 0 NA +(mu) x_fac2i[B] 1.000 0.000 1.000 1.000 1.000 0.00000 NA 0 NA +(mu) x_fac3t[B] 2.000 0.000 2.000 2.000 2.000 0.00000 NA 0 NA +(mu) x_fac3t[C] 2.000 0.000 2.000 2.000 2.000 0.00000 NA 0 NA +sigma 2.575 0.185 2.257 2.561 2.969 0.00853 0.046 557 1.043 diff --git a/tests/results/JAGS-summary-tables/fit_spike_slab_factor_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_spike_slab_factor_runjags_estimates.txt new file mode 100644 index 00000000..0ea7f34d --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_spike_slab_factor_runjags_estimates.txt @@ -0,0 +1,4 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +beta (inclusion) 0.527 NA NA NA NA NA NA NA NA +beta[1] 0.034 0.747 -1.569 0.000 1.759 0.02357 0.032 1000 1.006 +beta[2] 0.005 0.721 -1.680 0.000 1.639 0.02281 0.032 1000 1.000 diff --git a/tests/results/JAGS-summary-tables/fit_spike_slab_simple_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_spike_slab_simple_runjags_estimates.txt new file mode 100644 index 00000000..a5be7596 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_spike_slab_simple_runjags_estimates.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +mu (inclusion) 0.504 NA NA NA NA NA NA NA NA +mu -0.003 0.666 -1.553 0.000 1.506 0.02105 0.032 1000 1.003 diff --git a/tests/results/JAGS-summary-tables/fit_summary0_model_summary.txt b/tests/results/JAGS-summary-tables/fit_summary0_model_summary.txt new file mode 100644 index 00000000..5015c4f3 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_summary0_model_summary.txt @@ -0,0 +1,6 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 m ~ Normal(0, 1) + log(marglik) -1.11 + Post. prob. 1.000 + Inclusion BF Inf diff --git a/tests/results/JAGS-summary-tables/fit_summary0_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_summary0_runjags_estimates.txt new file mode 100644 index 00000000..393793eb --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_summary0_runjags_estimates.txt @@ -0,0 +1,2 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +m 0.198 0.212 -0.204 0.193 0.632 0.01019 0.048 434 NA diff --git a/tests/results/JAGS-summary-tables/fit_summary1_model_summary.txt b/tests/results/JAGS-summary-tables/fit_summary1_model_summary.txt new file mode 100644 index 00000000..2cb36596 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_summary1_model_summary.txt @@ -0,0 +1,6 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 m ~ Normal(0, 0.5) + log(marglik) -0.54 omega[one-sided: .05] ~ CumDirichlet(1, 1) + Post. prob. 1.000 + Inclusion BF Inf diff --git a/tests/results/JAGS-summary-tables/fit_summary1_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_summary1_runjags_estimates.txt new file mode 100644 index 00000000..400c1bc2 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_summary1_runjags_estimates.txt @@ -0,0 +1,4 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +m 0.155 0.198 -0.247 0.167 0.497 0.00921 0.047 461 NA +omega[0,0.05] 1.000 0.000 1.000 1.000 1.000 NA NA NA NA +omega[0.05,1] 0.509 0.301 0.028 0.508 0.983 0.01348 0.045 500 NA diff --git a/tests/results/JAGS-summary-tables/fit_summary2_model_summary.txt b/tests/results/JAGS-summary-tables/fit_summary2_model_summary.txt new file mode 100644 index 00000000..7993fbd6 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_summary2_model_summary.txt @@ -0,0 +1,6 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 m ~ Normal(0, 0.3) + log(marglik) -0.33 omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1) + Post. prob. 1.000 + Inclusion BF Inf diff --git a/tests/results/JAGS-summary-tables/fit_summary2_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_summary2_runjags_estimates.txt new file mode 100644 index 00000000..c694388b --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_summary2_runjags_estimates.txt @@ -0,0 +1,5 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +m 0.125 0.176 -0.202 0.128 0.479 0.00787 0.045 500 NA +omega[0,0.05] 1.000 0.000 1.000 1.000 1.000 NA NA NA NA +omega[0.05,0.5] 0.666 0.237 0.157 0.710 0.988 0.01061 0.045 500 NA +omega[0.5,1] 0.353 0.229 0.017 0.333 0.837 0.01023 0.045 500 NA diff --git a/tests/results/JAGS-summary-tables/fit_summary3_model_summary.txt b/tests/results/JAGS-summary-tables/fit_summary3_model_summary.txt new file mode 100644 index 00000000..456b27d2 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_summary3_model_summary.txt @@ -0,0 +1,6 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 m ~ Normal(0, 0.3) + log(marglik) -0.34 omega[two-sided: .2] = (1, 0.3) + Post. prob. 1.000 + Inclusion BF Inf diff --git a/tests/results/JAGS-summary-tables/fit_summary3_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_summary3_runjags_estimates.txt new file mode 100644 index 00000000..eaf0a2a8 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_summary3_runjags_estimates.txt @@ -0,0 +1,4 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +m 0.132 0.173 -0.219 0.132 0.482 0.00772 0.045 500 NA +omega[0,0.2] 0.300 0.000 0.300 0.300 0.300 NA NA NA NA +omega[0.2,1] 1.000 0.000 1.000 1.000 1.000 0.00000 NA 0 NA diff --git a/tests/results/JAGS-summary-tables/fit_vector_mcauchy_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_vector_mcauchy_runjags_estimates.txt new file mode 100644 index 00000000..3f174670 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_vector_mcauchy_runjags_estimates.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +p1[1] -0.227 16.382 -16.511 -0.071 18.750 0.51809 0.032 1000 1.031 +p1[2] 1.192 34.440 -15.713 -0.088 19.254 1.08910 0.032 1000 1.244 diff --git a/tests/results/JAGS-summary-tables/fit_vector_mnormal_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_vector_mnormal_runjags_estimates.txt new file mode 100644 index 00000000..51a88967 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_vector_mnormal_runjags_estimates.txt @@ -0,0 +1,4 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +p1[1] 0.012 0.997 -1.956 0.042 1.967 0.03154 0.032 1000 1.001 +p1[2] -0.009 0.994 -1.884 -0.024 2.035 0.03144 0.032 1000 0.999 +p1[3] 0.010 0.988 -1.985 0.047 1.893 0.03124 0.032 1000 0.999 diff --git a/tests/results/JAGS-summary-tables/fit_vector_mt_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_vector_mt_runjags_estimates.txt new file mode 100644 index 00000000..b4ed8d20 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_vector_mt_runjags_estimates.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +p1[1] 1.990 0.627 0.887 1.974 3.230 0.01982 0.032 1000 1.002 +p1[2] 2.026 0.639 0.737 2.044 3.280 0.02022 0.032 1000 1.001 diff --git a/tests/results/JAGS-summary-tables/fit_weightfunction_fixed_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_weightfunction_fixed_runjags_estimates.txt new file mode 100644 index 00000000..27ade754 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_weightfunction_fixed_runjags_estimates.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +omega[0,0.05] 1.000 0.000 1.000 1.000 1.000 NA NA NA NA +omega[0.05,1] 0.500 0.000 0.500 0.500 0.500 0.00000 NA 0 NA diff --git a/tests/results/JAGS-summary-tables/fit_weightfunction_onesided2_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_weightfunction_onesided2_runjags_estimates.txt new file mode 100644 index 00000000..53092294 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_weightfunction_onesided2_runjags_estimates.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +omega[0,0.05] 1.000 0.000 1.000 1.000 1.000 NA NA NA NA +omega[0.05,1] 0.510 0.283 0.037 0.525 0.968 0.00894 0.032 1000 0.999 diff --git a/tests/results/JAGS-summary-tables/fit_weightfunction_onesided3_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_weightfunction_onesided3_runjags_estimates.txt new file mode 100644 index 00000000..0bb034e7 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_weightfunction_onesided3_runjags_estimates.txt @@ -0,0 +1,4 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +omega[0,0.05] 1.000 0.000 1.000 1.000 1.000 NA NA NA NA +omega[0.05,0.1] 0.834 0.145 0.467 0.871 0.994 0.00458 0.032 1000 1.001 +omega[0.1,1] 0.510 0.187 0.154 0.510 0.852 0.00592 0.032 1000 0.999 diff --git a/tests/results/JAGS-summary-tables/fit_weightfunction_twosided_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_weightfunction_twosided_runjags_estimates.txt new file mode 100644 index 00000000..bee0b455 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_weightfunction_twosided_runjags_estimates.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +omega[0,0.05] 1.000 0.000 1.000 1.000 1.000 NA NA NA NA +omega[0.05,1] 0.506 0.297 0.027 0.514 0.975 0.00998 0.034 896 1.002 diff --git a/tests/results/JAGS-summary-tables/fit_wf_missing_model_summary.txt b/tests/results/JAGS-summary-tables/fit_wf_missing_model_summary.txt new file mode 100644 index 00000000..77027a38 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_wf_missing_model_summary.txt @@ -0,0 +1,6 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 mu ~ Normal(0, 0.8) + log(marglik) 0.00 + Post. prob. 1.000 + Inclusion BF Inf diff --git a/tests/results/JAGS-summary-tables/fit_wf_missing_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_wf_missing_runjags_estimates.txt new file mode 100644 index 00000000..2d5d8d15 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_wf_missing_runjags_estimates.txt @@ -0,0 +1,2 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +mu 0.005 0.790 -1.525 0.002 1.564 0.01767 0.022 2000 NA diff --git a/tests/results/JAGS-summary-tables/fit_wf_onesided_model_summary.txt b/tests/results/JAGS-summary-tables/fit_wf_onesided_model_summary.txt new file mode 100644 index 00000000..cc8bca90 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_wf_onesided_model_summary.txt @@ -0,0 +1,6 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 omega[one-sided: .025] ~ CumDirichlet(1, 1) + log(marglik) -0.02 + Post. prob. 1.000 + Inclusion BF Inf diff --git a/tests/results/JAGS-summary-tables/fit_wf_onesided_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_wf_onesided_runjags_estimates.txt new file mode 100644 index 00000000..0289900e --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_wf_onesided_runjags_estimates.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +omega[0,0.025] 1.000 0.000 1.000 1.000 1.000 NA NA NA NA +omega[0.025,1] 0.509 0.289 0.028 0.517 0.978 0.00647 0.022 2000 NA diff --git a/tests/results/JAGS-summary-tables/fit_wf_twosided_model_summary.txt b/tests/results/JAGS-summary-tables/fit_wf_twosided_model_summary.txt new file mode 100644 index 00000000..014001b6 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_wf_twosided_model_summary.txt @@ -0,0 +1,6 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 omega[two-sided: .05] ~ CumDirichlet(1, 1) + log(marglik) 0.00 + Post. prob. 1.000 + Inclusion BF Inf diff --git a/tests/results/JAGS-summary-tables/fit_wf_twosided_runjags_estimates.txt b/tests/results/JAGS-summary-tables/fit_wf_twosided_runjags_estimates.txt new file mode 100644 index 00000000..a83381d2 --- /dev/null +++ b/tests/results/JAGS-summary-tables/fit_wf_twosided_runjags_estimates.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +omega[0,0.05] 1.000 0.000 1.000 1.000 1.000 NA NA NA NA +omega[0.05,1] 0.503 0.285 0.032 0.508 0.979 0.00638 0.022 2000 NA diff --git a/tests/results/JAGS-summary-tables/model_summary_empty.txt b/tests/results/JAGS-summary-tables/model_summary_empty.txt new file mode 100644 index 00000000..33949d63 --- /dev/null +++ b/tests/results/JAGS-summary-tables/model_summary_empty.txt @@ -0,0 +1,6 @@ + + Model Parameter prior distributions + Prior prob. + log(marglik) + Post. prob. + Inclusion BF diff --git a/tests/results/JAGS-summary-tables/runjags_factor_conditional.txt b/tests/results/JAGS-summary-tables/runjags_factor_conditional.txt new file mode 100644 index 00000000..e0a1c3e0 --- /dev/null +++ b/tests/results/JAGS-summary-tables/runjags_factor_conditional.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 +p1[1] 0.017 0.977 -1.862 0.031 1.936 +p1[2] 0.050 1.002 -1.998 0.074 1.966 diff --git a/tests/results/JAGS-summary-tables/runjags_factor_conditional_transformed.txt b/tests/results/JAGS-summary-tables/runjags_factor_conditional_transformed.txt new file mode 100644 index 00000000..80e739ae --- /dev/null +++ b/tests/results/JAGS-summary-tables/runjags_factor_conditional_transformed.txt @@ -0,0 +1,4 @@ + Mean SD 0.025 0.5 0.975 +p1 [dif: 1] 0.041 0.818 -1.631 0.060 1.605 +p1 [dif: 2] -0.033 0.796 -1.612 -0.029 1.527 +p1 [dif: 3] -0.008 0.811 -1.550 -0.009 1.564 diff --git a/tests/results/JAGS-summary-tables/runjags_formula_mixture_inference.txt b/tests/results/JAGS-summary-tables/runjags_formula_mixture_inference.txt new file mode 100644 index 00000000..6566f87b --- /dev/null +++ b/tests/results/JAGS-summary-tables/runjags_formula_mixture_inference.txt @@ -0,0 +1,2 @@ + Prior prob. Post. prob. Inclusion BF +(mu) x_fac3t 0.500 0.398 0.661 diff --git a/tests/results/JAGS-summary-tables/runjags_inference_empty.txt b/tests/results/JAGS-summary-tables/runjags_inference_empty.txt new file mode 100644 index 00000000..4601b457 --- /dev/null +++ b/tests/results/JAGS-summary-tables/runjags_inference_empty.txt @@ -0,0 +1,2 @@ +[1] Prior prob. Post. prob. Inclusion BF +<0 rows> (or 0-length row.names) diff --git a/tests/results/JAGS-summary-tables/runjags_joint_complex_inference.txt b/tests/results/JAGS-summary-tables/runjags_joint_complex_inference.txt new file mode 100644 index 00000000..fe29dbfe --- /dev/null +++ b/tests/results/JAGS-summary-tables/runjags_joint_complex_inference.txt @@ -0,0 +1,5 @@ + Prior prob. Post. prob. Inclusion BF +(mu) intercept 0.500 0.027 0.028 +(mu) x_cont1 0.500 0.363 0.570 +(mu) x_fac3t 0.500 0.066 0.071 +sigma 0.500 0.495 0.980 diff --git a/tests/results/JAGS-summary-tables/runjags_mixture_conditional.txt b/tests/results/JAGS-summary-tables/runjags_mixture_conditional.txt new file mode 100644 index 00000000..2312efaa --- /dev/null +++ b/tests/results/JAGS-summary-tables/runjags_mixture_conditional.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 +mu (inclusion) 0.721 NA NA NA NA +mu -3.003 0.951 -4.813 -2.981 -1.128 diff --git a/tests/results/JAGS-summary-tables/runjags_mixture_inference.txt b/tests/results/JAGS-summary-tables/runjags_mixture_inference.txt new file mode 100644 index 00000000..1805e8aa --- /dev/null +++ b/tests/results/JAGS-summary-tables/runjags_mixture_inference.txt @@ -0,0 +1,2 @@ + Prior prob. Post. prob. Inclusion BF +mu 0.714 0.721 1.034 diff --git a/tests/results/JAGS-summary-tables/runjags_mixture_spike_inference.txt b/tests/results/JAGS-summary-tables/runjags_mixture_spike_inference.txt new file mode 100644 index 00000000..78cc12a7 --- /dev/null +++ b/tests/results/JAGS-summary-tables/runjags_mixture_spike_inference.txt @@ -0,0 +1,2 @@ + Prior prob. Post. prob. Inclusion BF +gamma 1.000 1.000 Inf diff --git a/tests/results/JAGS-summary-tables/runjags_pub_bias_conditional.txt b/tests/results/JAGS-summary-tables/runjags_pub_bias_conditional.txt new file mode 100644 index 00000000..e42c5290 --- /dev/null +++ b/tests/results/JAGS-summary-tables/runjags_pub_bias_conditional.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 +PET 0.819 0.625 0.041 0.671 2.263 +PEESE 1.031 1.020 0.034 0.735 3.787 diff --git a/tests/results/JAGS-summary-tables/runjags_remove_diagnostics.txt b/tests/results/JAGS-summary-tables/runjags_remove_diagnostics.txt new file mode 100644 index 00000000..2e2ce8e4 --- /dev/null +++ b/tests/results/JAGS-summary-tables/runjags_remove_diagnostics.txt @@ -0,0 +1,5 @@ + Mean SD 0.025 0.5 0.975 +(mu) intercept 2.498 0.008 2.482 2.498 2.514 +(mu) x_mu 0.631 0.008 0.617 0.631 0.646 +(log_sigma) intercept 0.285 0.006 0.273 0.285 0.298 +(log_sigma) x_sigma -0.324 0.024 -0.370 -0.325 -0.280 diff --git a/tests/results/JAGS-summary-tables/runjags_spike_slab_conditional.txt b/tests/results/JAGS-summary-tables/runjags_spike_slab_conditional.txt new file mode 100644 index 00000000..b4cd50b2 --- /dev/null +++ b/tests/results/JAGS-summary-tables/runjags_spike_slab_conditional.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 +mu (inclusion) 0.504 NA NA NA NA +mu -0.006 0.939 -2.015 0.013 1.900 diff --git a/tests/results/JAGS-summary-tables/runjags_summary_complex2.txt b/tests/results/JAGS-summary-tables/runjags_summary_complex2.txt new file mode 100644 index 00000000..94c330fe --- /dev/null +++ b/tests/results/JAGS-summary-tables/runjags_summary_complex2.txt @@ -0,0 +1,12 @@ + Mean SD 0.025 0.5 0.975 +mu 1.019 0.588 0.243 0.891 2.460 +bias (inclusion) 0.526 NA NA NA NA +PET 0.823 0.562 0.034 0.831 2.016 +PEESE 1.365 1.080 0.060 1.208 3.836 +omega[0,0.025] 1.000 0.000 1.000 1.000 1.000 +omega[0.025,0.05] 0.604 0.267 0.062 0.657 0.970 +omega[0.05,0.975] 0.427 0.272 0.018 0.408 0.937 +omega[0.975,1] 0.635 0.367 0.038 0.651 1.000 +Conditional summary for PET is based on 59 samples. +Conditional summary for PEESE is based on 61 samples. +Conditional summary for omega is based on 143 samples. diff --git a/tests/results/JAGS-summary-tables/runjags_summary_complex3.txt b/tests/results/JAGS-summary-tables/runjags_summary_complex3.txt new file mode 100644 index 00000000..fdd01a38 --- /dev/null +++ b/tests/results/JAGS-summary-tables/runjags_summary_complex3.txt @@ -0,0 +1,28 @@ + Mean SD 0.025 0.5 0.975 +(mu) intercept (inclusion) 0.738 NA NA NA NA +(mu) intercept -0.140 0.049 -0.228 -0.143 -0.034 +(mu) x_cont1 (inclusion) 1.000 NA NA NA NA +(mu) x_cont1 0.273 0.063 0.150 0.273 0.398 +(mu) x_fac2t (inclusion) 0.128 NA NA NA NA +(mu) x_fac2t 0.068 0.062 -0.052 0.068 0.171 +(mu) x_fac3t (inclusion) 0.732 NA NA NA NA +(mu) x_fac3t[1] 0.298 0.075 0.156 0.298 0.441 +(mu) x_fac3t[2] -0.014 0.076 -0.153 -0.011 0.134 +sigma (inclusion: normal) 0.422 NA NA NA NA +sigma (inclusion: lognormal) 0.578 NA NA NA NA +sigma[normal] 0.800 0.035 0.729 0.800 0.868 +sigma[lognormal] 0.806 0.033 0.745 0.805 0.877 +bias (inclusion) 0.476 NA NA NA NA +PET 0.667 0.570 0.040 0.453 1.921 +omega[0,0.025] 1.000 0.000 1.000 1.000 1.000 +omega[0.025,0.05] 0.593 0.285 0.020 0.659 0.983 +omega[0.05,0.975] 0.419 0.284 0.017 0.363 0.947 +omega[0.975,1] 0.670 0.376 0.035 1.000 1.000 +Conditional summary for mu_intercept is based on 369 samples. +Conditional summary for mu_x_cont1 is based on 500 samples. +Conditional summary for mu_x_fac2t is based on 64 samples. +Conditional summary for mu_x_fac3t[1], mu_x_fac3t[2] is based on 366 samples. +Conditional summary for sigma[normal] is based on 211 samples. +Conditional summary for sigma[lognormal] is based on 289 samples. +Conditional summary for PET is based on 79 samples. +Conditional summary for omega is based on 159 samples. diff --git a/tests/results/JAGS-summary-tables/stan_estimates_basic.txt b/tests/results/JAGS-summary-tables/stan_estimates_basic.txt new file mode 100644 index 00000000..1fdc38eb --- /dev/null +++ b/tests/results/JAGS-summary-tables/stan_estimates_basic.txt @@ -0,0 +1,8 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +mu 1.439 0.377 0.811 1.425 2.159 0.06224 0.165 37 1.012 +sigma2 1.501 1.075 0.525 1.270 3.784 0.21677 0.202 25 1.150 +pooled_sigma 1.169 0.371 0.725 1.127 1.945 0.07779 0.210 23 1.178 +sigma_i[1] 1.169 0.371 0.725 1.127 1.945 0.07779 0.210 23 1.178 +sigma_i[2] 1.169 0.371 0.725 1.127 1.945 0.07779 0.210 23 1.178 +mu_i[1] 1.439 0.377 0.811 1.425 2.159 0.06224 0.165 37 1.012 +mu_i[2] 1.439 0.377 0.811 1.425 2.159 0.06224 0.165 37 1.012 diff --git a/tests/results/JAGS-summary-tables/stan_estimates_basic2.txt b/tests/results/JAGS-summary-tables/stan_estimates_basic2.txt new file mode 100644 index 00000000..39cb308b --- /dev/null +++ b/tests/results/JAGS-summary-tables/stan_estimates_basic2.txt @@ -0,0 +1,8 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +mu 4.514 1.718 0.811 4.157 2.159 1.06422 0.620 37 1.012 +sigma2 1.501 1.075 0.525 1.270 3.784 0.21677 0.202 25 1.150 +pooled_sigma 1.169 0.371 0.725 1.127 1.945 0.07779 0.210 23 1.178 +sigma_i[1] 1.169 0.371 0.725 1.127 1.945 0.07779 0.210 23 1.178 +sigma_i[2] 1.169 0.371 0.725 1.127 1.945 0.07779 0.210 23 1.178 +mu_i[1] 1.439 0.377 0.811 1.425 2.159 0.06224 0.165 37 1.012 +mu_i[2] 1.439 0.377 0.811 1.425 2.159 0.06224 0.165 37 1.012 diff --git a/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal01.txt b/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal01.txt new file mode 100644 index 00000000..07bea704 --- /dev/null +++ b/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal01.txt @@ -0,0 +1,5 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept 2.498 0.008 2.482 2.498 2.514 0.00026 0.032 1000 1.009 +(mu) x_mu 0.631 0.008 0.617 0.631 0.646 0.00025 0.033 925 1.000 +(log_sigma) intercept 0.285 0.006 0.273 0.285 0.298 0.00025 0.039 669 1.003 +(log_sigma) x_sigma -0.324 0.024 -0.370 -0.325 -0.280 0.00106 0.045 524 1.001 diff --git a/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal02.txt b/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal02.txt new file mode 100644 index 00000000..4adc2b88 --- /dev/null +++ b/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal02.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(log_sigma) intercept 0.285 0.006 0.273 0.285 0.298 0.00025 0.039 669 1.003 +(log_sigma) x_sigma -0.324 0.024 -0.370 -0.325 -0.280 0.00106 0.045 524 1.001 diff --git a/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal03.txt b/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal03.txt new file mode 100644 index 00000000..4adc2b88 --- /dev/null +++ b/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal03.txt @@ -0,0 +1,3 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(log_sigma) intercept 0.285 0.006 0.273 0.285 0.298 0.00025 0.039 669 1.003 +(log_sigma) x_sigma -0.324 0.024 -0.370 -0.325 -0.280 0.00106 0.045 524 1.001 diff --git a/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal04.txt b/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal04.txt new file mode 100644 index 00000000..24306651 --- /dev/null +++ b/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal04.txt @@ -0,0 +1,19 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept (inclusion) 0.738 NA NA NA NA NA NA NA NA +(mu) intercept -0.103 0.075 -0.222 -0.118 0.000 0.01088 0.146 47 NA +(mu) x_cont1 (inclusion) 1.000 NA NA NA NA NA NA NA NA +(mu) x_cont1 0.273 0.063 0.150 0.273 0.398 0.00330 0.053 361 NA +(mu) x_fac2t (inclusion) 0.128 NA NA NA NA NA NA NA NA +(mu) x_fac2t 0.009 0.032 0.000 0.000 0.129 0.00217 0.069 211 NA +(mu) x_fac3t (inclusion) 0.732 NA NA NA NA NA NA NA NA +(mu) x_fac3t[1] 0.218 0.147 0.000 0.261 0.427 0.04047 0.275 13 NA +(mu) x_fac3t[2] -0.010 0.065 -0.147 0.000 0.129 0.00292 0.045 500 NA +sigma (inclusion: normal) 0.422 NA NA NA NA NA NA NA NA +sigma (inclusion: lognormal) 0.578 NA NA NA NA NA NA NA NA +sigma 0.803 0.034 0.736 0.803 0.876 0.00263 0.078 166 NA +bias (inclusion) 0.476 NA NA NA NA NA NA NA NA +PET 0.105 0.332 0.000 0.000 1.346 0.01484 0.045 500 NA +omega[0,0.025] 1.000 0.000 1.000 1.000 1.000 NA NA NA NA +omega[0.025,0.05] 0.871 0.249 0.100 1.000 1.000 0.01112 0.045 500 NA +omega[0.05,0.975] 0.815 0.314 0.047 1.000 1.000 0.01406 0.045 500 NA +omega[0.975,1] 0.895 0.261 0.091 1.000 1.000 0.01169 0.045 500 NA diff --git a/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal05.txt b/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal05.txt new file mode 100644 index 00000000..b91bff76 --- /dev/null +++ b/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal05.txt @@ -0,0 +1,10 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept (inclusion) 0.738 NA NA NA NA NA NA NA NA +(mu) intercept -0.103 0.075 -0.222 -0.118 0.000 0.01088 0.146 47 NA +(mu) x_cont1 (inclusion) 1.000 NA NA NA NA NA NA NA NA +(mu) x_cont1 0.273 0.063 0.150 0.273 0.398 0.00330 0.053 361 NA +(mu) x_fac2t (inclusion) 0.128 NA NA NA NA NA NA NA NA +(mu) x_fac2t 0.009 0.032 0.000 0.000 0.129 0.00217 0.069 211 NA +(mu) x_fac3t (inclusion) 0.732 NA NA NA NA NA NA NA NA +(mu) x_fac3t[1] 0.218 0.147 0.000 0.261 0.427 0.04047 0.275 13 NA +(mu) x_fac3t[2] -0.010 0.065 -0.147 0.000 0.129 0.00292 0.045 500 NA diff --git a/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal06.txt b/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal06.txt new file mode 100644 index 00000000..b08b656a --- /dev/null +++ b/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal06.txt @@ -0,0 +1,6 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept -0.103 0.075 -0.222 -0.118 0.000 0.01088 0.146 47 NA +(mu) x_cont1 0.273 0.063 0.150 0.273 0.398 0.00330 0.053 361 NA +(mu) x_fac2t 0.009 0.032 0.000 0.000 0.129 0.00217 0.069 211 NA +(mu) x_fac3t[1] 0.218 0.147 0.000 0.261 0.427 0.04047 0.275 13 NA +(mu) x_fac3t[2] -0.010 0.065 -0.147 0.000 0.129 0.00292 0.045 500 NA diff --git a/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal07.txt b/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal07.txt new file mode 100644 index 00000000..5f5bdab6 --- /dev/null +++ b/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal07.txt @@ -0,0 +1,13 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept (inclusion) 0.738 NA NA NA NA NA NA NA NA +(mu) intercept -0.103 0.075 -0.222 -0.118 0.000 0.01088 0.146 47 NA +(mu) x_cont1 (inclusion) 1.000 NA NA NA NA NA NA NA NA +(mu) x_cont1 0.273 0.063 0.150 0.273 0.398 0.00330 0.053 361 NA +(mu) x_fac2t (inclusion) 0.128 NA NA NA NA NA NA NA NA +(mu) x_fac2t 0.009 0.032 0.000 0.000 0.129 0.00217 0.069 211 NA +(mu) x_fac3t (inclusion) 0.732 NA NA NA NA NA NA NA NA +(mu) x_fac3t[1] 0.218 0.147 0.000 0.261 0.427 0.04047 0.275 13 NA +(mu) x_fac3t[2] -0.010 0.065 -0.147 0.000 0.129 0.00292 0.045 500 NA +sigma (inclusion: normal) 0.422 NA NA NA NA NA NA NA NA +sigma (inclusion: lognormal) 0.578 NA NA NA NA NA NA NA NA +sigma 0.803 0.034 0.736 0.803 0.876 0.00263 0.078 166 NA diff --git a/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal08.txt b/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal08.txt new file mode 100644 index 00000000..627084ff --- /dev/null +++ b/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal08.txt @@ -0,0 +1,16 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept (inclusion) 0.738 NA NA NA NA NA NA NA NA +(mu) intercept -0.103 0.075 -0.222 -0.118 0.000 0.01088 0.146 47 NA +(mu) x_cont1 (inclusion) 1.000 NA NA NA NA NA NA NA NA +(mu) x_cont1 0.273 0.063 0.150 0.273 0.398 0.00330 0.053 361 NA +(mu) x_fac2t (inclusion) 0.128 NA NA NA NA NA NA NA NA +(mu) x_fac2t 0.009 0.032 0.000 0.000 0.129 0.00217 0.069 211 NA +(mu) x_fac3t (inclusion) 0.732 NA NA NA NA NA NA NA NA +(mu) x_fac3t[1] 0.218 0.147 0.000 0.261 0.427 0.04047 0.275 13 NA +(mu) x_fac3t[2] -0.010 0.065 -0.147 0.000 0.129 0.00292 0.045 500 NA +bias (inclusion) 0.476 NA NA NA NA NA NA NA NA +PET 0.105 0.332 0.000 0.000 1.346 0.01484 0.045 500 NA +omega[0,0.025] 1.000 0.000 1.000 1.000 1.000 NA NA NA NA +omega[0.025,0.05] 0.871 0.249 0.100 1.000 1.000 0.01112 0.045 500 NA +omega[0.05,0.975] 0.815 0.314 0.047 1.000 1.000 0.01406 0.045 500 NA +omega[0.975,1] 0.895 0.261 0.091 1.000 1.000 0.01169 0.045 500 NA diff --git a/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal09.txt b/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal09.txt new file mode 100644 index 00000000..04da98bc --- /dev/null +++ b/tests/results/JAGS-summary-tables/summary_parameter_or_formula_removal09.txt @@ -0,0 +1,10 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +sigma (inclusion: normal) 0.422 NA NA NA NA NA NA NA NA +sigma (inclusion: lognormal) 0.578 NA NA NA NA NA NA NA NA +sigma 0.803 0.034 0.736 0.803 0.876 0.00263 0.078 166 NA +bias (inclusion) 0.476 NA NA NA NA NA NA NA NA +PET 0.105 0.332 0.000 0.000 1.346 0.01484 0.045 500 NA +omega[0,0.025] 1.000 0.000 1.000 1.000 1.000 NA NA NA NA +omega[0.025,0.05] 0.871 0.249 0.100 1.000 1.000 0.01112 0.045 500 NA +omega[0.05,0.975] 0.815 0.314 0.047 1.000 1.000 0.01406 0.045 500 NA +omega[0.975,1] 0.895 0.261 0.091 1.000 1.000 0.01169 0.045 500 NA diff --git a/tests/results/JAGS-summary-tables/summary_parameter_probs1.txt b/tests/results/JAGS-summary-tables/summary_parameter_probs1.txt new file mode 100644 index 00000000..07bea704 --- /dev/null +++ b/tests/results/JAGS-summary-tables/summary_parameter_probs1.txt @@ -0,0 +1,5 @@ + Mean SD 0.025 0.5 0.975 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept 2.498 0.008 2.482 2.498 2.514 0.00026 0.032 1000 1.009 +(mu) x_mu 0.631 0.008 0.617 0.631 0.646 0.00025 0.033 925 1.000 +(log_sigma) intercept 0.285 0.006 0.273 0.285 0.298 0.00025 0.039 669 1.003 +(log_sigma) x_sigma -0.324 0.024 -0.370 -0.325 -0.280 0.00106 0.045 524 1.001 diff --git a/tests/results/JAGS-summary-tables/summary_parameter_probs2.txt b/tests/results/JAGS-summary-tables/summary_parameter_probs2.txt new file mode 100644 index 00000000..531ea59d --- /dev/null +++ b/tests/results/JAGS-summary-tables/summary_parameter_probs2.txt @@ -0,0 +1,5 @@ + Mean SD 0.5 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept 2.498 0.008 2.498 0.00026 0.032 1000 1.009 +(mu) x_mu 0.631 0.008 0.631 0.00025 0.033 925 1.000 +(log_sigma) intercept 0.285 0.006 0.285 0.00025 0.039 669 1.003 +(log_sigma) x_sigma -0.324 0.024 -0.325 0.00106 0.045 524 1.001 diff --git a/tests/results/JAGS-summary-tables/summary_parameter_probs3.txt b/tests/results/JAGS-summary-tables/summary_parameter_probs3.txt new file mode 100644 index 00000000..11d46d1f --- /dev/null +++ b/tests/results/JAGS-summary-tables/summary_parameter_probs3.txt @@ -0,0 +1,5 @@ + Mean SD 0.25 0.2 0.99 error(MCMC) error(MCMC)/SD ESS R-hat +(mu) intercept 2.498 0.008 2.492 2.491 2.517 0.00026 0.032 1000 1.009 +(mu) x_mu 0.631 0.008 0.626 0.625 0.651 0.00025 0.033 925 1.000 +(log_sigma) intercept 0.285 0.006 0.281 0.280 0.300 0.00025 0.039 669 1.003 +(log_sigma) x_sigma -0.324 0.024 -0.341 -0.344 -0.270 0.00106 0.045 524 1.001 diff --git a/tests/results/interpret/interpret2_basic.txt b/tests/results/interpret/interpret2_basic.txt new file mode 100644 index 00000000..4b3f1f9f --- /dev/null +++ b/tests/results/interpret/interpret2_basic.txt @@ -0,0 +1 @@ +RoBMA found moderate evidence in favor of the Effect, BF10 = 3.50, with mean model-averaged estimate mu = 0.298 kg, 95% CI [-0.020, 0.601]. diff --git a/tests/results/interpret/interpret2_conditional.txt b/tests/results/interpret/interpret2_conditional.txt new file mode 100644 index 00000000..78c3d31b --- /dev/null +++ b/tests/results/interpret/interpret2_conditional.txt @@ -0,0 +1 @@ +Test found strong evidence in favor of the Effect, BF10 = 15.00, with mean conditional estimate mu = 0.498, 95% CI [0.300, 0.704]. diff --git a/tests/results/interpret/interpret2_evidence_against.txt b/tests/results/interpret/interpret2_evidence_against.txt new file mode 100644 index 00000000..8b9d1946 --- /dev/null +++ b/tests/results/interpret/interpret2_evidence_against.txt @@ -0,0 +1 @@ +Method found moderate evidence against the Effect, BF01 = 0.100, with mean model-averaged estimate mu = 0.001, 95% CI [-0.103, 0.099]. diff --git a/tests/results/interpret/interpret2_inference_only.txt b/tests/results/interpret/interpret2_inference_only.txt new file mode 100644 index 00000000..586ae14e --- /dev/null +++ b/tests/results/interpret/interpret2_inference_only.txt @@ -0,0 +1 @@ +RoBMA found moderate evidence in favor of the Bias, BF_pb = 5.00. diff --git a/tests/results/interpret/interpret2_multiple.txt b/tests/results/interpret/interpret2_multiple.txt new file mode 100644 index 00000000..424a99cf --- /dev/null +++ b/tests/results/interpret/interpret2_multiple.txt @@ -0,0 +1 @@ +Test found moderate evidence in favor of the Effect, BF10 = 10.00, with mean model-averaged estimate mu = 0.298, 95% CI [0.109, 0.499]. Test found weak evidence against the Bias, BF_pb = 0.500. diff --git a/tests/results/interpret/interpret2_no_method.txt b/tests/results/interpret/interpret2_no_method.txt new file mode 100644 index 00000000..06ebd1d4 --- /dev/null +++ b/tests/results/interpret/interpret2_no_method.txt @@ -0,0 +1 @@ + found weak evidence in favor of the Effect, BF = 2.00. diff --git a/tests/results/interpret/interpret2_weak_evidence.txt b/tests/results/interpret/interpret2_weak_evidence.txt new file mode 100644 index 00000000..48c56235 --- /dev/null +++ b/tests/results/interpret/interpret2_weak_evidence.txt @@ -0,0 +1 @@ +Test found weak evidence in favor of the Effect, BF = 1.50, with mean model-averaged estimate delta = 0.102, 95% CI [-0.105, 0.305]. diff --git a/tests/results/interpret/interpret_BF_moderate_against1.txt b/tests/results/interpret/interpret_BF_moderate_against1.txt new file mode 100644 index 00000000..67ea16fc --- /dev/null +++ b/tests/results/interpret/interpret_BF_moderate_against1.txt @@ -0,0 +1 @@ +moderate evidence against the effect, BF = 0.100 diff --git a/tests/results/interpret/interpret_BF_moderate_against2.txt b/tests/results/interpret/interpret_BF_moderate_against2.txt new file mode 100644 index 00000000..8f1cd6e5 --- /dev/null +++ b/tests/results/interpret/interpret_BF_moderate_against2.txt @@ -0,0 +1 @@ +moderate evidence against the effect, BF = 0.200 diff --git a/tests/results/interpret/interpret_BF_moderate_favor.txt b/tests/results/interpret/interpret_BF_moderate_favor.txt new file mode 100644 index 00000000..12350a8e --- /dev/null +++ b/tests/results/interpret/interpret_BF_moderate_favor.txt @@ -0,0 +1 @@ +moderate evidence in favor of the effect, BF = 5.00 diff --git a/tests/results/interpret/interpret_BF_strong_against.txt b/tests/results/interpret/interpret_BF_strong_against.txt new file mode 100644 index 00000000..5f7c4dd2 --- /dev/null +++ b/tests/results/interpret/interpret_BF_strong_against.txt @@ -0,0 +1 @@ +strong evidence against the effect, BF01 = 0.050 diff --git a/tests/results/interpret/interpret_BF_strong_favor.txt b/tests/results/interpret/interpret_BF_strong_favor.txt new file mode 100644 index 00000000..a48058c8 --- /dev/null +++ b/tests/results/interpret/interpret_BF_strong_favor.txt @@ -0,0 +1 @@ +strong evidence in favor of the effect, BF10 = 15.00 diff --git a/tests/results/interpret/interpret_BF_weak_against.txt b/tests/results/interpret/interpret_BF_weak_against.txt new file mode 100644 index 00000000..c16222c2 --- /dev/null +++ b/tests/results/interpret/interpret_BF_weak_against.txt @@ -0,0 +1 @@ +weak evidence against the effect, BF = 0.500 diff --git a/tests/results/interpret/interpret_BF_weak_favor.txt b/tests/results/interpret/interpret_BF_weak_favor.txt new file mode 100644 index 00000000..74cb86ea --- /dev/null +++ b/tests/results/interpret/interpret_BF_weak_favor.txt @@ -0,0 +1 @@ +weak evidence in favor of the effect, BF = 1.50 diff --git a/tests/results/interpret/interpret_par_conditional.txt b/tests/results/interpret/interpret_par_conditional.txt new file mode 100644 index 00000000..7a128d77 --- /dev/null +++ b/tests/results/interpret/interpret_par_conditional.txt @@ -0,0 +1 @@ +with mean conditional estimate mu = 0.499, 95% CI [0.302, 0.696] diff --git a/tests/results/interpret/interpret_par_model_averaged.txt b/tests/results/interpret/interpret_par_model_averaged.txt new file mode 100644 index 00000000..99f1399e --- /dev/null +++ b/tests/results/interpret/interpret_par_model_averaged.txt @@ -0,0 +1 @@ +with mean model-averaged estimate mu = 0.499, 95% CI [0.302, 0.696] diff --git a/tests/results/interpret/interpret_par_model_averaged_null.txt b/tests/results/interpret/interpret_par_model_averaged_null.txt new file mode 100644 index 00000000..1f56942f --- /dev/null +++ b/tests/results/interpret/interpret_par_model_averaged_null.txt @@ -0,0 +1 @@ +with mean model-averaged estimate delta = 0.499, 95% CI [0.302, 0.696] diff --git a/tests/results/interpret/interpret_par_with_units.txt b/tests/results/interpret/interpret_par_with_units.txt new file mode 100644 index 00000000..6d97e5a2 --- /dev/null +++ b/tests/results/interpret/interpret_par_with_units.txt @@ -0,0 +1 @@ +with mean model-averaged estimate weight = 0.499 kg, 95% CI [0.302, 0.696] diff --git a/tests/results/model-averaging-edge-cases/weightfunctions_mapping_info.txt b/tests/results/model-averaging-edge-cases/weightfunctions_mapping_info.txt new file mode 100644 index 00000000..423b2e8c --- /dev/null +++ b/tests/results/model-averaging-edge-cases/weightfunctions_mapping_info.txt @@ -0,0 +1,3 @@ +Mixed mapping length: 2 +Inx 1: 2,2,1,1,1 +Inx 2: 3,2,1,2,3 diff --git a/tests/results/model-averaging/ensemble_inference_conditional.txt b/tests/results/model-averaging/ensemble_inference_conditional.txt new file mode 100644 index 00000000..bbcf71e1 --- /dev/null +++ b/tests/results/model-averaging/ensemble_inference_conditional.txt @@ -0,0 +1,2 @@ +Conditional: TRUE +BF: 0.0853 diff --git a/tests/results/model-averaging/ensemble_inference_int_spec.txt b/tests/results/model-averaging/ensemble_inference_int_spec.txt new file mode 100644 index 00000000..3cf75cd6 --- /dev/null +++ b/tests/results/model-averaging/ensemble_inference_int_spec.txt @@ -0,0 +1,4 @@ +BF: 0.0853 +is_null: FALSE, TRUE +prior_probs: 0.5, 0.5 +post_probs: 0.0786, 0.9214 diff --git a/tests/results/model-averaging/mix_posteriors_simple_info.txt b/tests/results/model-averaging/mix_posteriors_simple_info.txt new file mode 100644 index 00000000..00559723 --- /dev/null +++ b/tests/results/model-averaging/mix_posteriors_simple_info.txt @@ -0,0 +1,4 @@ +Class: list, mixed_posteriors +Parameters: m, s +Sample size m: 1000 +Sample size s: 1000 diff --git a/tests/results/model-averaging/models_inference_output.txt b/tests/results/model-averaging/models_inference_output.txt new file mode 100644 index 00000000..c1260620 --- /dev/null +++ b/tests/results/model-averaging/models_inference_output.txt @@ -0,0 +1,9 @@ +Model 1 inference: + m_number: 1 + prior_prob: 0.333333 + post_prob: 0.040927 +Model 2 inference: + m_number: 2 + prior_prob: 0.666667 + post_prob: 0.959073 +Total post_prob: 1 diff --git a/tests/results/summary-tables-helpers/add_column_end.txt b/tests/results/summary-tables-helpers/add_column_end.txt new file mode 100644 index 00000000..c52bce44 --- /dev/null +++ b/tests/results/summary-tables-helpers/add_column_end.txt @@ -0,0 +1,3 @@ + Mean Median SD CI_lower +mu 0.500 0.400 0.100 -0.500 +sigma 1.200 1.100 0.200 0.800 diff --git a/tests/results/summary-tables-helpers/add_column_position1.txt b/tests/results/summary-tables-helpers/add_column_position1.txt new file mode 100644 index 00000000..886a035b --- /dev/null +++ b/tests/results/summary-tables-helpers/add_column_position1.txt @@ -0,0 +1,3 @@ + ID Mean Median SD +mu 1 0.500 0.400 0.100 +sigma 2 1.200 1.100 0.200 diff --git a/tests/results/summary-tables-helpers/add_column_position2.txt b/tests/results/summary-tables-helpers/add_column_position2.txt new file mode 100644 index 00000000..cdd93b8e --- /dev/null +++ b/tests/results/summary-tables-helpers/add_column_position2.txt @@ -0,0 +1,3 @@ + Mean CI_lower Median SD +mu 0.500 -0.500 0.400 0.100 +sigma 1.200 0.800 1.100 0.200 diff --git a/tests/results/summary-tables-helpers/add_column_probability.txt b/tests/results/summary-tables-helpers/add_column_probability.txt new file mode 100644 index 00000000..6538b82e --- /dev/null +++ b/tests/results/summary-tables-helpers/add_column_probability.txt @@ -0,0 +1,3 @@ + Mean Median SD Prob +mu 0.500 0.400 0.100 0.500 +sigma 1.200 1.100 0.200 0.800 diff --git a/tests/results/summary-tables-helpers/add_column_string.txt b/tests/results/summary-tables-helpers/add_column_string.txt new file mode 100644 index 00000000..af2cf317 --- /dev/null +++ b/tests/results/summary-tables-helpers/add_column_string.txt @@ -0,0 +1,3 @@ + Mean Median SD Category +mu 0.500 0.400 0.100 A +sigma 1.200 1.100 0.200 B diff --git a/tests/results/summary-tables-helpers/ensemble_diagnostics_empty.txt b/tests/results/summary-tables-helpers/ensemble_diagnostics_empty.txt new file mode 100644 index 00000000..7332672b --- /dev/null +++ b/tests/results/summary-tables-helpers/ensemble_diagnostics_empty.txt @@ -0,0 +1,2 @@ +[1] Model max[error(MCMC)] max[error(MCMC)/SD] min(ESS) max(R-hat) +<0 rows> (or 0-length row.names) diff --git a/tests/results/summary-tables-helpers/ensemble_estimates_empty.txt b/tests/results/summary-tables-helpers/ensemble_estimates_empty.txt new file mode 100644 index 00000000..2d49d4b8 --- /dev/null +++ b/tests/results/summary-tables-helpers/ensemble_estimates_empty.txt @@ -0,0 +1,2 @@ +[1] Mean Median 0.025 0.95 +<0 rows> (or 0-length row.names) diff --git a/tests/results/summary-tables-helpers/ensemble_inference_empty.txt b/tests/results/summary-tables-helpers/ensemble_inference_empty.txt new file mode 100644 index 00000000..a6e4ded2 --- /dev/null +++ b/tests/results/summary-tables-helpers/ensemble_inference_empty.txt @@ -0,0 +1,2 @@ +[1] Models Prior prob. Post. prob. Inclusion BF +<0 rows> (or 0-length row.names) diff --git a/tests/results/summary-tables-helpers/ensemble_summary_empty.txt b/tests/results/summary-tables-helpers/ensemble_summary_empty.txt new file mode 100644 index 00000000..c836efff --- /dev/null +++ b/tests/results/summary-tables-helpers/ensemble_summary_empty.txt @@ -0,0 +1,2 @@ +[1] Model Prior prob. log(marglik) Post. prob. Inclusion BF +<0 rows> (or 0-length row.names) diff --git a/tests/results/summary-tables-helpers/remove_column_last.txt b/tests/results/summary-tables-helpers/remove_column_last.txt new file mode 100644 index 00000000..01a1f106 --- /dev/null +++ b/tests/results/summary-tables-helpers/remove_column_last.txt @@ -0,0 +1,3 @@ + Mean Median +mu 0.500 0.400 +sigma 1.200 1.100 diff --git a/tests/results/summary-tables-helpers/remove_column_position2.txt b/tests/results/summary-tables-helpers/remove_column_position2.txt new file mode 100644 index 00000000..a0eb98e7 --- /dev/null +++ b/tests/results/summary-tables-helpers/remove_column_position2.txt @@ -0,0 +1,3 @@ + Mean SD +mu 0.500 0.100 +sigma 1.200 0.200 diff --git a/tests/results/summary-tables/ensemble_diagnostics_basic.txt b/tests/results/summary-tables/ensemble_diagnostics_basic.txt new file mode 100644 index 00000000..9f9f098b --- /dev/null +++ b/tests/results/summary-tables/ensemble_diagnostics_basic.txt @@ -0,0 +1,3 @@ + Model Prior m Prior s max[error(MCMC)] max[error(MCMC)/SD] min(ESS) max(R-hat) + 1 Normal(0, 1) Normal(0, 1)[0, Inf] 0.00199 0.043 540 1.005 + 2 Normal(0, 1)[0, Inf] 0.00191 0.044 509 1.002 diff --git a/tests/results/summary-tables/ensemble_diagnostics_no_spike.txt b/tests/results/summary-tables/ensemble_diagnostics_no_spike.txt new file mode 100644 index 00000000..4dff1929 --- /dev/null +++ b/tests/results/summary-tables/ensemble_diagnostics_no_spike.txt @@ -0,0 +1,3 @@ + Model Prior m Prior s max[error(MCMC)] max[error(MCMC)/SD] min(ESS) max(R-hat) + 1 Normal(0, 1) Normal(0, 1)[0, Inf] 0.00199 0.043 540 1.005 + 2 Spike(0) Normal(0, 1)[0, Inf] 0.00191 0.044 509 1.002 diff --git a/tests/results/summary-tables/ensemble_diagnostics_short_name.txt b/tests/results/summary-tables/ensemble_diagnostics_short_name.txt new file mode 100644 index 00000000..a4332cae --- /dev/null +++ b/tests/results/summary-tables/ensemble_diagnostics_short_name.txt @@ -0,0 +1,3 @@ + Model Prior m Prior s max[error(MCMC)] max[error(MCMC)/SD] min(ESS) max(R-hat) + 1 N(0, 1) N(0, 1)[0, Inf] 0.00199 0.043 540 1.005 + 2 N(0, 1)[0, Inf] 0.00191 0.044 509 1.002 diff --git a/tests/results/summary-tables/ensemble_estimates_basic.txt b/tests/results/summary-tables/ensemble_estimates_basic.txt new file mode 100644 index 00000000..8410bd59 --- /dev/null +++ b/tests/results/summary-tables/ensemble_estimates_basic.txt @@ -0,0 +1,4 @@ + Mean Median 0.025 0.95 +m 0.173 0.179 -0.221 0.494 +omega[0,0.05] 1.000 1.000 1.000 1.000 +omega[0.05,1] 0.692 0.834 0.031 1.000 diff --git a/tests/results/summary-tables/ensemble_estimates_custom_probs.txt b/tests/results/summary-tables/ensemble_estimates_custom_probs.txt new file mode 100644 index 00000000..43c9431b --- /dev/null +++ b/tests/results/summary-tables/ensemble_estimates_custom_probs.txt @@ -0,0 +1,4 @@ + Mean Median 0.1 0.5 0.9 +m 0.173 0.179 -0.107 0.179 0.427 +omega[0,0.05] 1.000 1.000 1.000 1.000 1.000 +omega[0.05,1] 0.692 0.834 0.137 0.834 1.000 diff --git a/tests/results/summary-tables/ensemble_estimates_formula_prefix_false.txt b/tests/results/summary-tables/ensemble_estimates_formula_prefix_false.txt new file mode 100644 index 00000000..c2511e92 --- /dev/null +++ b/tests/results/summary-tables/ensemble_estimates_formula_prefix_false.txt @@ -0,0 +1,3 @@ + Mean Median 0.025 0.95 +intercept 0.514 0.512 0.355 0.650 +sigma 0.886 0.885 0.783 0.982 diff --git a/tests/results/summary-tables/ensemble_estimates_formula_prefix_true.txt b/tests/results/summary-tables/ensemble_estimates_formula_prefix_true.txt new file mode 100644 index 00000000..7aa34c53 --- /dev/null +++ b/tests/results/summary-tables/ensemble_estimates_formula_prefix_true.txt @@ -0,0 +1,3 @@ + Mean Median 0.025 0.95 +(mu) intercept 0.514 0.512 0.355 0.650 +sigma 0.886 0.885 0.783 0.982 diff --git a/tests/results/summary-tables/ensemble_estimates_transform_factors.txt b/tests/results/summary-tables/ensemble_estimates_transform_factors.txt new file mode 100644 index 00000000..0fd17956 --- /dev/null +++ b/tests/results/summary-tables/ensemble_estimates_transform_factors.txt @@ -0,0 +1,4 @@ + Mean Median 0.025 0.95 +(mu) x_fac3o [dif: A] 0.023 0.020 -0.185 0.188 +(mu) x_fac3o [dif: B] -0.306 -0.322 -0.520 0.000 +(mu) x_fac3o [dif: C] 0.282 0.289 0.000 0.476 diff --git a/tests/results/summary-tables/ensemble_inference_BF01.txt b/tests/results/summary-tables/ensemble_inference_BF01.txt new file mode 100644 index 00000000..2e884efc --- /dev/null +++ b/tests/results/summary-tables/ensemble_inference_BF01.txt @@ -0,0 +1,3 @@ + Models Prior prob. Post. prob. Exclusion BF +m 2/2 1.000 1.000 0.000 +omega 1/2 0.500 0.638 0.568 diff --git a/tests/results/summary-tables/ensemble_inference_basic.txt b/tests/results/summary-tables/ensemble_inference_basic.txt new file mode 100644 index 00000000..218ac8c8 --- /dev/null +++ b/tests/results/summary-tables/ensemble_inference_basic.txt @@ -0,0 +1,3 @@ + Models Prior prob. Post. prob. Inclusion BF +m 2/2 1.000 1.000 Inf +omega 1/2 0.500 0.638 1.759 diff --git a/tests/results/summary-tables/ensemble_inference_both.txt b/tests/results/summary-tables/ensemble_inference_both.txt new file mode 100644 index 00000000..f02847b7 --- /dev/null +++ b/tests/results/summary-tables/ensemble_inference_both.txt @@ -0,0 +1,3 @@ + Models Prior prob. Post. prob. log(Exclusion BF) +m 2/2 1.000 1.000 -Inf +omega 1/2 0.500 0.638 -0.565 diff --git a/tests/results/summary-tables/ensemble_inference_logBF.txt b/tests/results/summary-tables/ensemble_inference_logBF.txt new file mode 100644 index 00000000..f4dd627d --- /dev/null +++ b/tests/results/summary-tables/ensemble_inference_logBF.txt @@ -0,0 +1,3 @@ + Models Prior prob. Post. prob. log(Inclusion BF) +m 2/2 1.000 1.000 Inf +omega 1/2 0.500 0.638 0.565 diff --git a/tests/results/summary-tables/ensemble_summary_basic.txt b/tests/results/summary-tables/ensemble_summary_basic.txt new file mode 100644 index 00000000..b5b3576b --- /dev/null +++ b/tests/results/summary-tables/ensemble_summary_basic.txt @@ -0,0 +1,3 @@ + Model Prior m Prior s Prior prob. log(marglik) Post. prob. Inclusion BF + 1 Normal(0, 1) Normal(0, 1)[0, Inf] 0.500 -31.95 0.079 0.085 + 2 Normal(0, 1)[0, Inf] 0.500 -29.49 0.921 11.717 diff --git a/tests/results/summary-tables/ensemble_summary_bf_options.txt b/tests/results/summary-tables/ensemble_summary_bf_options.txt new file mode 100644 index 00000000..0dd2becd --- /dev/null +++ b/tests/results/summary-tables/ensemble_summary_bf_options.txt @@ -0,0 +1,3 @@ + Model Prior m Prior s Prior prob. log(marglik) Post. prob. log(Exclusion BF) + 1 Normal(0, 1) Normal(0, 1)[0, Inf] 0.500 -31.95 0.079 2.461 + 2 Normal(0, 1)[0, Inf] 0.500 -29.49 0.921 -2.461 diff --git a/tests/results/summary-tables/ensemble_summary_no_spike.txt b/tests/results/summary-tables/ensemble_summary_no_spike.txt new file mode 100644 index 00000000..c5be7158 --- /dev/null +++ b/tests/results/summary-tables/ensemble_summary_no_spike.txt @@ -0,0 +1,3 @@ + Model Prior m Prior s Prior prob. log(marglik) Post. prob. Inclusion BF + 1 Normal(0, 1) Normal(0, 1)[0, Inf] 0.500 -31.95 0.079 0.085 + 2 Spike(0) Normal(0, 1)[0, Inf] 0.500 -29.49 0.921 11.717 diff --git a/tests/results/summary-tables/ensemble_summary_params_list.txt b/tests/results/summary-tables/ensemble_summary_params_list.txt new file mode 100644 index 00000000..61831666 --- /dev/null +++ b/tests/results/summary-tables/ensemble_summary_params_list.txt @@ -0,0 +1,3 @@ + Model Prior m Prior renamed 2 Prior prob. log(marglik) Post. prob. Inclusion BF + 1 Normal(0, 1) Normal(0, 1)[0, Inf] 0.500 -31.95 0.079 0.085 + 2 Normal(0, 1)[0, Inf] 0.500 -29.49 0.921 11.717 diff --git a/tests/results/summary-tables/ensemble_summary_short_name.txt b/tests/results/summary-tables/ensemble_summary_short_name.txt new file mode 100644 index 00000000..9043a080 --- /dev/null +++ b/tests/results/summary-tables/ensemble_summary_short_name.txt @@ -0,0 +1,3 @@ + Model Prior m Prior s Prior prob. log(marglik) Post. prob. Inclusion BF + 1 N(0, 1) N(0, 1)[0, Inf] 0.500 -31.95 0.079 0.085 + 2 N(0, 1)[0, Inf] 0.500 -29.49 0.921 11.717 diff --git a/tests/results/summary-tables/marginal_estimates_BF01.txt b/tests/results/summary-tables/marginal_estimates_BF01.txt new file mode 100644 index 00000000..927d83ee --- /dev/null +++ b/tests/results/summary-tables/marginal_estimates_BF01.txt @@ -0,0 +1,2 @@ + Mean Median 0.025 0.95 Inclusion BF +mu[] -0.626 -0.626 -0.626 -0.626 0.400 diff --git a/tests/results/summary-tables/marginal_estimates_basic.txt b/tests/results/summary-tables/marginal_estimates_basic.txt new file mode 100644 index 00000000..2a4141a0 --- /dev/null +++ b/tests/results/summary-tables/marginal_estimates_basic.txt @@ -0,0 +1,2 @@ + Mean Median 0.025 0.95 Inclusion BF +mu[] -0.626 -0.626 -0.626 -0.626 2.500 diff --git a/tests/results/summary-tables/marginal_estimates_logBF.txt b/tests/results/summary-tables/marginal_estimates_logBF.txt new file mode 100644 index 00000000..48e8e35a --- /dev/null +++ b/tests/results/summary-tables/marginal_estimates_logBF.txt @@ -0,0 +1,2 @@ + Mean Median 0.025 0.95 Inclusion BF +mu[] -0.626 -0.626 -0.626 -0.626 0.916 diff --git a/tests/results/summary-tables/model_summary_basic.txt b/tests/results/summary-tables/model_summary_basic.txt new file mode 100644 index 00000000..acc0bb88 --- /dev/null +++ b/tests/results/summary-tables/model_summary_basic.txt @@ -0,0 +1,6 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 s ~ Normal(0, 1)[0, Inf] + log(marglik) -29.49 + Post. prob. 1.000 + Inclusion BF Inf diff --git a/tests/results/summary-tables/model_summary_no_spike.txt b/tests/results/summary-tables/model_summary_no_spike.txt new file mode 100644 index 00000000..acc0bb88 --- /dev/null +++ b/tests/results/summary-tables/model_summary_no_spike.txt @@ -0,0 +1,6 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 s ~ Normal(0, 1)[0, Inf] + log(marglik) -29.49 + Post. prob. 1.000 + Inclusion BF Inf diff --git a/tests/results/summary-tables/model_summary_short_name.txt b/tests/results/summary-tables/model_summary_short_name.txt new file mode 100644 index 00000000..ef7083e1 --- /dev/null +++ b/tests/results/summary-tables/model_summary_short_name.txt @@ -0,0 +1,6 @@ + + Model 1 Parameter prior distributions + Prior prob. 1.000 s ~ N(0, 1)[0, Inf] + log(marglik) -29.49 + Post. prob. 1.000 + Inclusion BF Inf diff --git a/tests/results/summary-tables/update_table_BF01.txt b/tests/results/summary-tables/update_table_BF01.txt new file mode 100644 index 00000000..2e884efc --- /dev/null +++ b/tests/results/summary-tables/update_table_BF01.txt @@ -0,0 +1,3 @@ + Models Prior prob. Post. prob. Exclusion BF +m 2/2 1.000 1.000 0.000 +omega 1/2 0.500 0.638 0.568 diff --git a/tests/results/summary-tables/update_table_footnotes.txt b/tests/results/summary-tables/update_table_footnotes.txt new file mode 100644 index 00000000..54a6071a --- /dev/null +++ b/tests/results/summary-tables/update_table_footnotes.txt @@ -0,0 +1,4 @@ + Models Prior prob. Post. prob. Inclusion BF +m 2/2 1.000 1.000 Inf +omega 1/2 0.500 0.638 1.759 +This is a footnote diff --git a/tests/results/summary-tables/update_table_logBF.txt b/tests/results/summary-tables/update_table_logBF.txt new file mode 100644 index 00000000..f4dd627d --- /dev/null +++ b/tests/results/summary-tables/update_table_logBF.txt @@ -0,0 +1,3 @@ + Models Prior prob. Post. prob. log(Inclusion BF) +m 2/2 1.000 1.000 Inf +omega 1/2 0.500 0.638 0.565 diff --git a/tests/results/summary-tables/update_table_new_title.txt b/tests/results/summary-tables/update_table_new_title.txt new file mode 100644 index 00000000..3103bf81 --- /dev/null +++ b/tests/results/summary-tables/update_table_new_title.txt @@ -0,0 +1,4 @@ +Updated Title + Models Prior prob. Post. prob. Inclusion BF +m 2/2 1.000 1.000 Inf +omega 1/2 0.500 0.638 1.759 diff --git a/tests/results/summary-tables/update_table_warnings.txt b/tests/results/summary-tables/update_table_warnings.txt new file mode 100644 index 00000000..70b177c0 --- /dev/null +++ b/tests/results/summary-tables/update_table_warnings.txt @@ -0,0 +1,4 @@ + Models Prior prob. Post. prob. Inclusion BF +m 2/2 1.000 1.000 Inf +omega 1/2 0.500 0.638 1.759 +This is a warning diff --git a/tests/testthat/_snaps/JAGS-diagnostic-plots/diagnostics-ggplot-autocorrelation-1.svg b/tests/testthat/_snaps/JAGS-diagnostic-plots/diagnostics-ggplot-autocorrelation-1.svg new file mode 100644 index 00000000..f32fd264 --- /dev/null +++ b/tests/testthat/_snaps/JAGS-diagnostic-plots/diagnostics-ggplot-autocorrelation-1.svg @@ -0,0 +1,149 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0.0 +0.2 +0.4 +0.6 +0.8 +1.0 + + + + + + + + + + + + + +0 +5 +10 +15 +20 +25 +30 +Lag +Autocorrelation(x_cont1) +Normal +(0, 1) + + diff --git a/tests/testthat/_snaps/JAGS-diagnostic-plots/diagnostics-ggplot-autocorrelation-2-1.svg b/tests/testthat/_snaps/JAGS-diagnostic-plots/diagnostics-ggplot-autocorrelation-2-1.svg new file mode 100644 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a/tests/testthat/_snaps/JAGS-fit/jags-model-prior-e2.svg +++ /dev/null @@ -1,101 +0,0 @@ - - - - - - - - - - - - -Normal -(0, x_sigma) - -* - -Spike -(0.5) -x_samples[abs(x_samples) < 10] -Density - - - - - - --10 --5 -0 -5 -10 - - - - - - -0.0 -0.1 -0.2 -0.3 -0.4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/JAGS-fit/jags-model-prior-e3.svg b/tests/testthat/_snaps/JAGS-fit/jags-model-prior-e3.svg deleted file mode 100644 index 8db9a15c..00000000 --- a/tests/testthat/_snaps/JAGS-fit/jags-model-prior-e3.svg +++ /dev/null @@ -1,123 +0,0 @@ - - - - - - - - - - - - -( -1 -/ -2 -) - -* - -Normal -(0, x_sigma) - -+ - -( -1 -/ -2 -) - -* - -Cauchy -(0, 1) -x_samples[abs(x_samples) < 10] -Density - - - - - - --10 --5 -0 -5 -10 - - - - - - - - - -0.00 -0.05 -0.10 -0.15 -0.20 -0.25 -0.30 -0.35 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git 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- - - -Histogram of samples[, paste0("omega[", j, "]")] -samples[, paste0("omega[", j, "]")] -Density - - - - - - - - - -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 - - - - - - - - - - - - - - - - - - - - - - -Histogram of samples[, paste0("omega[", j, "]")] -samples[, paste0("omega[", j, "]")] -Density - - - - - - - - -0.00 -0.05 -0.10 -0.15 -0.20 - - - - - - - -0 -1 -2 -3 -4 -5 - - - - - - - - - - - - - - - - - -Histogram of samples[, paste0("omega[", j, "]")] -samples[, paste0("omega[", j, "]")] -Density - - - - - - - - - -0.0 -0.2 -0.4 -0.6 -0.8 -1.0 - - - - - - - -0.0 -0.2 -0.4 -0.6 -0.8 -1.0 - - - - - diff --git a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-1.svg b/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-1.svg deleted file mode 100644 index aedb81a5..00000000 --- a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-1.svg +++ /dev/null @@ -1,202 +0,0 @@ - - - - - - - - - - - - - - - - - - - -Intercept -samples_1[, "mu_intercept"] -Density - - - - - - - -0.0 -0.2 -0.4 -0.6 - - - - - - -0 -1 -2 -3 -4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_cont1 -samples_1[, "mu_x_cont1"] -Density - - - - - - - - --0.2 -0.0 -0.2 -0.4 -0.6 - - - - - - -0 -1 -2 -3 -4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -sigma -samples_1[, "sigma"] -Density - - - - - - - - - -0.5 -0.6 -0.7 -0.8 -0.9 -1.0 - - - - - - - - -0 -1 -2 -3 -4 -5 -6 - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-10.svg b/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-10.svg deleted file mode 100644 index 7523e217..00000000 --- a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-10.svg +++ /dev/null @@ -1,386 +0,0 @@ - - - - - - - - - - - - - - - - - - - -Intercept -samples_10[, "mu_intercept"] -Density - - - - - - - - -0.4 -0.6 -0.8 -1.0 -1.2 - - - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 -2.5 -3.0 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_fac3t -samples_10[, "mu_x_fac3t[1]"] -Density - - - - - - - - - - --1.4 --1.2 --1.0 --0.8 --0.6 --0.4 --0.2 - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_fac3t -samples_10[, "mu_x_fac3t[2]"] -Density - - - - - - - --1.5 --1.0 --0.5 -0.0 - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_cont1 -samples_10[, "mu_x_cont1"] -Density - - - - - - - - - --0.6 --0.4 --0.2 -0.0 -0.2 -0.4 - - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 -2.5 - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_cont1:x_fac3t -samples_10[, "mu_x_cont1__xXx__x_fac3t[1]"] -Density - - - - - - - -0.0 -0.5 -1.0 -1.5 - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_cont1:x_fac3t -samples_10[, "mu_x_cont1__xXx__x_fac3t[2]"] -Density - - - - - - -0.0 -0.5 -1.0 - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-11.svg b/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-11.svg deleted file mode 100644 index d06ad535..00000000 --- a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-11.svg +++ /dev/null @@ -1,203 +0,0 @@ - - - - - - - - - - - - - - - - - - - -x_fac3i[1] -samples_11[, "mu_x_fac3i[1]"] -Density - - - - - - - - - -0.2 -0.4 -0.6 -0.8 -1.0 -1.2 - - - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 -2.5 -3.0 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_fac3i[2] -samples_11[, "mu_x_fac3i[2]"] -Density - - - - - - - - - --0.6 --0.4 --0.2 -0.0 -0.2 -0.4 - - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 -2.5 - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_fac3i[3] -samples_11[, "mu_x_fac3i[3]"] -Density - - - - - - - - - - --0.4 --0.2 -0.0 -0.2 -0.4 -0.6 -0.8 - - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 -2.5 - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-12.svg b/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-12.svg deleted file mode 100644 index 74bc2b01..00000000 --- a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-12.svg +++ /dev/null @@ -1,215 +0,0 @@ - - - - - - - - - - - - - - - - - - - -Intercept -samples_12[, "mu_intercept"] -Density - - - - - - - - - - -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 - - - - - - - -0 -1 -2 -3 -4 -5 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_fac3md -samples_12[, "mu_x_fac3md[1]"] -Density - - - - - - - --0.2 -0.0 -0.2 -0.4 - - - - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 -2.5 -3.0 -3.5 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_fac3md -samples_12[, "mu_x_fac3md[2]"] -Density - - - - - - - - - -0.0 -0.2 -0.4 -0.6 -0.8 -1.0 - - - - - -0 -1 -2 -3 - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-13.svg b/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-13.svg deleted file mode 100644 index e8bda532..00000000 --- a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-13.svg +++ /dev/null @@ -1,164 +0,0 @@ - - - - - - - - - - - - - - - - - - - -x_fac3i[1] -samples_13[, "mu_x_fac3i[1]"] -Density - - - - - - - - - -1.0 -1.2 -1.4 -1.6 -1.8 -2.0 - - - - - - - -0.0 -0.2 -0.4 -0.6 -0.8 -1.0 - - - - - - - - - - - - - - - - - - - - - - -x_fac3i[2] -samples_13[, "mu_x_fac3i[2]"] -Density - - - - - - - - - -1.0 -1.2 -1.4 -1.6 -1.8 -2.0 - - - - - - - -0.0 -0.2 -0.4 -0.6 -0.8 -1.0 - - - - - - - - - - - - - - - - - -x_fac3i[3] -samples_13[, "mu_x_fac3i[3]"] -Density - - - - - - - - - -1.0 -1.2 -1.4 -1.6 -1.8 -2.0 - - - - - - - -0.0 -0.2 -0.4 -0.6 -0.8 -1.0 - - - - - diff --git a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-14.svg b/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-14.svg deleted file mode 100644 index 75480fbb..00000000 --- a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-14.svg +++ /dev/null @@ -1,173 +0,0 @@ - - - - - - - - - - - - - - - - - - - -Intercept -samples_14[, "mu_intercept"] -Density - - - - - - - -0.0 -0.2 -0.4 -0.6 - - - - - - -0 -1 -2 -3 -4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_fac3md -samples_14[, "mu_x_fac3md[1]"] -Density - - - - - - - - - --1.0 --0.8 --0.6 --0.4 --0.2 -0.0 - - - - - - - -0.0 -0.2 -0.4 -0.6 -0.8 -1.0 - - - - - - - - - - - - - - - - - -x_fac3md -samples_14[, "mu_x_fac3md[2]"] -Density - - - - - - - - - --1.0 --0.8 --0.6 --0.4 --0.2 -0.0 - - - - - - - -0.0 -0.2 -0.4 -0.6 -0.8 -1.0 - - - - - diff --git a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-1s.svg b/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-1s.svg deleted file mode 100644 index f31c2f7a..00000000 --- a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-1s.svg +++ /dev/null @@ -1,204 +0,0 @@ - - - - - - - - - - - - - - - - - - - -Intercept -samples_1s[, "mu_intercept"] -Density - - - - - - - - --0.2 -0.0 -0.2 -0.4 -0.6 - - - - - - -0 -1 -2 -3 -4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -I(sd(y) * x_cont1) -samples_1s[, "mu_x_cont1"] -Density - - - - - - - - - - - --0.4 --0.2 -0.0 -0.2 -0.4 -0.6 -0.8 -1.0 - - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 -2.5 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -sigma -samples_1s[, "sigma"] -Density - - - - - - - - -0.5 -0.6 -0.7 -0.8 -0.9 - - - - - - - - -0 -1 -2 -3 -4 -5 -6 - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-2.svg b/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-2.svg deleted file mode 100644 index fd99e8ca..00000000 --- a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-2.svg +++ /dev/null @@ -1,202 +0,0 @@ - - - - - - - - - - - - - - - - - - - -x_cont1 -samples_2[, "mu_x_cont1"] -Density - - - - - - - --0.2 -0.0 -0.2 -0.4 - - - - - - -0 -1 -2 -3 -4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_cont2 -samples_2[, "mu_x_cont2"] -Density - - - - - - - - --0.4 --0.2 -0.0 -0.2 -0.4 - - - - - - -0 -1 -2 -3 -4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_cont1:x_cont2 -samples_2[, "mu_x_cont1__xXx__x_cont2"] -Density - - - - - - - - - --0.8 --0.6 --0.4 --0.2 -0.0 -0.2 - - - - - -0 -1 -2 -3 - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-3.svg b/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-3.svg deleted file mode 100644 index 21a17d8e..00000000 --- a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-3.svg +++ /dev/null @@ -1,207 +0,0 @@ - - - - - - - - - - - - - - - - - - - -Intercept -samples_3[, "mu_intercept"] -Density - - - - - - - - - --0.2 -0.0 -0.2 -0.4 -0.6 -0.8 - - - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 -2.5 -3.0 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_fac2t -samples_3[, "mu_x_fac2t"] -Density - - - - - - - - - - - --0.8 --0.6 --0.4 --0.2 -0.0 -0.2 -0.4 -0.6 - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -sigma -samples_3[, "sigma"] -Density - - - - - - - - - -0.5 -0.6 -0.7 -0.8 -0.9 -1.0 - - - - - - - - -0 -1 -2 -3 -4 -5 -6 - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-4.svg b/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-4.svg deleted file mode 100644 index b49f0431..00000000 --- a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-4.svg +++ /dev/null @@ -1,208 +0,0 @@ - - - - - - - - - - - - - - - - - - - -Intercept -samples_4[, "mu_intercept"] -Density - - - - - - - -0.0 -0.2 -0.4 -0.6 - - - - - - -0 -1 -2 -3 -4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_fac2o -samples_4[, "mu_x_fac2o"] -Density - - - - - - - - --0.4 --0.2 -0.0 -0.2 -0.4 - - - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 -2.5 -3.0 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -sigma -samples_4[, "sigma"] -Density - - - - - - - - - -0.5 -0.6 -0.7 -0.8 -0.9 -1.0 - - - - - - - - -0 -1 -2 -3 -4 -5 -6 - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-5.svg b/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-5.svg deleted file mode 100644 index 4a7c9ef2..00000000 --- a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-5.svg +++ /dev/null @@ -1,205 +0,0 @@ - - - - - - - - - - - - - - - - - - - -Intercept -samples_5[, "mu_intercept"] -Density - - - - - - - - - -0.2 -0.4 -0.6 -0.8 -1.0 -1.2 - - - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 -2.5 -3.0 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_fac3t[1] -samples_5[, "mu_x_fac3t[1]"] -Density - - - - - - - --1.5 --1.0 --0.5 -0.0 - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_fac3t[2] -samples_5[, "mu_x_fac3t[2]"] -Density - - - - - - --1.0 --0.5 -0.0 - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-6.svg b/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-6.svg deleted file mode 100644 index 6e8e6d97..00000000 --- a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-6.svg +++ /dev/null @@ -1,213 +0,0 @@ - - - - - - - - - - - - - - - - - - - -Intercept -samples_6[, "mu_intercept"] -Density - - - - - - - - - - - --0.1 -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 - - - - - - - -0 -1 -2 -3 -4 -5 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_fac3o -samples_6[, "mu_x_fac3o[1]"] -Density - - - - - - - - - - --0.4 --0.2 -0.0 -0.2 -0.4 -0.6 -0.8 - - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 -2.5 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_fac3o -samples_6[, "mu_x_fac3o[2]"] -Density - - - - - - - - - - -0.0 -0.2 -0.4 -0.6 -0.8 -1.0 -1.2 - - - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 -2.5 -3.0 - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-7.svg b/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-7.svg deleted file mode 100644 index 3a4f108c..00000000 --- a/tests/testthat/_snaps/JAGS-formula/jags-formula-lm-7.svg +++ /dev/null @@ -1,383 +0,0 @@ - - - - - - - - - - - - - - - - - - - -Intercept -samples_7[, "mu_intercept"] -Density - - - - - - - - -0.0 -0.2 -0.4 -0.6 -0.8 - - - - - -0 -1 -2 -3 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_fac3o -samples_7[, "mu_x_fac3o[1]"] -Density - - - - - - - - - - --0.4 --0.2 -0.0 -0.2 -0.4 -0.6 -0.8 - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_fac3o -samples_7[, "mu_x_fac3o[2]"] -Density - - - - - - -0.0 -0.5 -1.0 - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_fac2t -samples_7[, "mu_x_fac2t"] -Density - - - - - - - --1.0 --0.5 -0.0 -0.5 - - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 -2.5 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_fac2t:x_fac3o -samples_7[, "mu_x_fac2t__xXx__x_fac3o[1]"] 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -175,17 +175,15 @@ 0 5 10 - + - - - - + + + 0.0 -0.1 -0.2 -0.3 -0.4 +0.1 +0.2 +0.3 @@ -213,39 +211,39 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -299,102 +297,102 @@ 0 5 10 - + - - - - - - + + + + + + 0.00 -0.05 -0.10 -0.15 -0.20 -0.25 -0.30 +0.05 +0.10 +0.15 +0.20 +0.25 +0.30 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-con.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-con.svg new file mode 100644 index 00000000..ac83bf89 --- /dev/null +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-con.svg @@ -0,0 +1,200 @@ + + + + + + + + + + + + + + + + + + + +marginal posterior x_cont1 +(-1) +marg_post_x_cont1[["-1SD"]] +Density + + + + + + + + +0.2 +0.3 +0.4 +0.5 +0.6 + + + + + + + + +0 +1 +2 +3 +4 +5 +6 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +marginal posterior x_cont1 +(0) +marg_post_x_cont1[["0SD"]] +Density + + + + + + + +0.5 +0.6 +0.7 +0.8 + + + + + + +0 +2 +4 +6 +8 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +marginal posterior x_cont1 +(1) +marg_post_x_cont1[["1SD"]] +Density + + + + + + + + +0.6 +0.7 +0.8 +0.9 +1.0 + + + + + + + + +0 +1 +2 +3 +4 +5 +6 + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-fac-md-at.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-fac-md-at.svg new file mode 100644 index 00000000..c5009759 --- /dev/null +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-fac-md-at.svg @@ -0,0 +1,398 @@ + + + + + + + + + + + + + + + + + + + +marg_post_x_fac3md = A | 1,A +marg_post_x_fac3md_AT[["A"]][1, ] +Density + + + + + + + + + + + +0.6 +0.7 +0.8 +0.9 +1.0 +1.1 +1.2 +1.3 + + + + + + +0 +1 +2 +3 +4 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +marg_post_x_fac3md = A | 1,B +marg_post_x_fac3md_AT[["A"]][2, ] +Density + + + + + + + + + + + +0.6 +0.7 +0.8 +0.9 +1.0 +1.1 +1.2 +1.3 + + + + + + +0 +1 +2 +3 +4 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +marg_post_x_fac3md = B | 1,A +marg_post_x_fac3md_AT[["B"]][1, ] +Density + + + + + + + + + + +0.4 +0.5 +0.6 +0.7 +0.8 +0.9 +1.0 + + + + + + +0 +1 +2 +3 +4 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +marg_post_x_fac3md = B | 1,B +marg_post_x_fac3md_AT[["B"]][2, ] +Density + + + + + + + + + + +0.4 +0.5 +0.6 +0.7 +0.8 +0.9 +1.0 + + + + + + +0 +1 +2 +3 +4 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +marg_post_x_fac3md = C | 1,A +marg_post_x_fac3md_AT[["C"]][1, ] +Density + + + + + + + + + + + +0.4 +0.5 +0.6 +0.7 +0.8 +0.9 +1.0 +1.1 + + + + + + + +0 +1 +2 +3 +4 +5 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +marg_post_x_fac3md = C | 1,B +marg_post_x_fac3md_AT[["C"]][2, ] +Density + + + + + + + +0.4 +0.6 +0.8 +1.0 + + + + + + + +0 +1 +2 +3 +4 +5 + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-md-p.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-fac-md-p.svg similarity index 85% rename from tests/testthat/_snaps/marginal-distributions/marginal-form-fac-md-p.svg rename to tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-fac-md-p.svg index 2717efd1..6ec27ca8 100644 --- a/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-md-p.svg +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-fac-md-p.svg @@ -82,39 +82,39 @@ - - - + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -206,40 +206,40 @@ - - - + + + - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + - - - - + + + + - + - + @@ -330,40 +330,40 @@ - - - - + + + + - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + - + - + - - - + + + diff --git a/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-fac-md.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-fac-md.svg new file mode 100644 index 00000000..4bc899e3 --- /dev/null +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-fac-md.svg @@ -0,0 +1,239 @@ + + + + + + + + + + + + + + + + + + + +marg_post_x_fac3md = A +marg_post_x_fac3md[["A"]] +Density + + + + + + + + + +0.5 +0.6 +0.7 +0.8 +0.9 +1.0 + + + + + + + + +0 +1 +2 +3 +4 +5 +6 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +marg_post_x_fac3md = B +marg_post_x_fac3md[["B"]] +Density + + + + + + + + + +0.3 +0.4 +0.5 +0.6 +0.7 +0.8 + + + + + + + + + +0 +1 +2 +3 +4 +5 +6 +7 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +marg_post_x_fac2t = B +marg_post_x_fac3md[["C"]] +Density + + + + + + + +0.4 +0.5 +0.6 +0.7 + + + + + + +0 +2 +4 +6 +8 + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-mdi-p.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-fac-mdi-p.svg similarity index 72% rename from tests/testthat/_snaps/marginal-distributions/marginal-form-fac-mdi-p.svg rename to tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-fac-mdi-p.svg index 4e616ae0..902b9df4 100644 --- a/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-mdi-p.svg +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-fac-mdi-p.svg @@ -60,86 +60,86 @@ - + - - + + - - - - - - - - + + + + + + + + - - + + - - - + + + - - - - + + + + - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + - - - - - - - + + + + + + + @@ -187,84 +187,84 @@ - + - - - + + + - - - - - - - - - - - + + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - + + + + + + + + + + + + + - - + + - + - - - - - + + + + + - + @@ -310,86 +310,86 @@ 0.4 - - - - - - - - + + + + + + + + - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - + + + + + + + + + + + @@ -460,39 +460,39 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + @@ -583,38 +583,38 @@ - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -710,35 +710,35 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -813,81 +813,81 @@ - - - - + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - - - - + + + + - - - - - - + + + + + + @@ -935,86 +935,86 @@ 0.4 - + - - - - + + + + - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - + + + + + + - + - - - - + + + + @@ -1060,85 +1060,85 @@ 0.4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - + + + + + + + + + + + + - - - - - - - - - - - - - + + + + + + + + + + + + + - - - + + + diff --git a/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-fac-mdi.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-fac-mdi.svg new file mode 100644 index 00000000..f40a3bea --- /dev/null +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-fac-mdi.svg @@ -0,0 +1,620 @@ + + + + + + + + + + + + + + + + + + + +x_cont1 = -1 +marg_post_x_fac3md = A +marg_post_x_cont1__xXx__x_fac3md[["-1SD, A"]] +Density + + + + + + + +0.2 +0.4 +0.6 +0.8 + + + + + + +0 +1 +2 +3 +4 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +x_cont1 = -1 +marg_post_x_fac3md = B +marg_post_x_cont1__xXx__x_fac3md[["-1SD, B"]] +Density + + + + + + + +0.0 +0.2 +0.4 +0.6 + + + + + + + +0 +1 +2 +3 +4 +5 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +x_cont1 = -1 +marg_post_x_fac3md = B +marg_post_x_cont1__xXx__x_fac3md[["-1SD, C"]] +Density + + + + + + + + + + +0.1 +0.2 +0.3 +0.4 +0.5 +0.6 +0.7 + + + + + + + +0 +1 +2 +3 +4 +5 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +x_cont1 = 0 +marg_post_x_fac3md = A +marg_post_x_cont1__xXx__x_fac3md[["0SD, A"]] +Density + + + + + + + + + +0.5 +0.6 +0.7 +0.8 +0.9 +1.0 + + + + + + + + +0 +1 +2 +3 +4 +5 +6 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +x_cont1 = 0 +marg_post_x_fac3md = B +marg_post_x_cont1__xXx__x_fac3md[["0SD, B"]] +Density + + + + + + + + + +0.3 +0.4 +0.5 +0.6 +0.7 +0.8 + + + + + + + + + +0 +1 +2 +3 +4 +5 +6 +7 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +x_cont1 = 0 +marg_post_x_fac3md = B +marg_post_x_cont1__xXx__x_fac3md[["0SD, C"]] +Density + + + + + + + +0.4 +0.5 +0.6 +0.7 + + + + + + +0 +2 +4 +6 +8 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +x_cont1 = 1 +marg_post_x_fac3md = A +marg_post_x_cont1__xXx__x_fac3md[["1SD, A"]] +Density + + + + + + + + + + + +0.6 +0.7 +0.8 +0.9 +1.0 +1.1 +1.2 +1.3 + + + + + + +0 +1 +2 +3 +4 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +x_cont1 = 1 +marg_post_x_fac3md = B +marg_post_x_cont1__xXx__x_fac3md[["1SD, B"]] +Density + + + + + + + + + + +0.4 +0.5 +0.6 +0.7 +0.8 +0.9 +1.0 + + + + + + +0 +1 +2 +3 +4 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +x_cont1 = 1 +marg_post_x_fac3md = B +marg_post_x_cont1__xXx__x_fac3md[["1SD, C"]] +Density + + + + + + + + + + + +0.4 +0.5 +0.6 +0.7 +0.8 +0.9 +1.0 +1.1 + + + + + + + +0 +1 +2 +3 +4 +5 + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-t-p.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-fac-t-p.svg similarity index 83% rename from tests/testthat/_snaps/marginal-distributions/marginal-form-fac-t-p.svg rename to tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-fac-t-p.svg index dc13d159..63516651 100644 --- a/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-t-p.svg +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-fac-t-p.svg @@ -84,36 +84,36 @@ - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + - - - - + + + + @@ -200,51 +200,51 @@ - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - + + + + + + diff --git a/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-fac-t.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-fac-t.svg new file mode 100644 index 00000000..b9391ae8 --- /dev/null +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-fac-t.svg @@ -0,0 +1,146 @@ + + + + + + + + + + + + + + + + + + + +marg_post_x_fac2t = A +marg_post_x_fac2t[["A"]] +Density + + + + + + + +0.5 +0.6 +0.7 +0.8 + + + + + + +0 +2 +4 +6 +8 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +marg_post_x_fac2t = B +marg_post_x_fac2t[["B"]] +Density + + + + + + + +0.5 +0.6 +0.7 +0.8 + + + + + + + +0 +2 +4 +6 +8 +10 + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-int-p.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-int-p.svg new file mode 100644 index 00000000..a38afc3c --- /dev/null +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-int-p.svg @@ -0,0 +1,69 @@ + + + + + + + + + + + + +marginal prior intercept +attr(marg_post_int[["intercept"]], "prior_samples") +Density + + + + + + +-4 +-2 +0 +2 +4 + + + + + +0.0 +0.1 +0.2 +0.3 + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-int.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-int.svg new file mode 100644 index 00000000..821faebf --- /dev/null +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-form-int.svg @@ -0,0 +1,72 @@ + + + + + + + + + + + + +marginal posterior intercept +marg_post_int[["intercept"]] +Density + + + + + +0.5 +0.6 +0.7 +0.8 + + + + + + +0 +2 +4 +6 +8 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-cont-p.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-cont-p.svg new file mode 100644 index 00000000..b1a985c3 --- /dev/null +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-cont-p.svg @@ -0,0 +1,392 @@ + + + + + + + + + + + + + + + + + + + +mu_x_cont1 = -1SD +attr(marg_post_x_cont1[["-1SD"]], "prior_samples") +Density + + + + + + + + +-4 +-2 +0 +2 +4 + + + + + + +0.0 +0.1 +0.2 +0.3 +0.4 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +mu_x_cont1 = 0SD +attr(marg_post_x_cont1[["0SD"]], "prior_samples") +Density + + + + + + + + +-4 +-2 +0 +2 +4 + + + + + + +0.0 +0.1 +0.2 +0.3 +0.4 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +mu_x_cont1 = 1SD +attr(marg_post_x_cont1[["1SD"]], "prior_samples") +Density + + + + + + + + +-4 +-2 +0 +2 +4 + + + + + + +0.0 +0.1 +0.2 +0.3 +0.4 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-cont.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-cont.svg new file mode 100644 index 00000000..9f4b1b99 --- /dev/null +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-cont.svg @@ -0,0 +1,227 @@ + + + + + + + + + + + + + + + + + + + +mu_x_cont1 = -1SD +marg_post_x_cont1[["-1SD"]] +Density + + + + + + + + +0.2 +0.3 +0.4 +0.5 +0.6 + + + + + + + + + +0 +1 +2 +3 +4 +5 +6 +7 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +mu_x_cont1 = 0SD +marg_post_x_cont1[["0SD"]] +Density + + + + + + + +0.5 +0.6 +0.7 +0.8 + + + + + + +0 +2 +4 +6 +8 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +mu_x_cont1 = +1SD +marg_post_x_cont1[["1SD"]] +Density + + + + + + + + +0.6 +0.7 +0.8 +0.9 +1.0 + + + + + + + + +0 +1 +2 +3 +4 +5 +6 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-inference-fac-md-p.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-fac-md-p.svg similarity index 52% rename from tests/testthat/_snaps/marginal-distributions/marginal-inference-fac-md-p.svg rename to tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-fac-md-p.svg index b9ef91d0..9776f219 100644 --- a/tests/testthat/_snaps/marginal-distributions/marginal-inference-fac-md-p.svg +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-fac-md-p.svg @@ -82,39 +82,39 @@ - - - + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -139,7 +139,7 @@ - + @@ -206,40 +206,40 @@ - - - + + + - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + - - - - + + + + - + - + @@ -263,7 +263,7 @@ - + @@ -330,40 +330,40 @@ - - - - + + + + - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + - + - + - - - + + + @@ -387,6 +387,6 @@ - + diff --git a/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-fac-md.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-fac-md.svg new file mode 100644 index 00000000..180e7678 --- /dev/null +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-fac-md.svg @@ -0,0 +1,239 @@ + + + + + + + + + + + + + + + + + + + +marg_post_x_fac3md = A +marg_post_x_fac3md[["A"]] +Density + + + + + + + + + +0.5 +0.6 +0.7 +0.8 +0.9 +1.0 + + + + + + + + +0 +1 +2 +3 +4 +5 +6 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +marg_post_x_fac3md = B +marg_post_x_fac3md[["B"]] +Density + + + + + + + + + +0.3 +0.4 +0.5 +0.6 +0.7 +0.8 + + + + + + + + + +0 +1 +2 +3 +4 +5 +6 +7 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +marg_post_x_fac2t = B +marg_post_x_fac3md[["C"]] +Density + + + + + + + +0.4 +0.5 +0.6 +0.7 + + + + + + +0 +2 +4 +6 +8 + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-ss-cont-p.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-ss-cont-p.svg new file mode 100644 index 00000000..b21da813 --- /dev/null +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-ss-cont-p.svg @@ -0,0 +1,392 @@ + + + + + + + + + + + + + + + + + + + +mu_x_cont1 = -1SD +attr(marg_post_x_cont1[["-1SD"]], "prior_samples") +Density + + + + + + + + +-4 +-2 +0 +2 +4 + + + + + + +0.0 +0.1 +0.2 +0.3 +0.4 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +mu_x_cont1 = 0SD +attr(marg_post_x_cont1[["0SD"]], "prior_samples") +Density + + + + + + + + +-4 +-2 +0 +2 +4 + + + + + + +0.0 +0.1 +0.2 +0.3 +0.4 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +mu_x_cont1 = 1SD +attr(marg_post_x_cont1[["1SD"]], "prior_samples") +Density + + + + + + + + +-4 +-2 +0 +2 +4 + + + + + + +0.0 +0.1 +0.2 +0.3 +0.4 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-ss-cont.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-ss-cont.svg new file mode 100644 index 00000000..fc01536d --- /dev/null +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-ss-cont.svg @@ -0,0 +1,227 @@ + + + + + + + + + + + + + + + + + + + +mu_x_cont1 = -1SD +marg_post_x_cont1[["-1SD"]] +Density + + + + + + + + +0.2 +0.3 +0.4 +0.5 +0.6 + + + + + + + + +0 +1 +2 +3 +4 +5 +6 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +mu_x_cont1 = 0SD +marg_post_x_cont1[["0SD"]] +Density + + + + + + + + +0.50 +0.55 +0.60 +0.65 +0.70 + + + + + + + + +0 +2 +4 +6 +8 +10 +12 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +mu_x_cont1 = +1SD +marg_post_x_cont1[["1SD"]] +Density + + + + + + + + + + +0.65 +0.70 +0.75 +0.80 +0.85 +0.90 +0.95 + + + + + +0 +2 +4 +6 + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-inference-ss-fac-md-p.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-ss-fac-md-p.svg similarity index 53% rename from tests/testthat/_snaps/marginal-distributions/marginal-inference-ss-fac-md-p.svg rename to tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-ss-fac-md-p.svg index f928ba81..a1e63002 100644 --- a/tests/testthat/_snaps/marginal-distributions/marginal-inference-ss-fac-md-p.svg +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-ss-fac-md-p.svg @@ -83,39 +83,39 @@ - - - - + + + + - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + - - + + - - + + @@ -139,7 +139,7 @@ - + @@ -207,39 +207,39 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + @@ -263,7 +263,7 @@ - + @@ -331,39 +331,39 @@ - - - - + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + - + @@ -387,6 +387,6 @@ - + diff --git a/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-ss-fac-md.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-ss-fac-md.svg new file mode 100644 index 00000000..d8b62a9d --- /dev/null +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-inference-ss-fac-md.svg @@ -0,0 +1,225 @@ + + + + + + + + + + + + + + + + + + + +marg_post_x_fac3md = A +marg_post_x_fac3md[["A"]] +Density + + + + + + + + + +0.5 +0.6 +0.7 +0.8 +0.9 +1.0 + + + + + + +0 +1 +2 +3 +4 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +marg_post_x_fac3md = B +marg_post_x_fac3md[["B"]] +Density + + + + + + + +0.4 +0.5 +0.6 +0.7 + + + + + + + +0 +1 +2 +3 +4 +5 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +marg_post_x_fac3md = C +marg_post_x_fac3md[["C"]] +Density + + + + + + + +0.4 +0.5 +0.6 +0.7 + + + + + + + + +0 +1 +2 +3 +4 +5 +6 + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-prior-ind.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-prior-ind.svg similarity index 55% rename from tests/testthat/_snaps/marginal-distributions/marginal-prior-ind.svg rename to tests/testthat/_snaps/JAGS-marginal-distributions/marginal-prior-ind.svg index 1fc61153..1651ca59 100644 --- a/tests/testthat/_snaps/marginal-distributions/marginal-prior-ind.svg +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-prior-ind.svg @@ -30,26 +30,26 @@ Density - - - - - - --1 -0 -1 -2 -3 - + + + + + + +-1 +0 +1 +2 +3 + - - - + + + 0 -1 -2 -3 +1 +2 +3 @@ -57,48 +57,49 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -118,71 +119,71 @@ Density - - - - - - --1 -0 -1 -2 -3 - + + + + + + +-1 +0 +1 +2 +3 + - - - + + + 0 -1 -2 -3 +1 +2 +3 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -213,59 +214,59 @@ 1 2 3 - + - - - + + + 0 -1 -2 -3 +1 +2 +3 - - - - - - - - - + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-prior-trt.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-prior-trt.svg similarity index 56% rename from tests/testthat/_snaps/marginal-distributions/marginal-prior-trt.svg rename to tests/testthat/_snaps/JAGS-marginal-distributions/marginal-prior-trt.svg index a04f5b39..24e8df7a 100644 --- a/tests/testthat/_snaps/marginal-distributions/marginal-prior-trt.svg +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-prior-trt.svg @@ -30,21 +30,21 @@ Density - + - - - - - - + + + + + + 0.0 -0.5 -1.0 -1.5 -2.0 -2.5 -3.0 +0.5 +1.0 +1.5 +2.0 +2.5 +3.0 @@ -65,68 +65,66 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -146,21 +144,21 @@ Density - + - - - - - - + + + + + + 0.0 -0.5 -1.0 -1.5 -2.0 -2.5 -3.0 +0.5 +1.0 +1.5 +2.0 +2.5 +3.0 @@ -176,67 +174,65 @@ 10 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-prior-weightfunction.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-prior-weightfunction.svg similarity index 78% rename from tests/testthat/_snaps/marginal-distributions/marginal-prior-weightfunction.svg rename to tests/testthat/_snaps/JAGS-marginal-distributions/marginal-prior-weightfunction.svg index 1a1bb395..5ab653e6 100644 --- a/tests/testthat/_snaps/marginal-distributions/marginal-prior-weightfunction.svg +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-prior-weightfunction.svg @@ -159,55 +159,55 @@ 25 - - - - - - - - + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -256,55 +256,55 @@ 25 - - - - - - - - + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-simple-con-p.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-simple-con-p.svg new file mode 100644 index 00000000..07feb8b3 --- /dev/null +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-simple-con-p.svg @@ -0,0 +1,80 @@ + + + + + + + + + + + + +marginal prior sigma +attr(marg_post_sigma, "prior_samples") +Density + + + + + + + +0 +1 +2 +3 +4 +5 + + + + + +0.0 +0.2 +0.4 +0.6 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-simple-con.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-simple-con.svg new file mode 100644 index 00000000..6bded049 --- /dev/null +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-simple-con.svg @@ -0,0 +1,70 @@ + + + + + + + + + + + + +marginal posterior sigma +marg_post_sigma +Density + + + + + +0.45 +0.50 +0.55 +0.60 + + + + + +0 +5 +10 +15 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-ss-simple-fac-p.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-simple-fac-p.svg similarity index 77% rename from tests/testthat/_snaps/marginal-distributions/marginal-ss-simple-fac-p.svg rename to tests/testthat/_snaps/JAGS-marginal-distributions/marginal-simple-fac-p.svg index ae612df1..c48e8b86 100644 --- a/tests/testthat/_snaps/marginal-distributions/marginal-ss-simple-fac-p.svg +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-simple-fac-p.svg @@ -98,37 +98,37 @@ 1 2 3 - + - - - - - - + + + + + + 0.0 -0.2 -0.4 -0.6 -0.8 -1.0 -1.2 +0.2 +0.4 +0.6 +0.8 +1.0 +1.2 - - - - - - + + + + + + - - - - - - - - + + + + + + + + diff --git a/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-simple-fac.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-simple-fac.svg new file mode 100644 index 00000000..cffb520d --- /dev/null +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-simple-fac.svg @@ -0,0 +1,138 @@ + + + + + + + + + + + + + + + + + + + +marg_post_x_fac2t = A +marg_post_simple_x_fac2t[["A"]] +Density + + + + + + + + + +-1.0 +-0.8 +-0.6 +-0.4 +-0.2 +0.0 + + + + + + + +0.0 +0.2 +0.4 +0.6 +0.8 +1.0 + + + + + + + + + + + + + + + + + + + + + + +marg_post_x_fac2t = B +marg_post_simple_x_fac2t[["B"]] +Density + + + + + + + + +-0.2 +-0.1 +0.0 +0.1 +0.2 + + + + + + + +0 +5 +10 +15 +20 +25 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-ss-cond-fac.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-cond-fac.svg similarity index 51% rename from tests/testthat/_snaps/marginal-distributions/marginal-ss-cond-fac.svg rename to tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-cond-fac.svg index 6068a767..af62a768 100644 --- a/tests/testthat/_snaps/marginal-distributions/marginal-ss-cond-fac.svg +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-cond-fac.svg @@ -30,34 +30,30 @@ Density - - - - - - - -0.5 -0.6 -0.7 -0.8 -0.9 -1.0 - + + + + + + +0.6 +0.7 +0.8 +0.9 +1.0 + - - - - - - + + + + + 0 -1 -2 -3 -4 -5 -6 +1 +2 +3 +4 +5 @@ -65,17 +61,16 @@ - - - - - - - - - - - + + + + + + + + + + @@ -95,50 +90,43 @@ Density - + - - - + + - - -0.2 -0.3 -0.4 -0.5 + +0.3 +0.4 +0.5 0.6 -0.7 -0.8 - +0.7 + - - - - - - + + + + + + 0 -1 -2 -3 -4 -5 -6 +1 +2 +3 +4 +5 +6 - - - - - - - - - - - - + + + + + + + + + @@ -171,31 +159,31 @@ 0.6 0.7 0.8 - + - - - - - + + + + + 0 -1 -2 -3 -4 -5 +1 +2 +3 +4 +5 - - - - - + + + + + - - - - + + + + @@ -215,49 +203,45 @@ Density - - + + - - - --4 + + + +-4 -2 -0 -2 -4 - +0 +2 +4 + - - - + + + 0.0 -0.1 -0.2 -0.3 +0.1 +0.2 +0.3 - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + @@ -277,48 +261,45 @@ Density - - - - - - --4 --2 -0 -2 -4 - + + + + + + +-4 +-2 +0 +2 +4 + - - - + + + 0.0 -0.1 -0.2 -0.3 +0.1 +0.2 +0.3 - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + @@ -338,47 +319,45 @@ Density - - - - - - --4 --2 -0 -2 -4 - + + + + + + +-4 +-2 +0 +2 +4 + - - - + + + 0.0 -0.1 -0.2 -0.3 +0.1 +0.2 +0.3 - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-con-exp.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-con-exp.svg new file mode 100644 index 00000000..e64f61ba --- /dev/null +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-con-exp.svg @@ -0,0 +1,207 @@ + + + + + + + + + + + + + + + + + + + +exp marginal posterior x_cont1 +(-1) +marg_post_x_cont1.exp[["-1SD"]] +Density + + + + + + + + + + + +1.2 +1.3 +1.4 +1.5 +1.6 +1.7 +1.8 +1.9 + + + + + + +0 +1 +2 +3 +4 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +exp marginal posterior x_cont1 +(0) +marg_post_x_cont1.exp[["0SD"]] +Density + + + + + + + + + +1.6 +1.7 +1.8 +1.9 +2.0 +2.1 + + + + + + + +0 +1 +2 +3 +4 +5 + + + + + + + + + + + + + + + + + + + + + + + + + + + +exp marginal posterior x_cont1 +(1) +marg_post_x_cont1.exp[["1SD"]] +Density + + + + + + + +2.0 +2.2 +2.4 +2.6 + + + + + + + + +0.0 +0.5 +1.0 +1.5 +2.0 +2.5 +3.0 + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-con-p-exp.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-con-p-exp.svg new file mode 100644 index 00000000..60ea7c5c --- /dev/null +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-con-p-exp.svg @@ -0,0 +1,407 @@ + + + + + + + + + + + + + + + + + + + +marginal prior x_cont1 +(-1) +attr(marg_post_x_cont1.exp[["-1SD"]], "prior_samples") +Density + + + + + + + + +-10 +-5 +0 +5 +10 + + + + + + + + +0.0 +0.1 +0.2 +0.3 +0.4 +0.5 +0.6 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +marginal prior x_cont1 +(0) +exp(attr(marg_post_x_cont1[["0SD"]], "prior_samples")) +Density + + + + + + + + +-10 +-5 +0 +5 +10 + + + + + + + + +0.0 +0.1 +0.2 +0.3 +0.4 +0.5 +0.6 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +marginal prior x_cont1 +(1) +attr(marg_post_x_cont1.exp[["1SD"]], "prior_samples") +Density + + + + + + + + +-10 +-5 +0 +5 +10 + + + + + + + + +0.0 +0.1 +0.2 +0.3 +0.4 +0.5 +0.6 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-con-p.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-con-p.svg similarity index 62% rename from tests/testthat/_snaps/marginal-distributions/marginal-ss-form-con-p.svg rename to tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-con-p.svg index 389ffce8..ecd9c3be 100644 --- a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-con-p.svg +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-con-p.svg @@ -42,23 +42,23 @@ 0 5 10 - + - - - - - - - + + + + + + + 0.00 -0.05 -0.10 -0.15 -0.20 -0.25 -0.30 -0.35 +0.05 +0.10 +0.15 +0.20 +0.25 +0.30 +0.35 @@ -68,82 +68,82 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - + + + @@ -177,17 +177,17 @@ 0 5 10 - + - - - - + + + + 0.0 -0.1 -0.2 -0.3 -0.4 +0.1 +0.2 +0.3 +0.4 @@ -213,37 +213,37 @@ - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - + + + + + + + + + + + + + + @@ -301,104 +301,104 @@ 0 5 10 - + - - - - - - - + + + + + + + 0.00 -0.05 -0.10 -0.15 -0.20 -0.25 -0.30 -0.35 +0.05 +0.10 +0.15 +0.20 +0.25 +0.30 +0.35 - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - + + + - + diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-con.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-con.svg similarity index 56% rename from tests/testthat/_snaps/marginal-distributions/marginal-ss-form-con.svg rename to tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-con.svg index fafdf87d..7ecc9be4 100644 --- a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-con.svg +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-con.svg @@ -31,34 +31,32 @@ Density - + - - - - - + + + + 0.2 -0.3 -0.4 -0.5 -0.6 -0.7 - +0.3 +0.4 +0.5 +0.6 + - - - - - - + + + + + + 0 -1 -2 -3 -4 -5 -6 +1 +2 +3 +4 +5 +6 @@ -66,17 +64,15 @@ - - - - - - - - - - - + + + + + + + + + @@ -97,53 +93,47 @@ Density - - - - - - - - -0.45 -0.50 -0.55 -0.60 -0.65 -0.70 -0.75 - + + + + + + + +0.50 +0.55 +0.60 +0.65 +0.70 +0.75 + - - - - - + + + + + 0 -2 -4 -6 -8 -10 +2 +4 +6 +8 +10 - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + @@ -164,46 +154,41 @@ Density - - - - - - + + + + + -0.6 -0.7 -0.8 -0.9 -1.0 -1.1 - +0.6 +0.7 +0.8 +0.9 +1.0 + - - - - - - + + + + + + 0 -1 -2 -3 -4 -5 -6 +1 +2 +3 +4 +5 +6 - - - - - - - - - - - + + + + + + + + diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-fac-md-at.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-fac-md-at.svg similarity index 51% rename from tests/testthat/_snaps/marginal-distributions/marginal-ss-form-fac-md-at.svg rename to tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-fac-md-at.svg index eac143ee..3ed07a76 100644 --- a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-fac-md-at.svg +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-fac-md-at.svg @@ -30,34 +30,32 @@ Density - + - - - - - - - + + + + + + 0.6 -0.7 -0.8 -0.9 -1.0 -1.1 -1.2 -1.3 - +0.7 +0.8 +0.9 +1.0 +1.1 +1.2 + - - - - + + + + 0 -1 -2 -3 -4 +1 +2 +3 +4 @@ -65,20 +63,19 @@ - - - - - - - - - - - - - - + + + + + + + + + + + + + @@ -98,50 +95,47 @@ Density - + - - - - - - - + + + + + + 0.6 -0.7 -0.8 -0.9 -1.0 -1.1 -1.2 -1.3 - +0.7 +0.8 +0.9 +1.0 +1.1 +1.2 + - - - - + + + + 0 -1 -2 -3 -4 +1 +2 +3 +4 - - - - - - - - - - - - - - + + + + + + + + + + + + + @@ -161,43 +155,62 @@ Density - - - - - -0.4 -0.6 -0.8 -1.0 - + + + + + + + +0.4 +0.5 +0.6 +0.7 +0.8 +0.9 + - - - - + + + + + 0 -1 -2 -3 -4 +1 +2 +3 +4 +5 - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -217,43 +230,62 @@ Density - - - - - -0.4 -0.6 -0.8 -1.0 - + + + + + + + +0.4 +0.5 +0.6 +0.7 +0.8 +0.9 + - - - - + + + + + 0 -1 -2 -3 -4 +1 +2 +3 +4 +5 - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -273,46 +305,47 @@ Density - + - - - - + + + + + + 0.4 -0.6 -0.8 -1.0 -1.2 - +0.5 +0.6 +0.7 +0.8 +0.9 +1.0 + - - - - + + + + 0 -1 -2 -3 -4 +1 +2 +3 +4 - - - - - - - - - - - - - - - - + + + + + + + + + + + + + @@ -332,45 +365,46 @@ Density - + - - - - + + + + + + 0.4 -0.6 -0.8 -1.0 -1.2 - +0.5 +0.6 +0.7 +0.8 +0.9 +1.0 + - - - - + + + + 0 -1 -2 -3 -4 +1 +2 +3 +4 - - - - - - - - - - - - - - - - + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-fac-md-p.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-fac-md-p.svg similarity index 85% rename from tests/testthat/_snaps/marginal-distributions/marginal-ss-form-fac-md-p.svg rename to tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-fac-md-p.svg index 8e7a4800..30447841 100644 --- a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-fac-md-p.svg +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-fac-md-p.svg @@ -83,39 +83,39 @@ - - - - + + + + - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + - - + + - - + + @@ -207,39 +207,39 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + @@ -331,39 +331,39 @@ - - - - + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + - + diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-fac-md.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-fac-md.svg similarity index 52% rename from tests/testthat/_snaps/marginal-distributions/marginal-ss-form-fac-md.svg rename to tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-fac-md.svg index 71b9ce73..bd1a5232 100644 --- a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-fac-md.svg +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-fac-md.svg @@ -30,32 +30,30 @@ Density - - - - - - - -0.5 -0.6 -0.7 -0.8 -0.9 -1.0 - + + + + + + + +0.5 +0.6 +0.7 +0.8 +0.9 +1.0 + - - - - - + + + + 0 -1 -2 -3 -4 -5 +1 +2 +3 +4 @@ -63,34 +61,32 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -110,48 +106,51 @@ Density - - - - - + + + - - -0.2 -0.3 -0.4 -0.5 + +0.4 +0.5 0.6 -0.7 -0.8 - +0.7 + - - - - - + + + + + 0 -1 -2 -3 -4 -5 +1 +2 +3 +4 +5 - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + @@ -171,60 +170,53 @@ Density - - - - - - - -0.3 -0.4 -0.5 -0.6 -0.7 -0.8 - + + + + + +0.4 +0.5 +0.6 +0.7 + - - - - - - + + + + + + 0 -1 -2 -3 -4 -5 -6 +1 +2 +3 +4 +5 +6 - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-fac-mdi-p.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-fac-mdi-p.svg similarity index 72% rename from tests/testthat/_snaps/marginal-distributions/marginal-ss-form-fac-mdi-p.svg rename to tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-fac-mdi-p.svg index 47e7c764..27006104 100644 --- a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-fac-mdi-p.svg +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-fac-mdi-p.svg @@ -60,86 +60,86 @@ - + - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - - - + + + - + @@ -188,83 +188,83 @@ - - - - - + + + + + - - - - - - - - - + + + + + + + + + - - - - + + + + - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - - - - + + + + - - - - - - - - + + + + + + + + - - - + + + @@ -312,82 +312,82 @@ - - - - - - - - - - + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - + + @@ -459,36 +459,36 @@ - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + - - - - - - - - - - - + + + + + + + + + + + @@ -584,39 +584,39 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - - + + @@ -711,35 +711,35 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -810,82 +810,82 @@ 0.4 - + - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + @@ -936,84 +936,84 @@ - - - - - - - - - - - - + + + + + + + + + + + + - - - - - + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - + + + + + + + + + + + + + + - - - - - - + + + + + + - - - - + + + + - - + + @@ -1061,83 +1061,83 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - + + + - - - - - + + + + + - - - + + + - - + + diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-fac-mdi.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-fac-mdi.svg similarity index 53% rename from tests/testthat/_snaps/marginal-distributions/marginal-ss-form-fac-mdi.svg rename to tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-fac-mdi.svg index bc6e84c7..6c8b2273 100644 --- a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-fac-mdi.svg +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-fac-mdi.svg @@ -31,26 +31,26 @@ Density - - - - - -0.2 -0.4 -0.6 -0.8 - + + + + + +0.2 +0.4 +0.6 +0.8 + - - - - + + + + 0 -1 -2 -3 -4 +1 +2 +3 +4 @@ -58,24 +58,21 @@ - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + @@ -96,44 +93,62 @@ Density - - - - - -0.0 -0.2 -0.4 -0.6 - + + + + + + + +0.1 +0.2 +0.3 +0.4 +0.5 +0.6 + - - - - + + + + + 0 -1 -2 -3 -4 +1 +2 +3 +4 +5 - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -154,49 +169,59 @@ Density - - - - - - - - -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 - + + + + + + + +0.1 +0.2 +0.3 +0.4 +0.5 +0.6 + - - - - - + + + + + 0 -1 -2 -3 -4 -5 +1 +2 +3 +4 +5 - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + @@ -217,62 +242,58 @@ Density - - - - - - - -0.5 -0.6 -0.7 -0.8 -0.9 -1.0 - + + + + + + + +0.5 +0.6 +0.7 +0.8 +0.9 +1.0 + - - - - - + + + + 0 -1 -2 -3 -4 -5 +1 +2 +3 +4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -293,48 +314,51 @@ Density - - - - - + + + - - -0.2 -0.3 -0.4 -0.5 + +0.4 +0.5 0.6 -0.7 -0.8 - +0.7 + - - - - - + + + + + 0 -1 -2 -3 -4 -5 +1 +2 +3 +4 +5 - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + @@ -355,61 +379,54 @@ Density - - - - - - - -0.3 -0.4 -0.5 -0.6 -0.7 -0.8 - + + + + + +0.4 +0.5 +0.6 +0.7 + - - - - - - + + + + + + 0 -1 -2 -3 -4 -5 -6 +1 +2 +3 +4 +5 +6 - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + @@ -430,50 +447,47 @@ Density - + - - - - - - - + + + + + + 0.6 -0.7 -0.8 -0.9 -1.0 -1.1 -1.2 -1.3 - +0.7 +0.8 +0.9 +1.0 +1.1 +1.2 + - - - - + + + + 0 -1 -2 -3 -4 +1 +2 +3 +4 - - - - - - - - - - - - - - + + + + + + + + + + + + + @@ -494,43 +508,62 @@ Density - - - - - -0.4 -0.6 -0.8 -1.0 - + + + + + + + +0.4 +0.5 +0.6 +0.7 +0.8 +0.9 + - - - - + + + + + 0 -1 -2 -3 -4 +1 +2 +3 +4 +5 - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -551,45 +584,46 @@ Density - + - - - - + + + + + + 0.4 -0.6 -0.8 -1.0 -1.2 - +0.5 +0.6 +0.7 +0.8 +0.9 +1.0 + - - - - + + + + 0 -1 -2 -3 -4 +1 +2 +3 +4 - - - - - - - - - - - - - - - - + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-fac-t-p.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-fac-t-p.svg similarity index 84% rename from tests/testthat/_snaps/marginal-distributions/marginal-ss-form-fac-t-p.svg rename to tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-fac-t-p.svg index 9d8188f4..b884ef19 100644 --- a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-fac-t-p.svg +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-fac-t-p.svg @@ -82,41 +82,41 @@ - + - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -202,49 +202,49 @@ - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - + + + + - + diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-fac-t.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-fac-t.svg similarity index 54% rename from tests/testthat/_snaps/marginal-distributions/marginal-ss-form-fac-t.svg rename to tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-fac-t.svg index ab0a9f5d..a78377ae 100644 --- a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-fac-t.svg +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-fac-t.svg @@ -30,34 +30,32 @@ Density - - - - - - - - -0.45 -0.50 -0.55 -0.60 -0.65 -0.70 -0.75 - + + + + + + + +0.50 +0.55 +0.60 +0.65 +0.70 +0.75 + - - - - - + + + + + 0 -2 -4 -6 -8 -10 +2 +4 +6 +8 +10 @@ -65,23 +63,19 @@ - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + @@ -101,53 +95,51 @@ Density - - - - - - - - -0.45 -0.50 -0.55 -0.60 -0.65 -0.70 -0.75 - + + + + + + + + +0.50 +0.55 +0.60 +0.65 +0.70 +0.75 +0.80 + - - - - - + + + + + 0 -2 -4 -6 -8 -10 +2 +4 +6 +8 +10 - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-int-p.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-int-p.svg new file mode 100644 index 00000000..fa772ddd --- /dev/null +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-int-p.svg @@ -0,0 +1,70 @@ + + + + + + + + + + + + +marginal prior intercept +attr(marg_post_int[["intercept"]], "prior_samples") +Density + + + + + + +-4 +-2 +0 +2 +4 + + + + + +0.0 +0.1 +0.2 +0.3 + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-int.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-int.svg similarity index 54% rename from tests/testthat/_snaps/marginal-distributions/marginal-ss-form-int.svg rename to tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-int.svg index 3861ed06..7f77cfea 100644 --- a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-int.svg +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-form-int.svg @@ -21,34 +21,32 @@ marginal posterior intercept marg_post_int[["intercept"]] Density - - - - - - - - -0.45 -0.50 -0.55 -0.60 -0.65 -0.70 -0.75 - + + + + + + + +0.50 +0.55 +0.60 +0.65 +0.70 +0.75 + - - - - - + + + + + 0 -2 -4 -6 -8 -10 +2 +4 +6 +8 +10 @@ -56,22 +54,18 @@ - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-simple-con-p.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-simple-con-p.svg new file mode 100644 index 00000000..15833fb4 --- /dev/null +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-simple-con-p.svg @@ -0,0 +1,88 @@ + + + + + + + + + + + + +marginal prior sigma +attr(marg_post_sigma, "prior_samples") +Density + + + + + + + +0 +1 +2 +3 +4 +5 + + + + + + + + + +0.0 +0.1 +0.2 +0.3 +0.4 +0.5 +0.6 +0.7 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-simple-con.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-simple-con.svg new file mode 100644 index 00000000..4b929cc4 --- /dev/null +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-simple-con.svg @@ -0,0 +1,60 @@ + + + + + + + + + + + + +marginal posterior sigma +marg_post_sigma +Density + + + + + +0.45 +0.50 +0.55 +0.60 + + + + + +0 +5 +10 +15 + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-simple-fac-p.svg b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-simple-fac-p.svg similarity index 68% rename from tests/testthat/_snaps/marginal-distributions/marginal-simple-fac-p.svg rename to tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-simple-fac-p.svg index 0bd45410..bac946b6 100644 --- a/tests/testthat/_snaps/marginal-distributions/marginal-simple-fac-p.svg +++ b/tests/testthat/_snaps/JAGS-marginal-distributions/marginal-ss-simple-fac-p.svg @@ -83,51 +83,47 @@ Density - - - - - + + + + --4 --2 -0 -2 +-2 +0 +2 4 - + - - - - - - + + + + + + 0.0 -0.2 -0.4 -0.6 -0.8 -1.0 -1.2 +0.2 +0.4 +0.6 +0.8 +1.0 +1.2 - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + diff --git 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@@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -0 -2 -4 -6 -8 -10 -12 - - - - - - - - - - - - -0.4 -0.5 -0.6 -0.7 -0.8 -fac2t -Density - - - - - -A -B - - diff --git a/tests/testthat/_snaps/marginal-distributions/ggplot-marginal-ss-mu-x-fac2t-3.svg b/tests/testthat/_snaps/marginal-distributions/ggplot-marginal-ss-mu-x-fac2t-3.svg deleted file mode 100644 index 9aaf58c0..00000000 --- a/tests/testthat/_snaps/marginal-distributions/ggplot-marginal-ss-mu-x-fac2t-3.svg +++ /dev/null @@ -1,107 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -0 -2 -4 -6 -8 -10 -12 - - - - - - - - - - - - - - - --6 --4 --2 -0 -2 -4 -6 -8 -Density - - - - - - - - - -A -B - - diff --git a/tests/testthat/_snaps/marginal-distributions/ggplot-marginal-ss-mu-x-fac2t-4.svg b/tests/testthat/_snaps/marginal-distributions/ggplot-marginal-ss-mu-x-fac2t-4.svg deleted file mode 100644 index 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a/tests/testthat/_snaps/marginal-distributions/marginal-form-con-exp.svg b/tests/testthat/_snaps/marginal-distributions/marginal-form-con-exp.svg deleted file mode 100644 index bba0357d..00000000 --- a/tests/testthat/_snaps/marginal-distributions/marginal-form-con-exp.svg +++ /dev/null @@ -1,213 +0,0 @@ - - - - - - - - - - - - - - - - - - - -exp marginal posterior x_cont1 -(-1) -marg_post_x_cont1.exp[["-1SD"]] -Density - - - - - - - - -1.2 -1.4 -1.6 -1.8 -2.0 - - - - - - -0 -1 -2 -3 -4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -exp marginal posterior x_cont1 -(0) -marg_post_x_cont1.exp[["0SD"]] -Density - - - - - - - -1.6 -1.8 -2.0 -2.2 - - - - - - -0 -1 -2 -3 -4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -exp marginal posterior x_cont1 -(1) -marg_post_x_cont1.exp[["1SD"]] -Density - - - - - - - - - -1.8 -2.0 -2.2 -2.4 -2.6 -2.8 - - - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 -2.5 -3.0 - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-form-con-p-exp.svg b/tests/testthat/_snaps/marginal-distributions/marginal-form-con-p-exp.svg deleted file mode 100644 index 47794cdd..00000000 --- a/tests/testthat/_snaps/marginal-distributions/marginal-form-con-p-exp.svg +++ /dev/null @@ -1,411 +0,0 @@ - - - - - - - - - - - - - - - - - - - -marginal prior x_cont1 -(-1) -attr(marg_post_x_cont1.exp[["-1SD"]], "prior_samples") -Density - - - - - - - - --10 --5 -0 -5 -10 - - - - - - - - - -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -marginal prior x_cont1 -(0) -exp(attr(marg_post_x_cont1[["0SD"]], "prior_samples")) -Density - - - - - - - - --10 --5 -0 -5 -10 - - - - - - - - -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -marginal prior x_cont1 -(1) -attr(marg_post_x_cont1.exp[["1SD"]], "prior_samples") -Density - - - - - - - - --10 --5 -0 -5 -10 - - - - - - - - - -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-form-con.svg b/tests/testthat/_snaps/marginal-distributions/marginal-form-con.svg deleted file mode 100644 index f3641cdc..00000000 --- a/tests/testthat/_snaps/marginal-distributions/marginal-form-con.svg +++ /dev/null @@ -1,208 +0,0 @@ - - - - - - - - - - - - - - - - - - - -marginal posterior x_cont1 -(-1) -marg_post_x_cont1[["-1SD"]] -Density - - - - - - - - - -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 - - - - - - - - -0 -1 -2 -3 -4 -5 -6 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -marginal posterior x_cont1 -(0) -marg_post_x_cont1[["0SD"]] -Density - - - - - - - - -0.4 -0.5 -0.6 -0.7 -0.8 - - - - - - -0 -2 -4 -6 -8 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -marginal posterior x_cont1 -(1) -marg_post_x_cont1[["1SD"]] -Density - - - - - - - - -0.6 -0.7 -0.8 -0.9 -1.0 - - - - - - - - -0 -1 -2 -3 -4 -5 -6 - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-md-at.svg b/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-md-at.svg deleted file mode 100644 index 1bfd629c..00000000 --- a/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-md-at.svg +++ /dev/null @@ -1,369 +0,0 @@ - - - - - - - - - - - - - - - - - - - -marg_post_x_fac3md = A | 1,A -marg_post_x_fac3md_AT[["A"]][1, ] -Density - - - - - - - -0.6 -0.8 -1.0 -1.2 - - - - - - -0 -1 -2 -3 -4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -marg_post_x_fac3md = A | 1,B -marg_post_x_fac3md_AT[["A"]][2, ] -Density - - - - - - - -0.6 -0.8 -1.0 -1.2 - - - - - -0 -1 -2 -3 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -marg_post_x_fac3md = B | 1,A -marg_post_x_fac3md_AT[["B"]][1, ] -Density - - - - - - - -0.4 -0.6 -0.8 -1.0 - - - - - - -0 -1 -2 -3 -4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -marg_post_x_fac3md = B | 1,B -marg_post_x_fac3md_AT[["B"]][2, ] -Density - - - - - - - -0.4 -0.6 -0.8 -1.0 - - - - - - -0 -1 -2 -3 -4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -marg_post_x_fac3md = C | 1,A -marg_post_x_fac3md_AT[["C"]][1, ] -Density - - - - - - - -0.4 -0.6 -0.8 -1.0 - - - - - - - -0 -1 -2 -3 -4 -5 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -marg_post_x_fac3md = C | 1,B -marg_post_x_fac3md_AT[["C"]][2, ] -Density - - - - - - - -0.4 -0.6 -0.8 -1.0 - - - - - - -0 -1 -2 -3 -4 - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-md.svg b/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-md.svg deleted file mode 100644 index fa73ae0e..00000000 --- a/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-md.svg +++ /dev/null @@ -1,213 +0,0 @@ - - - - - - - - - - - - - - - - - - - -marg_post_x_fac3md = A -marg_post_x_fac3md[["A"]] -Density - - - - - - - - - - -0.5 -0.6 -0.7 -0.8 -0.9 -1.0 -1.1 - - - - - - - -0 -1 -2 -3 -4 -5 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -marg_post_x_fac3md = B -marg_post_x_fac3md[["B"]] -Density - - - - - - - - - - -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 -0.8 - - - - - - - - -0 -1 -2 -3 -4 -5 -6 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -marg_post_x_fac2t = B -marg_post_x_fac3md[["C"]] -Density - - - - - - - - - - -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 -0.8 - - - - - - - - -0 -1 -2 -3 -4 -5 -6 - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-mdi.svg b/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-mdi.svg deleted file mode 100644 index 6d38ffab..00000000 --- a/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-mdi.svg +++ /dev/null @@ -1,579 +0,0 @@ - - - - - - - - - - - - - - - - - - - -x_cont1 = -1 -marg_post_x_fac3md = A -marg_post_x_cont1__xXx__x_fac3md[["-1SD, A"]] -Density - - - - - - - - -0.2 -0.4 -0.6 -0.8 -1.0 - - - - - - -0 -1 -2 -3 -4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_cont1 = -1 -marg_post_x_fac3md = B -marg_post_x_cont1__xXx__x_fac3md[["-1SD, B"]] -Density - - - - - - - - -0.0 -0.2 -0.4 -0.6 -0.8 - - - - - - -0 -1 -2 -3 -4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_cont1 = -1 -marg_post_x_fac3md = B -marg_post_x_cont1__xXx__x_fac3md[["-1SD, C"]] -Density - - - - - - - -0.2 -0.4 -0.6 -0.8 - - - - - - - -0 -1 -2 -3 -4 -5 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_cont1 = 0 -marg_post_x_fac3md = A -marg_post_x_cont1__xXx__x_fac3md[["0SD, A"]] -Density - - - - - - - - - - -0.5 -0.6 -0.7 -0.8 -0.9 -1.0 -1.1 - - - - - - - -0 -1 -2 -3 -4 -5 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_cont1 = 0 -marg_post_x_fac3md = B -marg_post_x_cont1__xXx__x_fac3md[["0SD, B"]] -Density - - - - - - - - - - -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 -0.8 - - - - - - - - -0 -1 -2 -3 -4 -5 -6 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_cont1 = 0 -marg_post_x_fac3md = B -marg_post_x_cont1__xXx__x_fac3md[["0SD, C"]] -Density - - - - - - - - - - -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 -0.8 - - - - - - - - -0 -1 -2 -3 -4 -5 -6 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_cont1 = 1 -marg_post_x_fac3md = A -marg_post_x_cont1__xXx__x_fac3md[["1SD, A"]] -Density - - - - - - - -0.6 -0.8 -1.0 -1.2 - - - - - - -0 -1 -2 -3 -4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_cont1 = 1 -marg_post_x_fac3md = B -marg_post_x_cont1__xXx__x_fac3md[["1SD, B"]] -Density - - - - - - - -0.4 -0.6 -0.8 -1.0 - - - - - - -0 -1 -2 -3 -4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -x_cont1 = 1 -marg_post_x_fac3md = B -marg_post_x_cont1__xXx__x_fac3md[["1SD, C"]] -Density - - - - - - - -0.4 -0.6 -0.8 -1.0 - - - - - - - -0 -1 -2 -3 -4 -5 - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-t.svg b/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-t.svg deleted file mode 100644 index a2dbdb5a..00000000 --- a/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-t.svg +++ /dev/null @@ -1,150 +0,0 @@ - - - - - - - - - - - - - - - - - - - -marg_post_x_fac2t = A -marg_post_x_fac2t[["A"]] -Density - - - - - - - - -0.4 -0.5 -0.6 -0.7 -0.8 - - - - - - -0 -2 -4 -6 -8 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -marg_post_x_fac2t = B -marg_post_x_fac2t[["B"]] -Density - - - - - - - - -0.4 -0.5 -0.6 -0.7 -0.8 - - - - - - -0 -2 -4 -6 -8 - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-form-int-p.svg b/tests/testthat/_snaps/marginal-distributions/marginal-form-int-p.svg deleted file mode 100644 index be672578..00000000 --- a/tests/testthat/_snaps/marginal-distributions/marginal-form-int-p.svg +++ /dev/null @@ -1,73 +0,0 @@ - - - - - - - - - - - - -marginal prior intercept -attr(marg_post_int[["intercept"]], "prior_samples") -Density - - - - - - --4 --2 -0 -2 -4 - - - - - - -0.0 -0.1 -0.2 -0.3 -0.4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-form-int.svg b/tests/testthat/_snaps/marginal-distributions/marginal-form-int.svg deleted file mode 100644 index e0dbc187..00000000 --- a/tests/testthat/_snaps/marginal-distributions/marginal-form-int.svg +++ /dev/null @@ -1,76 +0,0 @@ - - - - - - - - - - - - -marginal posterior intercept -marg_post_int[["intercept"]] -Density - - - - - - -0.4 -0.5 -0.6 -0.7 -0.8 - - - - - - -0 -2 -4 -6 -8 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-inference-cont-p.svg b/tests/testthat/_snaps/marginal-distributions/marginal-inference-cont-p.svg deleted file mode 100644 index af927343..00000000 --- a/tests/testthat/_snaps/marginal-distributions/marginal-inference-cont-p.svg +++ /dev/null @@ -1,392 +0,0 @@ - - - - - - - - - - - - - - - - - - - -mu_x_cont1 = -1SD -attr(marg_post_x_cont1[["-1SD"]], "prior_samples") -Density - - - - - - - - --4 --2 -0 -2 -4 - - - - - - -0.0 -0.1 -0.2 -0.3 -0.4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -mu_x_cont1 = 0SD -attr(marg_post_x_cont1[["0SD"]], "prior_samples") -Density - - - - - - - - --4 --2 -0 -2 -4 - - - - - - -0.0 -0.1 -0.2 -0.3 -0.4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -mu_x_cont1 = 1SD -attr(marg_post_x_cont1[["1SD"]], "prior_samples") -Density - - - - - - - - --4 --2 -0 -2 -4 - - - - - - -0.0 -0.1 -0.2 -0.3 -0.4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-inference-cont.svg b/tests/testthat/_snaps/marginal-distributions/marginal-inference-cont.svg deleted file mode 100644 index 71ac0c4c..00000000 --- a/tests/testthat/_snaps/marginal-distributions/marginal-inference-cont.svg +++ /dev/null @@ -1,233 +0,0 @@ - - - - - - - - - - - - - - - - - - - -mu_x_cont1 = -1SD -marg_post_x_cont1[["-1SD"]] -Density - - - - - - - - - -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 - - - - - - - - -0 -1 -2 -3 -4 -5 -6 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -mu_x_cont1 = 0SD -marg_post_x_cont1[["0SD"]] -Density - - - - - - - - -0.4 -0.5 -0.6 -0.7 -0.8 - - - - - - -0 -2 -4 -6 -8 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -mu_x_cont1 = +1SD -marg_post_x_cont1[["1SD"]] -Density - - - - - - - - -0.6 -0.7 -0.8 -0.9 -1.0 - - - - - - - - -0 -1 -2 -3 -4 -5 -6 - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-inference-fac-md.svg b/tests/testthat/_snaps/marginal-distributions/marginal-inference-fac-md.svg deleted file mode 100644 index 788ea144..00000000 --- a/tests/testthat/_snaps/marginal-distributions/marginal-inference-fac-md.svg +++ /dev/null @@ -1,213 +0,0 @@ - - - - - - - - - - - - - - - - - - - -marg_post_x_fac3md = A -marg_post_x_fac3md[["A"]] -Density - - - - - - - - - - -0.5 -0.6 -0.7 -0.8 -0.9 -1.0 -1.1 - - - - - - - -0 -1 -2 -3 -4 -5 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -marg_post_x_fac3md = B -marg_post_x_fac3md[["B"]] -Density - - - - - - - - - - -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 -0.8 - - - - - - - - -0 -1 -2 -3 -4 -5 -6 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -marg_post_x_fac2t = B -marg_post_x_fac3md[["C"]] -Density - - - - - - - - - - -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 -0.8 - - - - - - - - -0 -1 -2 -3 -4 -5 -6 - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-inference-ss-cont-p.svg b/tests/testthat/_snaps/marginal-distributions/marginal-inference-ss-cont-p.svg deleted file mode 100644 index ade6ff85..00000000 --- a/tests/testthat/_snaps/marginal-distributions/marginal-inference-ss-cont-p.svg +++ /dev/null @@ -1,392 +0,0 @@ - - - - - - - - - - - - - - - - - - - -mu_x_cont1 = -1SD -attr(marg_post_x_cont1[["-1SD"]], "prior_samples") -Density - - - - - - - - --4 --2 -0 -2 -4 - - - - - - -0.0 -0.1 -0.2 -0.3 -0.4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -mu_x_cont1 = 0SD -attr(marg_post_x_cont1[["0SD"]], "prior_samples") -Density - - - - - - - - --4 --2 -0 -2 -4 - - - - - - -0.0 -0.1 -0.2 -0.3 -0.4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -mu_x_cont1 = 1SD -attr(marg_post_x_cont1[["1SD"]], "prior_samples") -Density - - - - - - - - --4 --2 -0 -2 -4 - - - - - - -0.0 -0.1 -0.2 -0.3 -0.4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-inference-ss-cont.svg b/tests/testthat/_snaps/marginal-distributions/marginal-inference-ss-cont.svg deleted file mode 100644 index 99590152..00000000 --- a/tests/testthat/_snaps/marginal-distributions/marginal-inference-ss-cont.svg +++ /dev/null @@ -1,235 +0,0 @@ - - - - - - - - - - - - - - - - - - - -mu_x_cont1 = -1SD -marg_post_x_cont1[["-1SD"]] -Density - - - - - - - - - -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 - - - - - - - - - -0 -1 -2 -3 -4 -5 -6 -7 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -mu_x_cont1 = 0SD -marg_post_x_cont1[["0SD"]] -Density - - - - - - - - - - -0.45 -0.50 -0.55 -0.60 -0.65 -0.70 -0.75 - - - - - - - -0 -2 -4 -6 -8 -10 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -mu_x_cont1 = +1SD -marg_post_x_cont1[["1SD"]] -Density - - - - - - - - - -0.6 -0.7 -0.8 -0.9 -1.0 -1.1 - - - - - -0 -2 -4 -6 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-inference-ss-fac-md.svg b/tests/testthat/_snaps/marginal-distributions/marginal-inference-ss-fac-md.svg deleted file mode 100644 index e25d7b3b..00000000 --- a/tests/testthat/_snaps/marginal-distributions/marginal-inference-ss-fac-md.svg +++ /dev/null @@ -1,233 +0,0 @@ - - - - - - - - - - - - - - - - - - - -marg_post_x_fac3md = A -marg_post_x_fac3md[["A"]] -Density - - - - - - - - - -0.5 -0.6 -0.7 -0.8 -0.9 -1.0 - - - - - - - -0 -1 -2 -3 -4 -5 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -marg_post_x_fac3md = B -marg_post_x_fac3md[["B"]] -Density - - - - - - - - - - -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 -0.8 - - - - - - - -0 -1 -2 -3 -4 -5 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -marg_post_x_fac3md = C -marg_post_x_fac3md[["C"]] -Density - - - - - - - - - -0.3 -0.4 -0.5 -0.6 -0.7 -0.8 - - - - - - - - -0 -1 -2 -3 -4 -5 -6 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-simple-con-p.svg b/tests/testthat/_snaps/marginal-distributions/marginal-simple-con-p.svg deleted file mode 100644 index b8bdf0bf..00000000 --- a/tests/testthat/_snaps/marginal-distributions/marginal-simple-con-p.svg +++ /dev/null @@ -1,88 +0,0 @@ - - - - - - - - - - - - -marginal prior sigma -attr(marg_post_sigma, "prior_samples") -Density - - - - - - - -0 -1 -2 -3 -4 -5 - - - - - - - - - -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-simple-con.svg b/tests/testthat/_snaps/marginal-distributions/marginal-simple-con.svg deleted file mode 100644 index af58a247..00000000 --- a/tests/testthat/_snaps/marginal-distributions/marginal-simple-con.svg +++ /dev/null @@ -1,73 +0,0 @@ - - - - - - - - - - - - -marginal posterior sigma -marg_post_sigma -Density - - - - - - -0.40 -0.45 -0.50 -0.55 -0.60 - - - - - - - - - -0 -2 -4 -6 -8 -10 -12 -14 - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-simple-fac.svg b/tests/testthat/_snaps/marginal-distributions/marginal-simple-fac.svg deleted file mode 100644 index 73d4b37a..00000000 --- a/tests/testthat/_snaps/marginal-distributions/marginal-simple-fac.svg +++ /dev/null @@ -1,148 +0,0 @@ - - - - - - - - - - - - - - - - - - - -marg_post_x_fac2t = A -marg_post_simple_x_fac2t[["A"]] -Density - - - - - - - - - --1.0 --0.8 --0.6 --0.4 --0.2 -0.0 - - - - - - - -0.0 -0.2 -0.4 -0.6 -0.8 -1.0 - - - - - - - - - - - - - - - - - - - - - - -marg_post_x_fac2t = B -marg_post_simple_x_fac2t[["B"]] -Density - - - - - - - - - - --0.3 --0.2 --0.1 -0.0 -0.1 -0.2 -0.3 - - - - - - - -0 -5 -10 -15 -20 -25 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-con-exp.svg b/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-con-exp.svg deleted file mode 100644 index 89882a21..00000000 --- a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-con-exp.svg +++ /dev/null @@ -1,209 +0,0 @@ - - - - - - - - - - - - - - - - - - - -exp marginal posterior x_cont1 -(-1) -marg_post_x_cont1.exp[["-1SD"]] -Density - - - - - - - - -1.2 -1.4 -1.6 -1.8 -2.0 - - - - - - -0 -1 -2 -3 -4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -exp marginal posterior x_cont1 -(0) -marg_post_x_cont1.exp[["0SD"]] -Density - - - - - - - - - - -1.6 -1.7 -1.8 -1.9 -2.0 -2.1 -2.2 - - - - - - - -0 -1 -2 -3 -4 -5 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -exp marginal posterior x_cont1 -(1) -marg_post_x_cont1.exp[["1SD"]] -Density - - - - - - - - - -1.8 -2.0 -2.2 -2.4 -2.6 -2.8 - - - - - - - - -0.0 -0.5 -1.0 -1.5 -2.0 -2.5 -3.0 - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-con-p-exp.svg b/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-con-p-exp.svg deleted file mode 100644 index d494ef4b..00000000 --- a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-con-p-exp.svg +++ /dev/null @@ -1,407 +0,0 @@ - - - - - - - - - - - - - - - - - - - -marginal prior x_cont1 -(-1) -attr(marg_post_x_cont1.exp[["-1SD"]], "prior_samples") -Density - - - - - - - - --10 --5 -0 -5 -10 - - - - - - - - -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -marginal prior x_cont1 -(0) -exp(attr(marg_post_x_cont1[["0SD"]], "prior_samples")) -Density - - - - - - - - --10 --5 -0 -5 -10 - - - - - - - - -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -marginal prior x_cont1 -(1) -attr(marg_post_x_cont1.exp[["1SD"]], "prior_samples") -Density - - - - - - - - --10 --5 -0 -5 -10 - - - - - - - - -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-int-p.svg b/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-int-p.svg deleted file mode 100644 index e0790130..00000000 --- a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-int-p.svg +++ /dev/null @@ -1,69 +0,0 @@ - - - - - - - - - - - - -marginal prior intercept -attr(marg_post_int[["intercept"]], "prior_samples") -Density - - - - - - --4 --2 -0 -2 -4 - - - - - -0.0 -0.1 -0.2 -0.3 - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-ss-simple-con-p.svg b/tests/testthat/_snaps/marginal-distributions/marginal-ss-simple-con-p.svg deleted file mode 100644 index fb5f81ba..00000000 --- a/tests/testthat/_snaps/marginal-distributions/marginal-ss-simple-con-p.svg +++ /dev/null @@ -1,88 +0,0 @@ - - - - - - - - - - - - -marginal prior sigma -attr(marg_post_sigma, "prior_samples") -Density - - - - - - - -0 -1 -2 -3 -4 -5 - - - - - - - - - -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-ss-simple-con.svg b/tests/testthat/_snaps/marginal-distributions/marginal-ss-simple-con.svg deleted file mode 100644 index f9e929b0..00000000 --- a/tests/testthat/_snaps/marginal-distributions/marginal-ss-simple-con.svg +++ /dev/null @@ -1,79 +0,0 @@ - - - - - - - - - - - - -marginal posterior sigma -marg_post_sigma -Density - - - - - -0.45 -0.50 -0.55 -0.60 - - - - - - - - - -0 -2 -4 -6 -8 -10 -12 -14 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/testthat/_snaps/marginal-distributions/plot-marginal-int.svg b/tests/testthat/_snaps/marginal-distributions/plot-marginal-int.svg deleted file mode 100644 index 3df96ac0..00000000 --- a/tests/testthat/_snaps/marginal-distributions/plot-marginal-int.svg +++ /dev/null @@ -1,83 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -0 -2 -4 -6 -8 -10 - - - - - - - - - - - --1.0 --0.5 -0.0 -0.5 -1.0 -Density - - - - -intercept - - diff --git a/tests/testthat/_snaps/marginal-distributions/plot-marginal-mu-x-cont1.svg b/tests/testthat/_snaps/marginal-distributions/plot-marginal-mu-x-cont1.svg deleted file mode 100644 index 2575419b..00000000 --- a/tests/testthat/_snaps/marginal-distributions/plot-marginal-mu-x-cont1.svg +++ /dev/null @@ -1,72 +0,0 @@ - - - - - - - - - - - - - - - - - - - -0.0 -0.2 -0.4 -0.6 -0.8 -1.0 - - - - - - -0 -2 -4 -6 -8 -Density - - - - - - - - - - - - - --1SD -0SD -1SD - - - - - - --1SD -0SD -1SD - - diff --git a/tests/testthat/_snaps/marginal-distributions/plot-marginal-mu-x-fac2t-1.svg b/tests/testthat/_snaps/marginal-distributions/plot-marginal-mu-x-fac2t-1.svg deleted file mode 100644 index 3072d93c..00000000 --- a/tests/testthat/_snaps/marginal-distributions/plot-marginal-mu-x-fac2t-1.svg +++ /dev/null @@ -1,60 +0,0 @@ - - - - - - - - - - - - - - - - - - - -0.4 -0.5 -0.6 -0.7 -0.8 -0.9 - - - - - - -0 -2 -4 -6 -8 -Density - - - - - - - - - - - -A -B - - diff --git a/tests/testthat/_snaps/marginal-distributions/plot-marginal-mu-x-fac2t-2.svg b/tests/testthat/_snaps/marginal-distributions/plot-marginal-mu-x-fac2t-2.svg deleted file mode 100644 index 6e669861..00000000 --- a/tests/testthat/_snaps/marginal-distributions/plot-marginal-mu-x-fac2t-2.svg +++ /dev/null @@ -1,61 +0,0 @@ - - - - - - - - - - - - - - - - - - - -0.4 -0.5 -0.6 -0.7 -0.8 -0.9 - - - - - - -0 -2 -4 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+Probability + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/model-averaging-plots/plot-models-basic.svg b/tests/testthat/_snaps/model-averaging-plots/plot-models-basic.svg new file mode 100644 index 00000000..cdad4f04 --- /dev/null +++ b/tests/testthat/_snaps/model-averaging-plots/plot-models-basic.svg @@ -0,0 +1,73 @@ + + + + + + + + + + + + + + + + + + + + +-0.4 +-0.2 +0.0 +0.2 +0.4 +0.6 +0.8 +m + + + + +Model-Averaged +Model 1 +Model 2 + + + + + + +0.17 [-0.22, 0.59] +0.20 [-0.20, 0.63] +BF = 0.57 [0.50 -> 0.36] +0.16 [-0.25, 0.50] +BF = 1.76 [0.50 -> 0.64] + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/model-averaging-plots/model-averaging-plot-models-10.svg b/tests/testthat/_snaps/model-averaging-plots/plot-models-ggplot.svg similarity index 53% rename from tests/testthat/_snaps/model-averaging-plots/model-averaging-plot-models-10.svg rename to tests/testthat/_snaps/model-averaging-plots/plot-models-ggplot.svg index e36079c0..edf03363 100644 --- a/tests/testthat/_snaps/model-averaging-plots/model-averaging-plot-models-10.svg +++ b/tests/testthat/_snaps/model-averaging-plots/plot-models-ggplot.svg @@ -28,64 +28,60 @@ - - - + + - - - - - - - - - - - + + + + + + + + + - - - - - - + + + + + - - - - - - - - + + + + + + + + + + -Model-Averaged -Model 1 -Model 2 -Model 3 -0.02 [-0.19, 0.43] -BF = 2.42 [0.33 -> 0.55] -BF = 0.43 [0.33 -> 0.18] -BF = 0.75 [0.33 -> 0.27] +Model-Averaged +Model 1 +Model 2 +0.17 [-0.22, 0.59] +0.20 [-0.20, 0.63] +0.16 [-0.25, 0.50] +BF = 0.57 [0.50 -> 0.36] +BF = 1.76 [0.50 -> 0.64] - - - - - - + + + + + --0.6 --0.4 --0.2 -0 -0.2 -0.4 -0.6 +-0.4 +-0.2 +0 +0.2 +0.4 +0.6 0.8 -mu -model-averaging-plot-models-10 +m +plot-models-ggplot diff --git a/tests/testthat/_snaps/model-averaging-plots/plot-models-order-decreasing-estimate.svg b/tests/testthat/_snaps/model-averaging-plots/plot-models-order-decreasing-estimate.svg new file mode 100644 index 00000000..cdad4f04 --- /dev/null +++ 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b/tests/testthat/_snaps/model-averaging-plots/plot-models-order-trans-prior-ggplot.svg new file mode 100644 index 00000000..e69de29b diff --git a/tests/testthat/_snaps/model-averaging-plots/plot-models-order-trans-prior.svg b/tests/testthat/_snaps/model-averaging-plots/plot-models-order-trans-prior.svg new file mode 100644 index 00000000..42fcda78 --- /dev/null +++ b/tests/testthat/_snaps/model-averaging-plots/plot-models-order-trans-prior.svg @@ -0,0 +1,77 @@ + + + + + + + + + + + + + + + + + + +0 +2 +4 +6 +8 +m + + + + +Model-Averaged +Model 1 +Model 2 + + + + + + +1.19 [0.80, 1.80] +1.22 [0.82, 1.88] +BF = 0.57 [0.50 -> 0.36] +1.17 [0.78, 1.64] +BF = 1.76 [0.50 -> 0.64] + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/model-averaging-plots/model-averaging-plot-models-2.svg b/tests/testthat/_snaps/model-averaging-plots/plot-models-order-trans.svg similarity index 56% rename from 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[0.62, 1.59] +1.00 [1.00, 1.00] +BF = 0.07 [0.50 -> 0.06] +1.36 [1.09, 1.68] +BF = 15.08 [0.50 -> 0.94] + + + + + + + + + + + diff --git a/tests/testthat/_snaps/model-averaging-plots/plot-models-orthonormal-3.svg b/tests/testthat/_snaps/model-averaging-plots/plot-models-orthonormal-3.svg new file mode 100644 index 00000000..a278ee6f --- /dev/null +++ b/tests/testthat/_snaps/model-averaging-plots/plot-models-orthonormal-3.svg @@ -0,0 +1,210 @@ + + + + + + + + + + + + + + + + + + + +0.0 +0.5 +1.0 +1.5 +2.0 +2.5 + + + + + + + +(mu) x_fac3o [dif: A] + + + + + + +Model-Averaged +Model 1 +Model 2 + + + + + + +1.00 [0.62, 1.59] +1.00 [1.00, 1.00] +BF = 0.07 [0.50 -> 0.06] +1.02 [0.82, 1.27] +BF = 15.08 [0.50 -> 0.94] + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0.0 +0.5 +1.0 +1.5 +2.0 +2.5 + + + + + + + +(mu) x_fac3o [dif: B] + + + + + + +Model-Averaged +Model 1 +Model 2 + + + + + + +1.00 [0.62, 1.59] +1.00 [1.00, 1.00] +BF = 0.07 [0.50 -> 0.06] +0.72 [0.58, 0.90] 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+0.3 +0.4 +0.5 +Probability @@ -57,9 +66,8 @@ - - - - + + + diff --git a/tests/testthat/_snaps/priors-plot/priors-plot-15-3.svg b/tests/testthat/_snaps/priors-plot/priors-plot-15-3.svg index b4fb12d7..2569a598 100644 --- a/tests/testthat/_snaps/priors-plot/priors-plot-15-3.svg +++ b/tests/testthat/_snaps/priors-plot/priors-plot-15-3.svg @@ -18,38 +18,47 @@ +Density - - - - + + + + + -0.0 -0.2 -0.4 -0.6 -0.8 -1.0 - +-3 +-2 +-1 +0 +1 +2 +3 + - - - - - - -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 - -Bernoulli -(probability = 0.33) -Probability + + + + +0 +0.05 +0.1 +0.15 +0.2 + + + + + + + +0 +0.1 +0.2 +0.3 +0.4 +0.5 +Probability @@ -57,9 +66,8 @@ - - - - + + + diff --git a/tests/testthat/_snaps/priors-plot/priors-plot-15-4.svg b/tests/testthat/_snaps/priors-plot/priors-plot-15-4.svg index c06549e0..74fd0e0a 100644 --- a/tests/testthat/_snaps/priors-plot/priors-plot-15-4.svg +++ b/tests/testthat/_snaps/priors-plot/priors-plot-15-4.svg @@ -18,38 +18,47 @@ +Density - - - - + + + + + -0.0 -0.2 -0.4 -0.6 -0.8 -1.0 - +-3 +-2 +-1 +0 +1 +2 +3 + - - - - - - -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 - -Bernoulli -(0.33) -Probability + + + + +0 +0.05 +0.1 +0.15 +0.2 + + + + + + + +0 +0.1 +0.2 +0.3 +0.4 +0.5 +Probability @@ -57,9 +66,8 @@ - - - - + + + diff --git a/tests/testthat/_snaps/priors-plot/priors-plot-15-5.svg b/tests/testthat/_snaps/priors-plot/priors-plot-15-5.svg index f08988bf..17d6f75e 100644 --- a/tests/testthat/_snaps/priors-plot/priors-plot-15-5.svg +++ b/tests/testthat/_snaps/priors-plot/priors-plot-15-5.svg @@ -18,41 +18,48 @@ +name +Density - - - - + + + + + -0.0 -0.2 -0.4 -0.6 -0.8 -1.0 - +-3 +-2 +-1 +0 +1 +2 +3 + - - - - - - -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -name - -~ - -Bernoulli -(0.33) -Probability + + + + +0 +0.05 +0.1 +0.15 +0.2 + + + + + + + +0 +0.1 +0.2 +0.3 +0.4 +0.5 +Probability @@ -60,9 +67,8 @@ - - - - + + + diff --git a/tests/testthat/_snaps/priors-plot/priors-plot-15-6.svg b/tests/testthat/_snaps/priors-plot/priors-plot-15-6.svg index c869b7a7..eb3336b6 100644 --- a/tests/testthat/_snaps/priors-plot/priors-plot-15-6.svg +++ b/tests/testthat/_snaps/priors-plot/priors-plot-15-6.svg @@ -21,73 +21,77 @@ - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 - - - - - - - - - - - - - - -0.0 -0.2 -0.4 -0.6 -0.8 -1.0 - -Bernoulli -(0.33) -Probability +0.00 +0.05 +0.10 +0.15 +0.20 + + + + + + + + + + + +0 +0.1 +0.2 +0.3 +0.4 +0.5 + + + + + + + +-3 +-2 +-1 +0 +1 +2 +3 +Density +Probability diff --git a/tests/testthat/_snaps/priors-plot/priors-plot-15-7.svg b/tests/testthat/_snaps/priors-plot/priors-plot-15-7.svg index 68e6c30b..eb3336b6 100644 --- a/tests/testthat/_snaps/priors-plot/priors-plot-15-7.svg +++ b/tests/testthat/_snaps/priors-plot/priors-plot-15-7.svg @@ -21,73 +21,77 @@ - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 - - - - - - - - - - - - - - -0.0 -0.2 -0.4 -0.6 -0.8 -1.0 - -Br -(0.33) -Probability +0.00 +0.05 +0.10 +0.15 +0.20 + + + + + + + + + + + +0 +0.1 +0.2 +0.3 +0.4 +0.5 + + + + + + + +-3 +-2 +-1 +0 +1 +2 +3 +Density +Probability diff --git a/tests/testthat/_snaps/priors-plot/priors-plot-15-8.svg b/tests/testthat/_snaps/priors-plot/priors-plot-15-8.svg index 159eb00d..eb3336b6 100644 --- a/tests/testthat/_snaps/priors-plot/priors-plot-15-8.svg +++ b/tests/testthat/_snaps/priors-plot/priors-plot-15-8.svg @@ -21,73 +21,77 @@ - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 - - - - - - - - - - - - - - -0.0 -0.2 -0.4 -0.6 -0.8 -1.0 - -Bernoulli -(probability = 0.33) -Probability +0.00 +0.05 +0.10 +0.15 +0.20 + + + + + + + + + + + +0 +0.1 +0.2 +0.3 +0.4 +0.5 + + + + + + + +-3 +-2 +-1 +0 +1 +2 +3 +Density +Probability diff --git a/tests/testthat/_snaps/priors-plot/priors-plot-15-9.svg b/tests/testthat/_snaps/priors-plot/priors-plot-15-9.svg index 5bfbc7ce..eb4eb75d 100644 --- a/tests/testthat/_snaps/priors-plot/priors-plot-15-9.svg +++ b/tests/testthat/_snaps/priors-plot/priors-plot-15-9.svg @@ -21,72 +21,79 @@ - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 - - - - - - - - - - - - - - -0.0 -0.2 -0.4 -0.6 -0.8 -1.0 -xlab +0.00 +0.05 +0.10 +0.15 +0.20 + + + + + + + + + + + +0 +0.1 +0.2 +0.3 +0.4 +0.5 + + + + + + + +-3 +-2 +-1 +0 +1 +2 +3 +xlab ylab -main +Probability +main diff --git a/tests/testthat/_snaps/priors-plot/priors-plot-17-1.svg b/tests/testthat/_snaps/priors-plot/priors-plot-17-1.svg new file mode 100644 index 00000000..4f297b7c --- /dev/null +++ b/tests/testthat/_snaps/priors-plot/priors-plot-17-1.svg @@ -0,0 +1,57 @@ + + + + + + + + + + + + + + + + + +1.0 +1.5 +2.0 +2.5 + + + + + + + +0.0 +0.2 +0.4 +0.6 +0.8 +1.0 + +independent contrast: +Uniform +(-0.5, 1) +Density + + + + + + + + + + diff --git a/tests/testthat/_snaps/priors-plot/priors-plot-20-5-1.svg b/tests/testthat/_snaps/priors-plot/priors-plot-20-5-1.svg new file mode 100644 index 00000000..0acf81bc --- /dev/null +++ b/tests/testthat/_snaps/priors-plot/priors-plot-20-5-1.svg @@ -0,0 +1,95 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0.0 +0.2 +0.4 +0.6 +0.8 +1.0 + + + + + + + + + + + + + + +0.3 +0.4 +0.5 +0.6 +0.7 +0.8 +0.9 +1.0 +dif + +orthonormal contrast: +mSpike +(0) +Probability + + diff --git a/tests/testthat/_snaps/priors-print/priors-print-4.svg b/tests/testthat/_snaps/priors-print/priors-print-4.svg index 01a17dc7..9e121d51 100644 --- a/tests/testthat/_snaps/priors-print/priors-print-4.svg +++ b/tests/testthat/_snaps/priors-print/priors-print-4.svg @@ -77,20 +77,20 @@ * Normal -(0, 1) - -+ - -( -5 -/ -7 -) - -* - -Normal -(-3, 1) +(-3, 1) + ++ + +( +5 +/ +7 +) + +* + +Normal +(0, 1) + diff --git a/tests/testthat/_snaps/priors/prior-mixture-2.svg b/tests/testthat/_snaps/priors/prior-mixture-2.svg index 32d87092..2cd51279 100644 --- a/tests/testthat/_snaps/priors/prior-mixture-2.svg +++ b/tests/testthat/_snaps/priors/prior-mixture-2.svg @@ -27,20 +27,20 @@ * Normal -(0, 1) - -+ - -( -5 -/ -7 -) - -* - -Normal -(-3, 1) +(-3, 1) + ++ + +( +5 +/ +7 +) + +* + +Normal +(0, 1) + diff --git a/tests/testthat/_snaps/priors/prior-mixture-4.svg b/tests/testthat/_snaps/priors/prior-mixture-4.svg index baa9cd23..9aa66672 100644 --- a/tests/testthat/_snaps/priors/prior-mixture-4.svg +++ b/tests/testthat/_snaps/priors/prior-mixture-4.svg @@ -27,36 +27,36 @@ * orthonormal contrast: -mSpike -(0) - -+ - -( -3 -/ -5 -) - -* - -orthonormal contrast: -mNormal -(0, 10) - -+ - -( -1 -/ -5 -) - -* - -orthonormal contrast: -mNormal -(0, 1) +mNormal +(0, 10) + ++ + +( +3 +/ +5 +) + +* + +orthonormal contrast: +mNormal +(0, 1) + ++ + +( +1 +/ +5 +) + +* + +orthonormal contrast: +mSpike +(0) rng(p4, 10000, transform_factor_samples = FALSE) Density diff --git a/tests/testthat/_snaps/priors/prior-mixture-5.svg b/tests/testthat/_snaps/priors/prior-mixture-5.svg index af5b50c8..796ce6b8 100644 --- a/tests/testthat/_snaps/priors/prior-mixture-5.svg +++ b/tests/testthat/_snaps/priors/prior-mixture-5.svg @@ -27,36 +27,36 @@ * orthonormal contrast: -mSpike -(0) - -+ - -( -3 -/ -5 -) - -* - -orthonormal contrast: -mNormal -(0, 10) - -+ - -( -1 -/ -5 -) - -* - -orthonormal contrast: -mNormal -(0, 1) +mNormal +(0, 10) + ++ + +( +3 +/ +5 +) + +* + +orthonormal contrast: +mNormal +(0, 1) + ++ + +( +1 +/ +5 +) + +* + +orthonormal contrast: +mSpike +(0) rng(p4, 10000, transform_factor_samples = TRUE) Density diff --git a/tests/testthat/_snaps/priors/prior-mixture-6.svg b/tests/testthat/_snaps/priors/prior-mixture-6.svg new file mode 100644 index 00000000..fa4f580c --- /dev/null +++ b/tests/testthat/_snaps/priors/prior-mixture-6.svg @@ -0,0 +1,129 @@ + + + + + + + + + + + + +( +1 +/ +3 +) + +* + +Spike +(0) + ++ + +( +1 +/ +3 +) + +* + +Spike +(1) + ++ + +( +1 +/ +3 +) + +* + +Gamma +(5, 10) +rng(p5, 10000, transform_factor_samples = FALSE) +Density + + + + + +0.0 +0.5 +1.0 +1.5 + + + + + + + + + +0 +1 +2 +3 +4 +5 +6 +7 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/priors/prior-mixture-7.svg b/tests/testthat/_snaps/priors/prior-mixture-7.svg new file mode 100644 index 00000000..dbd51eb4 --- /dev/null +++ b/tests/testthat/_snaps/priors/prior-mixture-7.svg @@ -0,0 +1,126 @@ + + + + + + + + + + + + +( +1 +/ +3 +) + +* + +orthonormal contrast: +mSpike +(0) + ++ + +( +1 +/ +3 +) + +* + +orthonormal contrast: +mSpike +(1) + ++ + +( +1 +/ +3 +) + +* + +orthonormal contrast: +mNormal +(0, 1) +rng(p6, 10000, transform_factor_samples = FALSE) +Density + + + + + +-2 +0 +2 +4 + + + + + +0.0 +0.5 +1.0 +1.5 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/priors/prior-mixture-8.svg b/tests/testthat/_snaps/priors/prior-mixture-8.svg new file mode 100644 index 00000000..9fb897df --- /dev/null +++ b/tests/testthat/_snaps/priors/prior-mixture-8.svg @@ -0,0 +1,142 @@ + + + + + + + + + + + + +( +1 +/ +3 +) + +* + +treatment contrast: +Spike +(0, 2) + ++ + +( +1 +/ +3 +) + +* + +treatment contrast: +Spike +(1, 2) + ++ + +( +1 +/ +3 +) + +* + +treatment contrast: +Beta +(3, 1, 2) +rng(p7, 10000, transform_factor_samples = FALSE) +Density + + + + + + + +0.0 +0.2 +0.4 +0.6 +0.8 +1.0 + + + + + +0 +5 +10 +15 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/common-functions.R b/tests/testthat/common-functions.R new file mode 100644 index 00000000..d56a946a --- /dev/null +++ b/tests/testthat/common-functions.R @@ -0,0 +1,392 @@ +# ============================================================================ # +# CONFIGURATION: Set to TRUE to regenerate reference files, FALSE to run tests +# ============================================================================ # +if (!exists("GENERATE_REFERENCE_FILES")) { + GENERATE_REFERENCE_FILES <- FALSE +} + + +test_files_dir <- Sys.getenv("BAYESTOOLS_TEST_FILES_DIR") +if (test_files_dir == "" || !dir.exists(test_files_dir)) { + test_files_dir <- file.path(tempdir(), "BayesTools_test_files") +} + +# Setup directory for saving fitted models +temp_fits_dir <- file.path(test_files_dir, "fits") +temp_marglik_dir <- file.path(test_files_dir, "margliks") +temp_temp_dir <- file.path(test_files_dir, "temp") + +if (!dir.exists(temp_fits_dir)) dir.create(temp_fits_dir, showWarnings = FALSE, recursive = TRUE) +if (!dir.exists(temp_marglik_dir)) dir.create(temp_marglik_dir, showWarnings = FALSE, recursive = TRUE) +if (!dir.exists(temp_temp_dir)) dir.create(temp_temp_dir, showWarnings = FALSE, recursive = TRUE) + +# Set environment variable so other test files can locate pre-fitted models +Sys.setenv(BAYESTOOLS_TEST_FILES_DIR = test_files_dir) + +# Use skip_if_no_fits() for tests that need pre-fitted models. + +# ============================================================================ # +# HELPER FUNCTIONS: Reference File Testing +# ============================================================================ # +require("runjags") + +# Process reference file: save if GENERATE_REFERENCE_FILES=TRUE, test otherwise +test_reference_table <- function(table, filename, info_msg = NULL, + print_dir = REFERENCE_DIR) { + if (GENERATE_REFERENCE_FILES) { + # Save mode + if (!dir.exists(print_dir)) { + dir.create(print_dir, recursive = TRUE) + } + writeLines(capture_output_lines(table, print = TRUE, width = 150), + file.path(print_dir, filename)) + } else { + # Test mode + ref_file <- file.path(print_dir, filename) + if (file.exists(ref_file)) { + expected_output <- readLines(ref_file, warn = FALSE) + actual_output <- capture_output_lines(table, print = TRUE, width = 150) + expect_equal(actual_output, expected_output, info = info_msg) + } else { + skip(paste("Reference file", filename, "not found.")) + } + } +} + +test_reference_text <- function(text, filename, info_msg = NULL, + print_dir = REFERENCE_DIR) { + if (GENERATE_REFERENCE_FILES) { + # Save mode + if (!dir.exists(print_dir)) { + dir.create(print_dir, recursive = TRUE) + } + writeLines(text, file.path(print_dir, filename)) + } else { + # Test mode + ref_file <- file.path(print_dir, filename) + if (file.exists(ref_file)) { + expected_output <- readLines(ref_file, warn = FALSE) + expected_output <- paste0(expected_output, collapse = "\n") + expect_equal(text, expected_output, info = info_msg) + } else { + skip(paste("Reference file", filename, "not found.")) + } + } +} + +# Skip if pre-fitted models are not available +skip_if_no_fits <- function() { + model_registry_file <- file.path(test_files_dir, "model_registry.RDS") + if (!file.exists(model_registry_file)) { + skip("Pre-fitted models not found. Run test-00-model-fits.R first.") + } +} + +# ============================================================================ # +# STANDARD TEST FIXTURES: Reusable Prior Definitions +# ============================================================================ # +# These fixtures reduce duplication across test files. Use these instead of +# creating new prior definitions when testing standard functionality. + +# Standard log_posterior function for marginal likelihood tests +# Returns 0 (log of 1) for prior-only models +STANDARD_LOG_POSTERIOR <- function(parameters, data) { + + return(0) +} + +if (isNamespaceLoaded("BayesTools")) { + + # Standard simple priors (commonly used across tests) + STANDARD_PRIORS <- list( + normal = prior("normal", list(0, 1)), + normal_trunc = prior("normal", list(0, 1), list(0, Inf)), + lognormal = prior("lognormal", list(0, 0.5)), + t = prior("t", list(0, 0.5, 5)), + + cauchy = prior("Cauchy", list(0, 1)), + cauchy_trunc = prior("Cauchy", list(1, 0.1), list(-10, 0)), + gamma = prior("gamma", list(2, 1)), + invgamma = prior("invgamma", list(3, 2)), + invgamma_trunc = prior("invgamma", list(3, 2), list(1, 3)), + exp = prior("exp", list(1.5)), + beta = prior("beta", list(3, 2)), + uniform = prior("uniform", list(0, 1)), + spike = prior("spike", list(0)), + PET = prior_PET("normal", list(0, 1)), + PEESE = prior_PEESE("gamma", list(1, 1)) + ) + + # Standard factor priors (for contrast testing) + STANDARD_FACTOR_PRIORS <- list( + orthonormal = prior_factor("mnormal", list(mean = 0, sd = 1), contrast = "orthonormal"), + meandif = prior_factor("mnormal", list(mean = 0, sd = 1), contrast = "meandif"), + treatment = prior_factor("normal", list(mean = 0, sd = 1), contrast = "treatment"), + independent = prior_factor("normal", list(mean = 0, sd = 1), contrast = "independent"), + orth_cauchy = prior_factor("mcauchy", list(location = 0, scale = 1), contrast = "orthonormal"), + orth_spike = prior_factor("point", list(0), contrast = "orthonormal") + ) + + # Complete prior collections for comprehensive testing + ALL_SIMPLE_PRIORS <- list( + p1 = prior("normal", list(0, 1)), + p2 = prior("normal", list(0, 1), list(1, Inf)), + p3 = prior("lognormal", list(0, 0.5)), + p4 = prior("t", list(0, 0.5, 5)), + p5 = prior("Cauchy", list(1, 0.1), list(-10, 0)), + p6 = prior("gamma", list(2, 1)), + p7 = prior("invgamma", list(3, 2), list(1, 3)), + p8 = prior("exp", list(1.5)), + p9 = prior("beta", list(3, 2)), + p10 = prior("uniform", list(1, 5)), + PET = prior_PET("normal", list(0, 1)), + PEESE = prior_PEESE("gamma", list(1, 1)) + ) + + ALL_VECTOR_PRIORS <- list( + mnormal = prior("mnormal", list(mean = 0, sd = 1, K = 3)), + mcauchy = prior("mcauchy", list(location = 0, scale = 1.5, K = 2)), + mt = prior("mt", list(location = 2, scale = 0.5, df = 5, K = 2)) + ) + + ALL_FACTOR_PRIORS <- list( + orthonormal = prior_factor("mnormal", list(mean = 0, sd = 1), contrast = "orthonormal"), + meandif = prior_factor("mnormal", list(mean = 0, sd = 1), contrast = "meandif"), + treatment = prior_factor("normal", list(mean = 0, sd = 1), contrast = "treatment"), + independent = prior_factor("normal", list(mean = 0, sd = 1), contrast = "independent") + ) +} +# ============================================================================ # +# HELPER FUNCTIONS: Prior Distribution Testing +# ============================================================================ # + +#' Test a prior distribution for consistency +#' +#' Validates that a prior distribution's rng, pdf, cdf, quant, mean, and sd + +' functions work correctly and are mutually consistent. +#' +#' @param prior A prior object to test +#' @param skip_moments Logical; skip mean/sd validation (for distributions +#' with undefined moments like Cauchy) +#' @return invisible(); used for visual regression testing +test_prior <- function(prior, skip_moments = FALSE) { + set.seed(1) + # tests rng and print function (for plot) + samples <- rng(prior, 100000) + if (is.prior.discrete(prior)) { + barplot(table(samples) / length(samples), main = print(prior, plot = TRUE), + width = 1 / (max(samples) + 1), space = 0, + xlim = c(-0.25, max(samples) + 0.25)) + } else if (is.prior.spike_and_slab(prior)) { + xh <- hist(samples[samples != 0], breaks = 50, plot = FALSE) + xh$density <- xh$density * mean(samples != 0) + plot(xh, main = print(prior, plot = TRUE), freq = FALSE) + } else { + hist(samples, main = print(prior, plot = TRUE), breaks = 50, freq = FALSE) + } + # tests density function + lines(prior, individual = TRUE) + + # tests quantile function + if (!is.prior.spike_and_slab(prior) && !is.prior.mixture(prior)) { + abline(v = quant(prior, 0.5), col = "blue", lwd = 2) + } + # tests that cdf(quant(x)) == x + + if (!is.prior.point(prior) && !is.prior.discrete(prior) && + !is.prior.spike_and_slab(prior) && !is.prior.mixture(prior)) { + expect_equal(.25, cdf(prior, quant(prior, 0.25)), tolerance = 1e-4) + expect_equal(.25, ccdf(prior, quant(prior, 0.75)), tolerance = 1e-4) + } + # test mean and sd functions + if (!skip_moments) { + expect_equal(mean(samples), mean(prior), tolerance = 1e-2) + expect_equal(sd(samples), sd(prior), tolerance = 1e-2) + } + return(invisible()) +} + +#' Test a weight function prior distribution +#' +#' Validates weight function priors with multiple components. +#' +#' @param prior A weight function prior object +#' @param skip_moments Logical; skip moment validation +#' @return invisible() +test_weightfunction <- function(prior, skip_moments = FALSE) { + set.seed(1) + # tests rng and print function (for plot) + samples <- rng(prior, 10000) + densities <- density(prior, individual = TRUE) + + if (!all(names(prior$parameters) %in% c("steps", "alpha1", "alpha2"))) { + quantiles <- mquant(prior, 0.5) + } + + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) + par(mfcol = c(1, ncol(samples) - 1)) + + for (i in 1:(ncol(samples) - 1)) { + hist(samples[, i], main = print(prior, plot = TRUE), breaks = 50, freq = FALSE) + lines(densities[[i]]) + if (!all(names(prior$parameters) %in% c("steps", "alpha1", "alpha2"))) { + abline(v = quantiles[i], col = "blue", lwd = 2) + } + if (!grepl("fixed", prior$distribution) && + !all(names(prior$parameters) %in% c("steps", "alpha1", "alpha2"))) { + expect_equal(.25, mcdf(prior, mquant(prior, 0.25)[, i])[, i], tolerance = 1e-5) + expect_equal(.25, mccdf(prior, mquant(prior, 0.75)[, i])[, i], tolerance = 1e-5) + } + if (!skip_moments) { + expect_equal(apply(samples, 2, mean), mean(prior), tolerance = 1e-2) + expect_equal(apply(samples, 2, sd), sd(prior), tolerance = 1e-2) + } + } + return(invisible()) +} + +#' Test an orthonormal contrast prior +#' +#' Validates orthonormal factor priors. +#' +#' @param prior An orthonormal prior object +#' @param skip_moments Logical; skip moment validation +#' @return invisible() +test_orthonormal <- function(prior, skip_moments = FALSE) { + set.seed(1) + # tests rng and print function (for plot) + samples <- rng(prior, 100000) + samples <- samples[abs(samples) < 10] + hist(samples, main = print(prior, plot = TRUE), breaks = 50, freq = FALSE) + # tests density function + lines(prior, individual = TRUE) + # tests quantile function + abline(v = mquant(prior, 0.5), col = "blue", lwd = 2) + # tests that mcdf(mquant(x)) == x + if (!is.prior.point(prior)) { + expect_equal(.25, mcdf(prior, mquant(prior, 0.25)), tolerance = 1e-5) + expect_equal(.25, mccdf(prior, mquant(prior, 0.75)), tolerance = 1e-5) + } + # test mean and sd functions + if (!skip_moments) { + expect_equal(mean(samples), mean(prior), tolerance = 1e-2) + expect_equal(sd(samples), sd(prior), tolerance = 1e-2) + } + return(invisible()) +} + +#' Test a mean difference contrast prior +#' +#' Validates meandif factor priors. +#' +#' @param prior A meandif prior object +#' @param skip_moments Logical; skip moment validation +#' @return invisible() +test_meandif <- function(prior, skip_moments = FALSE) { + set.seed(1) + # tests rng and print function (for plot) + samples <- rng(prior, 100000) + samples <- samples[abs(samples) < 10] + hist(samples, main = print(prior, plot = TRUE), breaks = 50, freq = FALSE) + # tests density function + lines(prior, individual = TRUE) + # tests quantile function + abline(v = mquant(prior, 0.5), col = "blue", lwd = 2) + # tests that mcdf(mquant(x)) == x + if (!is.prior.point(prior)) { + expect_equal(.25, mcdf(prior, mquant(prior, 0.25)), tolerance = 1e-5) + expect_equal(.25, mccdf(prior, mquant(prior, 0.75)), tolerance = 1e-5) + } + # test mean and sd functions + if (!skip_moments) { + expect_equal(mean(samples), mean(prior), tolerance = 1e-2) + expect_equal(sd(samples), sd(prior), tolerance = 1e-2) + } + return(invisible()) +} + +# Helper function to save fitted models and register metadata +save_fit <- function(fit, name, marglik = NULL, simple_priors = FALSE, vector_priors = FALSE, + factor_priors = FALSE, pub_bias_priors = FALSE, + weightfunction_priors = FALSE, spike_and_slab_priors = FALSE, + mixture_priors = FALSE, formulas = FALSE, + random_effects = FALSE, interactions = FALSE, + expression_priors = FALSE, multi_formula = FALSE, + autofit = FALSE, parallel = FALSE, thinning = FALSE, + add_parameters = FALSE, note = "") { + + saveRDS(fit, file = file.path(temp_fits_dir, paste0(name, ".RDS"))) + + # Save marglik if provided + if (!is.null(marglik)) { + saveRDS(marglik, file = file.path(temp_marglik_dir, paste0(name, ".RDS"))) + } + + # Return model metadata entry for registry + list( + fit = fit, + marglik = marglik, + registry_entry = data.frame( + model_name = name, + has_marglik = !is.null(marglik), + simple_priors = simple_priors, + vector_priors = vector_priors, + factor_priors = factor_priors, + pub_bias_priors = pub_bias_priors, + weightfunction_priors = weightfunction_priors, + spike_and_slab_priors = spike_and_slab_priors, + mixture_priors = mixture_priors, + formulas = formulas, + random_effects = random_effects, + interactions = interactions, + expression_priors = expression_priors, + multi_formula = multi_formula, + autofit = autofit, + parallel = parallel, + thinning = thinning, + add_parameters = add_parameters, + note = note, + stringsAsFactors = FALSE + ) + ) +} + +# Skip model fitting if cached fits exist and ROBMA_TEST_SKIP_REFIT is TRUE +skip_refit_if_cached <- function(name) { + # refitting settings + skip_refit <- Sys.getenv("BAYESTOOLS_TEST_SKIP_REFIT") + skip_refit <- skip_refit != "" && as.logical(skip_refit) + + # fitted indicator + fitted_indicator <- file.exists(file.path(temp_temp_dir, paste0(name, ".txt"))) + + if (skip_refit && fitted_indicator) { + skip("Skipping model refitting: cached fits exist and BAYESTOOLS_TEST_SKIP_REFIT=TRUE.") + } + + # tests are not going to be skipped -- add fits done indicator into `temp_temp_dir` + file.create(file.path(temp_temp_dir, paste0(name, ".txt"))) +} + +# Clean cached fitted models and margliks +clean_cached_fits <- function(name) { + + if (!missing(name)) { + # remove only the specific `name`` fitted indicator files side-effects from `temp_temp_dir` + file.remove(file.path(temp_temp_dir, paste0(name, ".txt"))) + } else { + # Remove all cached files from test directories + unlink(temp_fits_dir, recursive = TRUE) + unlink(temp_marglik_dir, recursive = TRUE) + unlink(temp_temp_dir, recursive = TRUE) + + # Recreate empty directories + dir.create(temp_fits_dir, showWarnings = FALSE, recursive = TRUE) + dir.create(temp_marglik_dir, showWarnings = FALSE, recursive = TRUE) + dir.create(temp_temp_dir, showWarnings = FALSE, recursive = TRUE) + } + + return(invisible(TRUE)) +} diff --git a/tests/testthat/test-00-model-fits.R b/tests/testthat/test-00-model-fits.R new file mode 100644 index 00000000..88ba5ab2 --- /dev/null +++ b/tests/testthat/test-00-model-fits.R @@ -0,0 +1,2054 @@ +# ============================================================================ # +# TEST FILE: Model Fits for Reuse Across Tests +# ============================================================================ # +# +# PURPOSE: +# Centralized model fitting for all JAGS models used across the test suite. +# Fitted models are saved to temp directory for reuse in other test files. +# This reduces redundant MCMC sampling and speeds up the overall test suite. +# +# DEPENDENCIES: +# - rjags, runjags, bridgesampling: For model fitting +# +# SKIP CONDITIONS: +# - skip_on_cran(): Long-running model fitting +# - skip_if_not_installed("rjags") +# +# MODELS/FIXTURES: +# - Creates all pre-fitted models used by other test files +# - Models saved to BAYESTOOLS_TEST_FITS_DIR environment variable +# - Maintains model_registry.RDS with metadata +# +# TAGS: @slow, @JAGS, @model-fits +# ============================================================================ # + +# This file contains all model fitting procedures used across the test suite. +# Fitted models are saved to a temporary directory for reuse in other tests. +# This reduces redundant MCMC sampling and speeds up the overall test suite. + +skip_on_cran() +skip_if_not_installed("rjags") + +# Load common test helpers +source(testthat::test_path("common-functions.R")) +skip_refit_if_cached("model-fit") + +# Initialize model registry to track metadata about each fitted model +model_registry <- list() + +# ============================================================================ # +# SECTION 1: SIMPLE PRIOR DISTRIBUTIONS +# ============================================================================ # +test_that("Simple prior models fit correctly", { + + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + set.seed(1) + data <- list( + x = rnorm(50, 0, .5), + N = 50 + ) + + # Model 1: Normal and truncated normal priors + priors_simple_normal <- list( + m = prior("normal", list(0, 1)), + s = prior("normal", list(0, 1), list(0, Inf)) + ) + model_syntax <- + "model + { + for(i in 1:N){ + x[i] ~ dnorm(m, pow(s, -2)) + } + }" + + fit_simple_normal <- JAGS_fit(model_syntax, data, priors_simple_normal, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 1) + + # Compute marginal likelihood for model averaging + log_posterior_simple_normal <- function(parameters, data){ + sum(stats::dnorm(data$x, parameters[["m"]], parameters[["s"]], log = TRUE)) + } + marglik_simple_normal <- JAGS_bridgesampling(fit_simple_normal, + log_posterior = log_posterior_simple_normal, + data = data, prior_list = priors_simple_normal) + + result <- save_fit(fit_simple_normal, "fit_simple_normal", + marglik = marglik_simple_normal, + simple_priors = TRUE, + note = "Normal and truncated normal priors with data") + model_registry[["fit_simple_normal"]] <<- result$registry_entry + fit_simple_normal <- result$fit + + # Model 2: Spike and normal priors (for model averaging) + priors_simple_spike <- list( + m = prior("spike", list(0)), + s = prior("normal", list(0, 1), list(0, Inf)) + ) + + fit_simple_spike <- JAGS_fit(model_syntax, data, priors_simple_spike, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 2) + + # Compute marginal likelihood for model averaging + marglik_simple_spike <- JAGS_bridgesampling(fit_simple_spike, + log_posterior = log_posterior_simple_normal, + data = data, prior_list = priors_simple_spike) + + result <- save_fit(fit_simple_spike, "fit_simple_spike", + marglik = marglik_simple_spike, + simple_priors = TRUE, + note = "Spike and truncated normal priors with data (for model averaging)") + model_registry[["fit_simple_spike"]] <<- result$registry_entry + fit_simple_spike <- result$fit + + # Model 3: Various prior distributions + priors_various <- list( + p1 = prior("normal", list(0, 1)), + p2 = prior("lognormal", list(0, .5)), + p3 = prior("t", list(0, .5, 5)), + p4 = prior("Cauchy", list(1, 0.1), list(-10, 0)), + p5 = prior("gamma", list(2, 1)), + p6 = prior("invgamma", list(3, 2), list(1, 3)), + p7 = prior("exp", list(1.5)), + p8 = prior("beta", list(3, 2)), + p9 = prior("uniform", list(1, 5)), + p10 = prior("point", list(1)) + ) + + model_syntax_simple <- "model{}" + + fit_simple_various <- suppressWarnings(JAGS_fit(model_syntax_simple, data = NULL, prior_list = priors_various, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 1)) + result <- save_fit(fit_simple_various, "fit_simple_various", + simple_priors = TRUE, + note = "Various univariate distributions: normal, lognormal, t, Cauchy, gamma, invgamma, exp, beta, uniform, point") + model_registry[["fit_simple_various"]] <<- result$registry_entry + fit_simple_various <- result$fit + + # Model 4: PET and PEESE priors + priors_pub_bias <- list( + PET = prior_PET("normal", list(0, 1)), + PEESE = prior_PEESE("gamma", list(1, 1)) + ) + + model_syntax_pb <- "model{}" + + fit_simple_pub_bias <- suppressWarnings(JAGS_fit(model_syntax_pb, data = NULL, prior_list = priors_pub_bias, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 1)) + result <- save_fit(fit_simple_pub_bias, "fit_simple_pub_bias", + pub_bias_priors = TRUE, + note = "PET and PEESE priors for publication bias") + model_registry[["fit_simple_pub_bias"]] <<- result$registry_entry + fit_simple_pub_bias <- result$fit + + # Model 5: Test with thinning parameter + priors_thin <- list( + mu = prior("normal", list(0, 1)) + ) + model_syntax_thin <- "model{}" + + fit_simple_thin <- suppressWarnings(JAGS_fit(model_syntax_thin, data = NULL, prior_list = priors_thin, + chains = 2, adapt = 100, burnin = 150, sample = 300, thin = 3, seed = 2)) + result <- save_fit(fit_simple_thin, "fit_simple_thin", + simple_priors = TRUE, thinning = TRUE, + note = "Simple normal prior with thinning parameter (thin=3)") + model_registry[["fit_simple_thin"]] <<- result$registry_entry + fit_simple_thin <- result$fit +}) + + +# ============================================================================ # +# SECTION 1B: MODELS FOR SUMMARY TABLES TESTING +# ============================================================================ # +test_that("Summary tables models fit correctly", { + + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + set.seed(1) + data_summary <- list( + x = rnorm(20, 0, 1), + N = 20 + ) + + model_syntax_summary <- + "model + { + for(i in 1:N){ + x[i] ~ dnorm(m, 1) + } + }" + + # Log posterior for summary tables (constant, no data dependency) + log_posterior_summary <- function(parameters, data){ + return(0) + } + + # Model 1: Normal prior with prior_none weightfunction + priors_summary0 <- list( + m = prior("normal", list(0, 1)), + omega = prior_none() + ) + + fit_summary0 <- JAGS_fit(model_syntax_summary, data_summary, priors_summary0, + chains = 1, adapt = 100, burnin = 150, sample = 500, seed = 0) + + marglik_summary0 <- JAGS_bridgesampling(fit_summary0, + log_posterior = log_posterior_summary, + data = data_summary, prior_list = priors_summary0) + + result <- save_fit(fit_summary0, "fit_summary0", + marglik = marglik_summary0, + simple_priors = TRUE, weightfunction_priors = TRUE, + note = "Model for summary tables with no weightfunction") + model_registry[["fit_summary0"]] <<- result$registry_entry + fit_summary0 <- result$fit + + # Model 2: Normal prior with one-sided weightfunction (2 intervals) + priors_summary1 <- list( + m = prior("normal", list(0, .5)), + omega = prior_weightfunction("one.sided", list(c(0.05), c(1, 1))) + ) + + fit_summary1 <- JAGS_fit(model_syntax_summary, data_summary, priors_summary1, + chains = 1, adapt = 100, burnin = 150, sample = 500, seed = 1) + + marglik_summary1 <- JAGS_bridgesampling(fit_summary1, + log_posterior = log_posterior_summary, + data = data_summary, prior_list = priors_summary1) + + result <- save_fit(fit_summary1, "fit_summary1", + marglik = marglik_summary1, + simple_priors = TRUE, weightfunction_priors = TRUE, + note = "Model for summary tables with one-sided weightfunction (cutpoint at .05)") + model_registry[["fit_summary1"]] <<- result$registry_entry + fit_summary1 <- result$fit + + # Model 3: Normal prior with one-sided weightfunction (3 intervals) + priors_summary2 <- list( + m = prior("normal", list(0, .3)), + omega = prior_weightfunction("one.sided", list(c(0.05, 0.50), c(1, 1, 1))) + ) + + fit_summary2 <- JAGS_fit(model_syntax_summary, data_summary, priors_summary2, + chains = 1, adapt = 100, burnin = 150, sample = 500, seed = 1) + + marglik_summary2 <- JAGS_bridgesampling(fit_summary2, + log_posterior = log_posterior_summary, + data = data_summary, prior_list = priors_summary2) + + result <- save_fit(fit_summary2, "fit_summary2", + marglik = marglik_summary2, + simple_priors = TRUE, weightfunction_priors = TRUE, + note = "Model for summary tables with one-sided weightfunction (cutpoints at .05, .50)") + model_registry[["fit_summary2"]] <<- result$registry_entry + fit_summary2 <- result$fit + + # Model 4: Normal prior with fixed weightfunction + priors_summary3 <- list( + m = prior("normal", list(0, .3)), + omega = prior_weightfunction("two.sided.fixed", list(0.20, c(.3, 1))) + ) + + fit_summary3 <- JAGS_fit(model_syntax_summary, data_summary, priors_summary3, + chains = 1, adapt = 100, burnin = 150, sample = 500, seed = 1) + + marglik_summary3 <- JAGS_bridgesampling(fit_summary3, + log_posterior = log_posterior_summary, + data = data_summary, prior_list = priors_summary3) + + result <- save_fit(fit_summary3, "fit_summary3", + marglik = marglik_summary3, + simple_priors = TRUE, weightfunction_priors = TRUE, + note = "Model for summary tables with fixed weightfunction") + model_registry[["fit_summary3"]] <<- result$registry_entry + fit_summary3 <- result$fit +}) + + +# ============================================================================ # +# SECTION 2: VECTOR PRIOR DISTRIBUTIONS +# ============================================================================ # +test_that("Vector prior models fit correctly", { + + skip_if_not_installed("rjags") + + # Multivariate normal + priors_mnormal <- list( + p1 = prior("mnormal", list(mean = 0, sd = 1, K = 3)) + ) + + model_syntax_vec <- "model{}" + + fit_vector_mnormal <- suppressWarnings(JAGS_fit(model_syntax_vec, data = NULL, prior_list = priors_mnormal, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 1)) + result <- save_fit(fit_vector_mnormal, "fit_vector_mnormal", + vector_priors = TRUE, + note = "Multivariate normal prior (K=3)") + model_registry[["fit_vector_mnormal"]] <<- result$registry_entry + fit_vector_mnormal <- result$fit + + # Multivariate cauchy + priors_mcauchy <- list( + p1 = prior("mcauchy", list(location = 0, scale = 1.5, K = 2)) + ) + + model_syntax_mc <- "model{}" + + fit_vector_mcauchy <- suppressWarnings(JAGS_fit(model_syntax_mc, data = NULL, prior_list = priors_mcauchy, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 2)) + result <- save_fit(fit_vector_mcauchy, "fit_vector_mcauchy", + vector_priors = TRUE, + note = "Multivariate Cauchy prior (K=2)") + model_registry[["fit_vector_mcauchy"]] <<- result$registry_entry + fit_vector_mcauchy <- result$fit + + # Multivariate t + priors_mt <- list( + p1 = prior("mt", list(location = 2, scale = 0.5, df = 5, K = 2)) + ) + + model_syntax_mt <- "model{}" + + fit_vector_mt <- suppressWarnings(JAGS_fit(model_syntax_mt, data = NULL, prior_list = priors_mt, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 3)) + result <- save_fit(fit_vector_mt, "fit_vector_mt", + vector_priors = TRUE, + note = "Multivariate t prior with df=5 (K=2)") + model_registry[["fit_vector_mt"]] <<- result$registry_entry + fit_vector_mt <- result$fit +}) + + +# ============================================================================ # + +# SECTION 3: FACTOR PRIOR DISTRIBUTIONS +# ============================================================================ # +test_that("Factor prior models fit correctly", { + + skip_if_not_installed("rjags") + + # Orthonormal contrast + priors_orthonormal <- list( + p1 = prior_factor("mnorm", list(mean = 0, sd = 1), contrast = "orthonormal") + ) + attr(priors_orthonormal[[1]], "levels") <- 3 + + model_syntax_orth <- "model{}" + + fit_factor_orthonormal <- suppressWarnings(JAGS_fit(model_syntax_orth, data = NULL, prior_list = priors_orthonormal, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 1)) + result <- save_fit(fit_factor_orthonormal, "fit_factor_orthonormal", + factor_priors = TRUE, + note = "Orthonormal contrast with 3 levels") + model_registry[["fit_factor_orthonormal"]] <<- result$registry_entry + fit_factor_orthonormal <- result$fit + + # Treatment contrast + priors_treatment <- list( + p1 = prior_factor("beta", list(alpha = 1, beta = 1), contrast = "treatment") + ) + attr(priors_treatment[[1]], "levels") <- 2 + + model_syntax_treat <- "model{}" + + fit_factor_treatment <- suppressWarnings(JAGS_fit(model_syntax_treat, data = NULL, prior_list = priors_treatment, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 2)) + result <- save_fit(fit_factor_treatment, "fit_factor_treatment", + factor_priors = TRUE, + note = "Treatment contrast with 2 levels and beta prior") + model_registry[["fit_factor_treatment"]] <<- result$registry_entry + fit_factor_treatment <- result$fit + + # Independent contrast + priors_independent <- list( + p1 = prior_factor("gamma", list(shape = 2, rate = 3), contrast = "independent") + ) + attr(priors_independent[[1]], "levels") <- 3 + + model_syntax_ind <- "model{}" + + fit_factor_independent <- suppressWarnings(JAGS_fit(model_syntax_ind, data = NULL, prior_list = priors_independent, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 3)) + result <- save_fit(fit_factor_independent, "fit_factor_independent", + factor_priors = TRUE, + note = "Independent contrast with 3 levels and gamma prior") + model_registry[["fit_factor_independent"]] <<- result$registry_entry + fit_factor_independent <- result$fit + + # Meandif contrast + priors_meandif <- list( + p1 = prior_factor("mnorm", list(mean = 0, sd = 0.5), contrast = "meandif") + ) + attr(priors_meandif[[1]], "levels") <- 3 + + model_syntax_md <- "model{}" + + fit_factor_meandif <- suppressWarnings(JAGS_fit(model_syntax_md, data = NULL, prior_list = priors_meandif, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 4)) + result <- save_fit(fit_factor_meandif, "fit_factor_meandif", + factor_priors = TRUE, + note = "Meandif contrast with 3 levels") + model_registry[["fit_factor_meandif"]] <<- result$registry_entry + fit_factor_meandif <- result$fit +}) + + +# ============================================================================ # +# SECTION 4: WEIGHTFUNCTION PRIORS +# ============================================================================ # +test_that("Weightfunction prior models fit correctly", { + + skip_if_not_installed("rjags") + + # One-sided weightfunction (2 intervals) + priors_wf_onesided2 <- list( + prior_weightfunction("one.sided", list(c(.05), c(1, 1))) + ) + + model_syntax_wf1 <- "model{}" + + fit_weightfunction_onesided2 <- suppressWarnings(JAGS_fit(model_syntax_wf1, data = NULL, prior_list = priors_wf_onesided2, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 1)) + result <- save_fit(fit_weightfunction_onesided2, "fit_weightfunction_onesided2", + weightfunction_priors = TRUE, + note = "One-sided weightfunction with 2 intervals (cutpoint at .05)") + model_registry[["fit_weightfunction_onesided2"]] <<- result$registry_entry + fit_weightfunction_onesided2 <- result$fit + + # One-sided weightfunction (3 intervals) + priors_wf_onesided3 <- list( + prior_weightfunction("one.sided", list(c(.05, 0.10), c(1, 2, 3))) + ) + + model_syntax_wf2 <- "model{}" + + fit_weightfunction_onesided3 <- suppressWarnings(JAGS_fit(model_syntax_wf2, data = NULL, prior_list = priors_wf_onesided3, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 2)) + result <- save_fit(fit_weightfunction_onesided3, "fit_weightfunction_onesided3", + weightfunction_priors = TRUE, + note = "One-sided weightfunction with 3 intervals (cutpoints at .05, .10)") + model_registry[["fit_weightfunction_onesided3"]] <<- result$registry_entry + fit_weightfunction_onesided3 <- result$fit + + # Two-sided weightfunction + priors_wf_twosided <- list( + prior_weightfunction("two.sided", list(c(.05), c(1, 1))) + ) + + model_syntax_wf3 <- "model{}" + + fit_weightfunction_twosided <- suppressWarnings(JAGS_fit(model_syntax_wf3, data = NULL, prior_list = priors_wf_twosided, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 3)) + result <- save_fit(fit_weightfunction_twosided, "fit_weightfunction_twosided", + weightfunction_priors = TRUE, + note = "Two-sided weightfunction with cutpoint at .05") + model_registry[["fit_weightfunction_twosided"]] <<- result$registry_entry + fit_weightfunction_twosided <- result$fit + + # One-sided fixed weightfunction + priors_wf_fixed <- list( + prior_weightfunction("one.sided.fixed", list(c(.05), c(1, .5))) + ) + + model_syntax_wf4 <- "model{}" + + fit_weightfunction_fixed <- suppressWarnings(JAGS_fit(model_syntax_wf4, data = NULL, prior_list = priors_wf_fixed, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 4)) + result <- save_fit(fit_weightfunction_fixed, "fit_weightfunction_fixed", + weightfunction_priors = TRUE, + note = "One-sided fixed weightfunction (weights: 1, .5)") + model_registry[["fit_weightfunction_fixed"]] <<- result$registry_entry + fit_weightfunction_fixed <- result$fit +}) + + +# ============================================================================ # +# SECTION 5: SPIKE-AND-SLAB PRIORS +# ============================================================================ # +test_that("Spike-and-slab prior models fit correctly", { + + skip_if_not_installed("rjags") + + # Simple spike-and-slab + priors_spike_slab_simple <- list( + "mu" = prior_spike_and_slab(prior("normal", list(0, 1)), + prior_inclusion = prior("beta", list(1,1))) + ) + + model_syntax_ss1 <- "model{}" + + fit_spike_slab_simple <- suppressWarnings(JAGS_fit(model_syntax_ss1, data = NULL, prior_list = priors_spike_slab_simple, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 1)) + result <- save_fit(fit_spike_slab_simple, "fit_spike_slab_simple", + spike_and_slab_priors = TRUE, + note = "Simple spike-and-slab with normal alternative and beta inclusion prior") + model_registry[["fit_spike_slab_simple"]] <<- result$registry_entry + fit_spike_slab_simple <- result$fit + + # Spike-and-slab with factor prior + priors_spike_slab_factor <- list( + "beta" = prior_spike_and_slab(prior_factor("mnormal", list(0, 1), contrast = "orthonormal"), + prior_inclusion = prior("beta", list(1,1))) + ) + + # Set levels attribute on the factor prior component within the spike_and_slab mixture + # The spike_and_slab prior contains multiple components; we need to set levels on the factor component + components <- attr(priors_spike_slab_factor$beta, "components") + alternative_idx <- which(components == "alternative") + # Set to 3 levels for a 3-level factor (A, B, C) + attr(priors_spike_slab_factor$beta[[alternative_idx]], "levels") <- 3 + + model_syntax_ss2 <- "model{}" + + fit_spike_slab_factor <- suppressWarnings(JAGS_fit(model_syntax_ss2, data = NULL, prior_list = priors_spike_slab_factor, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 2)) + result <- save_fit(fit_spike_slab_factor, "fit_spike_slab_factor", + spike_and_slab_priors = TRUE, factor_priors = TRUE, + note = "Spike-and-slab with orthonormal factor prior (3 levels) as alternative") + model_registry[["fit_spike_slab_factor"]] <<- result$registry_entry + fit_spike_slab_factor <- result$fit +}) + + +# ============================================================================ # +# SECTION 6: MIXTURE PRIORS +# ============================================================================ # +test_that("Mixture prior models fit correctly", { + + skip_if_not_installed("rjags") + + # Simple mixture + priors_mixture_simple <- list( + "mu" = prior_mixture( + list( + prior("normal", list(0, 1), prior_weights = 1), + prior("normal", list(-3, 1), prior_weights = 5), + prior("gamma", list(5, 10), prior_weights = 1) + ), + is_null = c(T, F, T) + ) + ) + + model_syntax_mix1 <- "model{}" + + fit_mixture_simple <- suppressWarnings(JAGS_fit(model_syntax_mix1, data = NULL, prior_list = priors_mixture_simple, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 1)) + result <- save_fit(fit_mixture_simple, "fit_mixture_simple", + mixture_priors = TRUE, + note = "Mixture of 3 components (2 normals, 1 gamma) with is_null flags") + model_registry[["fit_mixture_simple"]] <<- result$registry_entry + fit_mixture_simple <- result$fit + + # Mixture with components + priors_mixture_components <- list( + "beta" = prior_mixture( + list( + prior("normal", list(0, 1), prior_weights = 1), + prior("normal", list(-3, 1), prior_weights = 5) + ), + components = c("b", "a") + ) + ) + + model_syntax_mix2 <- "model{}" + + fit_mixture_components <- suppressWarnings(JAGS_fit(model_syntax_mix2, data = NULL, prior_list = priors_mixture_components, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 2)) + result <- save_fit(fit_mixture_components, "fit_mixture_components", + mixture_priors = TRUE, + note = "Mixture with named components (a, b)") + model_registry[["fit_mixture_components"]] <<- result$registry_entry + fit_mixture_components <- result$fit + + # Mixture with spike + priors_mixture_spike <- list( + "gamma" = prior_mixture( + list( + prior("spike", list(2)), + prior("normal", list(-3, 1)) + ) + ) + ) + + model_syntax_mix3 <- "model{}" + + fit_mixture_spike <- suppressWarnings(JAGS_fit(model_syntax_mix3, data = NULL, prior_list = priors_mixture_spike, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 3)) + result <- save_fit(fit_mixture_spike, "fit_mixture_spike", + mixture_priors = TRUE, + note = "Mixture containing spike prior at value 2") + model_registry[["fit_mixture_spike"]] <<- result$registry_entry + fit_mixture_spike <- result$fit +}) + + +# ============================================================================ # +# SECTION 7: FORMULA-BASED MODELS (SIMPLE REGRESSION) +# ============================================================================ # +test_that("Simple formula-based regression models fit correctly", { + + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + set.seed(1) + data_formula <- data.frame( + x_cont1 = rnorm(100), + x_fac2t = factor(rep(c("A", "B"), 50), levels = c("A", "B")), + x_fac3o = factor(rep(c("A", "B", "C"), length.out = 100), levels = c("A", "B", "C")) + ) + data <- list( + y = rnorm(100, .4 * data_formula$x_cont1, 1), + N = 100 + ) + + # Simple linear regression + formula_list_simple <- list(mu = ~ x_cont1) + formula_data_list_simple <- list(mu = data_formula) + formula_prior_list_simple <- list( + mu = list( + "intercept" = prior("normal", list(0, 5)), + "x_cont1" = prior("normal", list(0, 1)) + ) + ) + prior_list_simple <- list(sigma = prior("lognormal", list(0, 1))) + + model_syntax_simple <- paste0( + "model{\n", + "for(i in 1:N){\n", + " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", + "}\n", + "}" + ) + + fit_formula_simple <- JAGS_fit( + model_syntax = model_syntax_simple, data = data, prior_list = prior_list_simple, + formula_list = formula_list_simple, formula_data_list = formula_data_list_simple, + formula_prior_list = formula_prior_list_simple, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 1) + + # Compute marginal likelihood for model averaging + log_posterior_formula <- function(parameters, data){ + sum(stats::dnorm(data$y, parameters[["mu"]], parameters[["sigma"]], log = TRUE)) + } + marglik_formula_simple <- JAGS_bridgesampling( + fit_formula_simple, log_posterior = log_posterior_formula, data = data, + prior_list = prior_list_simple, + formula_list = formula_list_simple, formula_data_list = formula_data_list_simple, + formula_prior_list = formula_prior_list_simple) + + result <- save_fit(fit_formula_simple, "fit_formula_simple", + marglik = marglik_formula_simple, + formulas = TRUE, simple_priors = TRUE, + note = "Simple linear regression with continuous predictor") + model_registry[["fit_formula_simple"]] <<- result$registry_entry + fit_formula_simple <- result$fit + + # Regression with treatment factor + formula_list_treatment <- list(mu = ~ x_cont1 + x_fac2t) + formula_data_list_treatment <- list(mu = data_formula) + formula_prior_list_treatment <- list( + mu = list( + "intercept" = prior("normal", list(0, 5)), + "x_cont1" = prior("normal", list(0, 1)), + "x_fac2t" = prior_factor("normal", contrast = "treatment", list(0, 1)) + ) + ) + + fit_formula_treatment <- JAGS_fit( + model_syntax = model_syntax_simple, data = data, prior_list = prior_list_simple, + formula_list = formula_list_treatment, formula_data_list = formula_data_list_treatment, + formula_prior_list = formula_prior_list_treatment, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 2) + + # Compute marginal likelihood for model averaging + marglik_formula_treatment <- JAGS_bridgesampling( + fit_formula_treatment, log_posterior = log_posterior_formula, data = data, + prior_list = prior_list_simple, + formula_list = formula_list_treatment, formula_data_list = formula_data_list_treatment, + formula_prior_list = formula_prior_list_treatment) + + result <- save_fit(fit_formula_treatment, "fit_formula_treatment", + marglik = marglik_formula_treatment, + formulas = TRUE, factor_priors = TRUE, simple_priors = TRUE, + note = "Regression with continuous predictor and 2-level treatment factor") + model_registry[["fit_formula_treatment"]] <<- result$registry_entry + fit_formula_treatment <- result$fit + + # Regression with orthonormal factor + formula_list_orthonormal <- list(mu = ~ x_cont1 + x_fac3o) + formula_data_list_orthonormal <- list(mu = data_formula) + formula_prior_list_orthonormal <- list( + mu = list( + "intercept" = prior("normal", list(0, 5)), + "x_cont1" = prior("normal", list(0, 1)), + "x_fac3o" = prior_factor("mnormal", contrast = "orthonormal", list(0, 1)) + ) + ) + + fit_formula_orthonormal <- JAGS_fit( + model_syntax = model_syntax_simple, data = data, prior_list = prior_list_simple, + formula_list = formula_list_orthonormal, formula_data_list = formula_data_list_orthonormal, + formula_prior_list = formula_prior_list_orthonormal, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 3) + + # Compute marginal likelihood for model averaging + marglik_formula_orthonormal <- JAGS_bridgesampling( + fit_formula_orthonormal, log_posterior = log_posterior_formula, data = data, + prior_list = prior_list_simple, + formula_list = formula_list_orthonormal, formula_data_list = formula_data_list_orthonormal, + formula_prior_list = formula_prior_list_orthonormal) + + result <- save_fit(fit_formula_orthonormal, "fit_formula_orthonormal", + marglik = marglik_formula_orthonormal, + formulas = TRUE, factor_priors = TRUE, simple_priors = TRUE, + note = "Regression with continuous predictor and 3-level orthonormal factor") + model_registry[["fit_formula_orthonormal"]] <<- result$registry_entry + fit_formula_orthonormal <- result$fit +}) + + +# ============================================================================ # +# SECTION 8: FORMULA-BASED MODELS (INTERACTIONS) +# ============================================================================ # +test_that("Formula-based interaction models fit correctly", { + + skip_if_not_installed("rjags") + + set.seed(1) + data_formula <- data.frame( + x_cont1 = rnorm(100), + x_cont2 = rnorm(100), + x_fac2t = factor(rep(c("A", "B"), 50), levels = c("A", "B")), + x_fac3o = factor(rep(c("A", "B", "C"), length.out = 100), levels = c("A", "B", "C")) + ) + data <- list( + y = rnorm(100, .4 * data_formula$x_cont1 - 0.15 * data_formula$x_cont1 * data_formula$x_cont2, 1), + N = 100 + ) + + model_syntax <- paste0( + "model{\n", + "for(i in 1:N){\n", + " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", + "}\n", + "}" + ) + prior_list <- list(sigma = prior("lognormal", list(0, 1))) + + # Continuous interaction + formula_list_cont_int <- list(mu = ~ x_cont1 * x_cont2) + formula_data_list_cont_int <- list(mu = data_formula) + formula_prior_list_cont_int <- list( + mu = list( + "intercept" = prior("normal", list(0, 5)), + "x_cont1" = prior("normal", list(0, 1)), + "x_cont2" = prior("normal", list(0, 1)), + "x_cont1:x_cont2" = prior("normal", list(0, 1)) + ) + ) + + fit_formula_interaction_cont <- JAGS_fit( + model_syntax = model_syntax, data = data, prior_list = prior_list, + formula_list = formula_list_cont_int, formula_data_list = formula_data_list_cont_int, + formula_prior_list = formula_prior_list_cont_int, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 1) + result <- save_fit(fit_formula_interaction_cont, "fit_formula_interaction_cont", + formulas = TRUE, interactions = TRUE, simple_priors = TRUE, + note = "Continuous-continuous interaction") + model_registry[["fit_formula_interaction_cont"]] <<- result$registry_entry + fit_formula_interaction_cont <- result$fit + + # Test standardization: manual vs automatic scaling + # Create data with large scale differences (far from being scaled) + set.seed(2) + data_unscaled <- data.frame( + x_cont1 = rnorm(100, mean = 1000, sd = 1000), # Large scale + x_cont2 = rnorm(100, mean = 0.5, sd = 0.01) # Small scale + ) + data_scale <- list( + y = rnorm(100, 500 * data_unscaled$x_cont1 - 20 * data_unscaled$x_cont1 * data_unscaled$x_cont2, 1), + N = 100 + ) + + # Manual scaling: scale the data manually before fitting + data_manual_scaled <- data_unscaled + x_cont1_mean <- mean(data_unscaled$x_cont1) + x_cont1_sd <- sd(data_unscaled$x_cont1) + x_cont2_mean <- mean(data_unscaled$x_cont2) + x_cont2_sd <- sd(data_unscaled$x_cont2) + data_manual_scaled$x_cont1 <- (data_unscaled$x_cont1 - x_cont1_mean) / x_cont1_sd + data_manual_scaled$x_cont2 <- (data_unscaled$x_cont2 - x_cont2_mean) / x_cont2_sd + + formula_list_scale <- list(mu = ~ x_cont1 * x_cont2) + formula_prior_list_scale <- list( + mu = list( + "intercept" = prior("normal", list(0, 5)), + "x_cont1" = prior("normal", list(0, 1)), + "x_cont2" = prior("normal", list(0, 1)), + "x_cont1:x_cont2" = prior("normal", list(0, 1)) + ) + ) + + # Fit 1: Manual scaling + formula_data_list_manual <- list(mu = data_manual_scaled) + fit_formula_manual_scaled <- JAGS_fit( + model_syntax = model_syntax, data = data_scale, prior_list = prior_list, + formula_list = formula_list_scale, formula_data_list = formula_data_list_manual, + formula_prior_list = formula_prior_list_scale, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 2) + # Store scaling info as attribute for comparison + attr(fit_formula_manual_scaled, "manual_scale") <- list( + mu_x_cont1 = list(mean = x_cont1_mean, sd = x_cont1_sd), + mu_x_cont2 = list(mean = x_cont2_mean, sd = x_cont2_sd) + ) + result <- save_fit(fit_formula_manual_scaled, "fit_formula_manual_scaled", + formulas = TRUE, interactions = TRUE, simple_priors = TRUE, + note = "Manual scaling of continuous predictors") + model_registry[["fit_formula_manual_scaled"]] <<- result$registry_entry + fit_formula_manual_scaled <- result$fit + + # Fit 2: Automatic scaling + formula_data_list_auto <- list(mu = data_unscaled) + formula_scale_list_auto <- list(mu = list(x_cont1 = TRUE, x_cont2 = TRUE)) + fit_formula_auto_scaled <- JAGS_fit( + model_syntax = model_syntax, data = data_scale, prior_list = prior_list, + formula_list = formula_list_scale, formula_data_list = formula_data_list_auto, + formula_prior_list = formula_prior_list_scale, + formula_scale_list = formula_scale_list_auto, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 2) + result <- save_fit(fit_formula_auto_scaled, "fit_formula_auto_scaled", + formulas = TRUE, interactions = TRUE, simple_priors = TRUE, + note = "Automatic scaling of continuous predictors") + model_registry[["fit_formula_auto_scaled"]] <<- result$registry_entry + fit_formula_auto_scaled <- result$fit + + # Continuous-factor interaction + formula_list_mix_int <- list(mu = ~ x_cont1 * x_fac3o) + formula_data_list_mix_int <- list(mu = data_formula) + formula_prior_list_mix_int <- list( + mu = list( + "intercept" = prior("normal", list(0, 5)), + "x_cont1" = prior("normal", list(0, 1)), + "x_fac3o" = prior_factor("mnormal", contrast = "orthonormal", list(0, 1)), + "x_cont1:x_fac3o" = prior_factor("mnormal", contrast = "orthonormal", list(0, 1)) + ) + ) + + fit_formula_interaction_mix <- JAGS_fit( + model_syntax = model_syntax, data = data, prior_list = prior_list, + formula_list = formula_list_mix_int, formula_data_list = formula_data_list_mix_int, + formula_prior_list = formula_prior_list_mix_int, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 2) + result <- save_fit(fit_formula_interaction_mix, "fit_formula_interaction_mix", + formulas = TRUE, interactions = TRUE, factor_priors = TRUE, simple_priors = TRUE, + note = "Continuous-factor interaction with 3-level orthonormal factor") + model_registry[["fit_formula_interaction_mix"]] <<- result$registry_entry + fit_formula_interaction_mix <- result$fit + + # Continuous-factor interaction (Main effects only) + formula_list_mix_main <- list(mu = ~ x_cont1 + x_fac3o) + formula_data_list_mix_main <- list(mu = data_formula) + formula_prior_list_mix_main <- list( + mu = list( + "intercept" = prior("normal", list(0, 5)), + "x_cont1" = prior("normal", list(0, 1)), + "x_fac3o" = prior_factor("mnormal", contrast = "orthonormal", list(0, 1)) + ) + ) + + fit_formula_interaction_mix_main <- JAGS_fit( + model_syntax = model_syntax, data = data, prior_list = prior_list, + formula_list = formula_list_mix_main, formula_data_list = formula_data_list_mix_main, + formula_prior_list = formula_prior_list_mix_main, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 2) + result <- save_fit(fit_formula_interaction_mix_main, "fit_formula_interaction_mix_main", + formulas = TRUE, factor_priors = TRUE, simple_priors = TRUE, + note = "Continuous-factor main effects only (for interaction test)") + model_registry[["fit_formula_interaction_mix_main"]] <<- result$registry_entry + fit_formula_interaction_mix_main <- result$fit + + # Factor-factor interaction + formula_list_fac_int <- list(mu = ~ x_fac2t * x_fac3o) + formula_data_list_fac_int <- list(mu = data_formula) + formula_prior_list_fac_int <- list( + mu = list( + "intercept" = prior("normal", list(0, 5)), + "x_fac2t" = prior_factor("normal", contrast = "treatment", list(0, 1)), + "x_fac3o" = prior_factor("mnormal", contrast = "orthonormal", list(0, 1)), + "x_fac2t:x_fac3o" = prior_factor("mnormal", contrast = "orthonormal", list(0, 1)) + ) + ) + + fit_formula_interaction_fac <- JAGS_fit( + model_syntax = model_syntax, data = data, prior_list = prior_list, + formula_list = formula_list_fac_int, formula_data_list = formula_data_list_fac_int, + formula_prior_list = formula_prior_list_fac_int, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 3) + result <- save_fit(fit_formula_interaction_fac, "fit_formula_interaction_fac", + formulas = TRUE, interactions = TRUE, factor_priors = TRUE, simple_priors = TRUE, + note = "Factor-factor interaction: 2-level treatment x 3-level orthonormal") + model_registry[["fit_formula_interaction_fac"]] <<- result$registry_entry + fit_formula_interaction_fac <- result$fit + + # Regression with prior_mixture for factor predictor + # Testing mixture of spike and normal factor priors + set.seed(1) + data_formula_mix <- data.frame( + x_cont = rnorm(100), + x_fac3t = factor(rep(c("A", "B", "C"), length.out = 100), levels = c("A", "B", "C")) + ) + data_mix <- list( + y = rnorm(100, 0.20 * data_formula_mix$x_cont, 1), + N = 100 + ) + + formula_list_factor_mix <- list(mu = ~ x_cont + x_fac3t) + formula_data_list_factor_mix <- list(mu = data_formula_mix) + formula_prior_list_factor_mix <- list( + mu = list( + "intercept" = prior("normal", list(0, 5)), + "x_cont" = prior("normal", list(0, 1)), + "x_fac3t" = prior_mixture(list( + prior("spike", list(0)), + prior_factor("normal", list(0, 0.3), contrast = "treatment") + ), is_null = c(TRUE, FALSE)) + ) + ) + + fit_formula_factor_mixture <- JAGS_fit( + model_syntax = model_syntax, data = data_mix, prior_list = prior_list, + formula_list = formula_list_factor_mix, formula_data_list = formula_data_list_factor_mix, + formula_prior_list = formula_prior_list_factor_mix, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 4) + result <- save_fit(fit_formula_factor_mixture, "fit_formula_factor_mixture", + formulas = TRUE, mixture_priors = TRUE, factor_priors = TRUE, simple_priors = TRUE, + note = "Regression with mixture prior on 3-level treatment factor (spike vs normal)") + model_registry[["fit_formula_factor_mixture"]] <<- result$registry_entry + fit_formula_factor_mixture <- result$fit +}) + + +# ============================================================================ # +# SECTION 9: FORMULA-BASED MODELS (MULTIPLE FORMULAS) +# ============================================================================ # +test_that("Multi-formula models fit correctly", { + + skip_if_not_installed("rjags") + + set.seed(1) + data_formula <- data.frame( + x_cont1 = rnorm(100), + x_fac2t = factor(rep(c("A", "B"), 50), levels = c("A", "B")) + ) + data_mu <- 0.20 * data_formula$x_cont1 + data_sigma <- 0.50 * exp(ifelse(data_formula$x_fac2t == "A", -0.5, 0.5)) + data <- list( + y = rnorm(100, data_mu, data_sigma), + N = 100 + ) + + # Model with two formulas (mu and sigma) + formula_list_multi <- list( + mu = ~ x_cont1, + sigma_exp = ~ x_fac2t + ) + formula_data_list_multi <- list( + mu = data_formula, + sigma_exp = data_formula + ) + formula_prior_list_multi <- list( + mu = list( + "intercept" = prior("normal", list(0, 5)), + "x_cont1" = prior("normal", list(0, 1)) + ), + sigma_exp = list( + "intercept" = prior("spike", list(0)), + "x_fac2t" = prior_factor("mnormal", list(0, 1), contrast = "meandif") + ) + ) + prior_list_multi <- list( + "sigma" = prior("normal", list(0, 5), list(0, Inf)) + ) + + model_syntax_multi <- paste0( + "model{\n", + "for(i in 1:N){\n", + " y[i] ~ dnorm(mu[i], 1/pow(sigma * exp(sigma_exp[i]), 2))\n", + "}\n", + "}" + ) + + fit_formula_multi <- JAGS_fit( + model_syntax = model_syntax_multi, data = data, prior_list = prior_list_multi, + formula_list = formula_list_multi, formula_data_list = formula_data_list_multi, + formula_prior_list = formula_prior_list_multi, + chains = 2, adapt = 500, burnin = 500, sample = 500, seed = 1) + result <- save_fit(fit_formula_multi, "fit_formula_multi", + formulas = TRUE, multi_formula = TRUE, factor_priors = TRUE, simple_priors = TRUE, + note = "Two formulas: mu (continuous) and sigma_exp (meandif factor)") + model_registry[["fit_formula_multi"]] <<- result$registry_entry + fit_formula_multi <- result$fit +}) + + +# ============================================================================ # +# SECTION 10: RANDOM EFFECTS MODELS +# ============================================================================ # +test_that("Random effects models fit correctly", { + + skip_if_not_installed("rjags") + + set.seed(1) + data_formula <- data.frame( + x_cont1 = rnorm(100), + x_fac3 = as.factor(sample(LETTERS[1:3], 100, replace = TRUE)), + id = factor(rep(LETTERS[1:10], 10)) + ) + id_values <- rnorm(10, 0, 0.5) + names(id_values) <- LETTERS[1:10] + + data <- list( + y = rnorm(100, 0.4 * data_formula$x_cont1 + id_values[data_formula$id]), + N = 100 + ) + + model_syntax <- paste0( + "model{\n", + "for(i in 1:N){\n", + " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", + "}\n", + "}" + ) + prior_list <- list(sigma = prior("lognormal", list(0, 1))) + + # Random intercept only + # Note: Using || for uncorrelated random effects (as opposed to | for correlated) + formula_list_re_int <- list(mu = ~ 1 + (1 ||id)) + formula_data_list_re_int <- list(mu = data_formula) + formula_prior_list_re_int <- list( + mu = list( + "intercept" = prior("normal", list(0, 5)), + "intercept|id" = prior("normal", list(0, 1), list(0, 1)) + ) + ) + + fit_random_intercept <- JAGS_fit( + model_syntax = model_syntax, data = data, prior_list = prior_list, + formula_list = formula_list_re_int, formula_data_list = formula_data_list_re_int, + formula_prior_list = formula_prior_list_re_int, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 1) + result <- save_fit(fit_random_intercept, "fit_random_intercept", + formulas = TRUE, random_effects = TRUE, simple_priors = TRUE, + note = "Random intercept model (uncorrelated random effects)") + model_registry[["fit_random_intercept"]] <<- result$registry_entry + fit_random_intercept <- result$fit + + # Random slope (no intercept) + formula_list_re_slope <- list(mu = ~ 1 + (0 + x_cont1 ||id)) + formula_data_list_re_slope <- list(mu = data_formula) + formula_prior_list_re_slope <- list( + mu = list( + "intercept" = prior("normal", list(0, 5)), + "x_cont1|id" = prior("normal", list(0, 1), list(0, 1)) + ) + ) + + fit_random_slope <- JAGS_fit( + model_syntax = model_syntax, data = data, prior_list = prior_list, + formula_list = formula_list_re_slope, formula_data_list = formula_data_list_re_slope, + formula_prior_list = formula_prior_list_re_slope, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 2) + result <- save_fit(fit_random_slope, "fit_random_slope", + formulas = TRUE, random_effects = TRUE, simple_priors = TRUE, + note = "Random slope for continuous predictor (no random intercept)") + model_registry[["fit_random_slope"]] <<- result$registry_entry + fit_random_slope <- result$fit + + # Random factor slope + formula_list_re_fac <- list(mu = ~ 1 + x_cont1 + (x_fac3 ||id)) + formula_data_list_re_fac <- list(mu = data_formula) + formula_prior_list_re_fac <- list( + mu = list( + "intercept" = prior("normal", list(0, 5)), + "x_cont1" = prior("normal", list(0, 1)), + "intercept|id" = prior("normal", list(0, 1), list(0, 1)), + "x_fac3|id" = prior("normal", list(0, 1), list(0, 1)) + ) + ) + + fit_random_factor_slope <- JAGS_fit( + model_syntax = model_syntax, data = data, prior_list = prior_list, + formula_list = formula_list_re_fac, formula_data_list = formula_data_list_re_fac, + formula_prior_list = formula_prior_list_re_fac, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 3) + result <- save_fit(fit_random_factor_slope, "fit_random_factor_slope", + formulas = TRUE, random_effects = TRUE, factor_priors = TRUE, simple_priors = TRUE, + note = "Random factor slopes with random intercept") + model_registry[["fit_random_factor_slope"]] <<- result$registry_entry + fit_random_factor_slope <- result$fit + + # Random factor slope with orthonormal contrast + formula_list_re_fac <- list(mu = ~ 1 + x_fac3 + (x_fac3 ||id)) + formula_data_list_re_fac <- list(mu = data_formula) + formula_prior_list_re_fac <- list( + mu = list( + "intercept" = prior("normal", list(0, 5)), + "x_fac3" = prior_factor("mnormal", list(0, 1)), + "intercept|id" = prior("normal", list(0, 1), list(0, 1)), + "x_fac3|id" = prior("normal", list(0, 1), list(0, 1)) + ) + ) + + fit_random_factor_slope2 <- JAGS_fit( + model_syntax = model_syntax, data = data, prior_list = prior_list, + formula_list = formula_list_re_fac, formula_data_list = formula_data_list_re_fac, + formula_prior_list = formula_prior_list_re_fac, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 3) + result <- save_fit(fit_random_factor_slope2, "fit_random_factor_slope2", + formulas = TRUE, random_effects = TRUE, factor_priors = TRUE, simple_priors = TRUE, + note = "Random factor slopes with random intercept") + model_registry[["fit_random_factor_slope2"]] <<- result$registry_entry + fit_random_factor_slope2 <- result$fit + + + # Random factor slope independent spike and slab contrast + formula_list_re_fac <- list(mu = ~ -1 + x_fac3 + (x_fac3 - 1 ||id)) + formula_data_list_re_fac <- list(mu = data_formula) + formula_prior_list_re_fac <- list( + mu = list( + "x_fac3" = prior_factor("normal", list(0, 1), contrast = "independent"), + "x_fac3|id" = prior_spike_and_slab(prior("normal", list(0, 1), list(0, 1))) + ) + ) + + fit_random_factor_slope3 <- JAGS_fit( + model_syntax = model_syntax, data = data, prior_list = prior_list, + formula_list = formula_list_re_fac, formula_data_list = formula_data_list_re_fac, + formula_prior_list = formula_prior_list_re_fac, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 3) + result <- save_fit(fit_random_factor_slope3, "fit_random_factor_slope3", + formulas = TRUE, random_effects = TRUE, factor_priors = TRUE, simple_priors = TRUE, + note = "Random factor slopes with random intercept") + model_registry[["fit_random_factor_slope3"]] <<- result$registry_entry + fit_random_factor_slope3 <- result$fit +}) + + +# ============================================================================ # +# SECTION 11: SPIKE FACTOR PRIORS +# ============================================================================ # +test_that("Spike factor prior models fit correctly", { + + skip_if_not_installed("rjags") + + set.seed(1) + data_formula <- data.frame( + x_fac2i = factor(rep(c("A", "B"), 50), levels = c("A", "B")), + x_fac3o = factor(rep(c("A", "B", "C"), length.out = 100), levels = c("A", "B", "C")), + x_fac3t = factor(rep(c("A", "B", "C"), length.out = 100), levels = c("A", "B", "C")), + x_fac3md = factor(rep(c("A", "B", "C"), length.out = 100), levels = c("A", "B", "C")) + ) + data <- list(y = rnorm(100, 0, 1), N = 100) + + model_syntax <- paste0( + "model{\n", + "for(i in 1:N){\n", + " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", + "}\n", + "}" + ) + prior_list <- list(sigma = prior("lognormal", list(0, 1))) + + # Spike priors with different contrasts + # Note: Using - 1 to remove the intercept since spike priors for independent factors + # define all levels explicitly, and we're testing different contrast behaviors + formula_list_spike <- list(mu = ~ x_fac2i + x_fac3o + x_fac3t + x_fac3md - 1) + formula_data_list_spike <- list(mu = data_formula) + formula_prior_list_spike <- list( + mu = list( + "x_fac2i" = prior_factor("spike", contrast = "independent", list(1)), + "x_fac3o" = prior_factor("spike", contrast = "orthonormal", list(0)), + "x_fac3t" = prior_factor("spike", contrast = "treatment", list(2)), + "x_fac3md" = prior_factor("spike", contrast = "meandif", list(0)) + ) + ) + + fit_spike_factors <- JAGS_fit( + model_syntax = model_syntax, data = data, prior_list = prior_list, + formula_list = formula_list_spike, formula_data_list = formula_data_list_spike, + formula_prior_list = formula_prior_list_spike, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 1) + result <- save_fit(fit_spike_factors, "fit_spike_factors", + formulas = TRUE, factor_priors = TRUE, + note = "Spike priors with all 4 contrast types: independent, orthonormal, treatment, meandif") + model_registry[["fit_spike_factors"]] <<- result$registry_entry + fit_spike_factors <- result$fit +}) + + +# ============================================================================ # +# SECTION 12: JOINT MODELS (FORMULA + SPIKE-AND-SLAB + MIXTURE) +# ============================================================================ # +test_that("Joint complex models fit correctly", { + + skip_if_not_installed("rjags") + + set.seed(1) + data_formula <- data.frame( + x_cont1 = rnorm(100), + x_fac2t = factor(rep(c("A", "B"), 50), levels = c("A", "B")), + x_fac3t = factor(rep(c("A", "B", "C"), length.out = 100), levels = c("A", "B", "C")) + ) + data <- list( + y = rnorm(100, 0.20 * data_formula$x_cont1, 1), + N = 100 + ) + + # Model with mixture intercept, spike-and-slab continuous, spike-and-slab factor + formula_list_joint <- list(mu = ~ x_cont1 + x_fac3t) + formula_data_list_joint <- list(mu = data_formula) + formula_prior_list_joint <- list( + mu = list( + "intercept" = prior_mixture( + list( + prior("spike", list(0), prior_weights = 2), + prior("normal", list(-1, 0.5), prior_weights = 1), + prior("normal", list( 1, 0.5), prior_weights = 1) + ), + is_null = c(T, F, F) + ), + "x_cont1" = prior_mixture( + list( + prior("spike", list(0), prior_weights = 1), + prior("normal", list(0, 1), prior_weights = 1) + ), + is_null = c(T, F) + ), + "x_fac3t" = prior_spike_and_slab( + prior_factor("mnormal", list(0, 1), contrast = "orthonormal"), + prior_inclusion = prior("spike", list(0.5)) + ) + ) + ) + # Scale the continuous predictor by sigma (standard practice for hierarchical centering) + attr(formula_prior_list_joint$mu$x_cont1, "multiply_by") <- "sigma" + + prior_list_joint <- list( + "sigma" = prior_mixture( + list( + prior("normal", list(0, 1), truncation = list(0, Inf)), + prior("lognormal", list(0, 1)) + ), + is_null = c(T, F) + ) + ) + + model_syntax_joint <- paste0( + "model{\n", + "for(i in 1:N){\n", + " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", + "}\n", + "}" + ) + + fit_joint_complex <- JAGS_fit( + model_syntax = model_syntax_joint, data = data, prior_list = prior_list_joint, + formula_list = formula_list_joint, formula_data_list = formula_data_list_joint, + formula_prior_list = formula_prior_list_joint, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 1) + result <- save_fit(fit_joint_complex, "fit_joint_complex", + formulas = TRUE, mixture_priors = TRUE, spike_and_slab_priors = TRUE, + factor_priors = TRUE, simple_priors = TRUE, + note = "Complex model: mixture intercept, mixture sigma, spike-and-slab continuous, spike-and-slab factor") + model_registry[["fit_joint_complex"]] <<- result$registry_entry + fit_joint_complex <- result$fit +}) + + +# ============================================================================ # +# SECTION 13: EXPRESSION PRIORS +# ============================================================================ # +test_that("Expression prior models fit correctly", { + + skip_if_not_installed("rjags") + + # Simple prior with expression + priors_expr_simple <- list( + x = prior("normal", list(0, expression(x_sigma))), + x_sigma = prior("invgamma", list(1/2, 1/2)) + ) + + model_syntax_expr1 <- "model{}" + + fit_expression_simple <- suppressWarnings(JAGS_fit(model_syntax_expr1, data = NULL, prior_list = priors_expr_simple, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 1)) + result <- save_fit(fit_expression_simple, "fit_expression_simple", + expression_priors = TRUE, simple_priors = TRUE, + note = "Normal prior with expression referencing another parameter (x_sigma)") + model_registry[["fit_expression_simple"]] <<- result$registry_entry + fit_expression_simple <- result$fit + + # Spike-and-slab with expression + priors_expr_ss <- list( + x = prior_spike_and_slab( + prior("normal", list(0, expression(x_sigma))) + ), + x_sigma = prior("invgamma", list(1/2, 1/2)) + ) + + model_syntax_expr2 <- "model{}" + + fit_expression_spike_slab <- suppressWarnings(JAGS_fit(model_syntax_expr2, data = NULL, prior_list = priors_expr_ss, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 2)) + result <- save_fit(fit_expression_spike_slab, "fit_expression_spike_slab", + expression_priors = TRUE, spike_and_slab_priors = TRUE, simple_priors = TRUE, + note = "Spike-and-slab with expression in alternative prior") + model_registry[["fit_expression_spike_slab"]] <<- result$registry_entry + fit_expression_spike_slab <- result$fit + + # Mixture with expression + priors_expr_mix <- list( + x = prior_mixture(list( + prior("normal", list(0, expression(x_sigma))), + prior("cauchy", list(0, 1)) + ), is_null = c(T, F)), + x_sigma = prior("invgamma", list(1/2, 1/2)) + ) + + model_syntax_expr3 <- "model{}" + + fit_expression_mixture <- suppressWarnings(JAGS_fit(model_syntax_expr3, data = NULL, prior_list = priors_expr_mix, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 3)) + result <- save_fit(fit_expression_mixture, "fit_expression_mixture", + expression_priors = TRUE, mixture_priors = TRUE, simple_priors = TRUE, + note = "Mixture prior with expression in one component") + model_registry[["fit_expression_mixture"]] <<- result$registry_entry + fit_expression_mixture <- result$fit +}) + + +# ============================================================================ # +# SECTION 14: ADVANCED JAGS_FIT FEATURES +# ============================================================================ # +test_that("Advanced JAGS_fit features work correctly", { + + skip_if_not_installed("rjags") + + set.seed(1) + data <- list( + x = rnorm(20, 0, 1), + N = 20 + ) + priors_list <- list( + m = prior("normal", list(0, 1)), + s = prior("normal", list(0, 1), list(0, Inf)) + ) + model_syntax <- + "model + { + for(i in 1:N){ + x[i] ~ dnorm(m, pow(s, -2)) + } + }" + + # Test 1: add_parameters - monitoring additional parameters not in prior_list + model_syntax_add_param <- + "model + { + g ~ dnorm(0, 1) + for(i in 1:N){ + x[i] ~ dnorm(m, pow(s, -2)) + } + }" + + log_posterior <- function(parameters, data){ + return(stats::dnorm(parameters[["g"]], log = TRUE)) + #return(sum(stats::dnorm(data$x, mean = parameters[["m"]], sd = parameters[["s"]], log = TRUE))) + } + add_l <- c("g" = -Inf) + add_u <- c("g" = Inf) + + fit_add_parameters <- JAGS_fit(model_syntax_add_param, data, priors_list, + add_parameters = "g", + chains = 2, adapt = 100, burnin = 100, sample = 300, seed = 1) + marglik_fit_add_parameters <- JAGS_bridgesampling( + fit = fit_add_parameters, + log_posterior = log_posterior, + data = data, + prior_list = priors_list, + add_parameters = "g", + add_bounds = list("lb" = add_l, "ub" = add_u) + ) + + result <- save_fit(fit_add_parameters, "fit_add_parameters", + simple_priors = TRUE, add_parameters = TRUE, + note = "Model with additional monitored parameter 'g' not in prior_list") + model_registry[["fit_add_parameters"]] <<- result$registry_entry + fit_add_parameters <- result$fit + + # Verify that 'g' is in the output + expect_true("g" %in% colnames(fit_add_parameters$mcmc[[1]])) + expect_equal(ncol(fit_add_parameters$mcmc[[1]]), 3) # m, s, g + + # Test 2: autofit - automatic refitting until convergence + # Using a model that requires more samples to converge + priors_autofit <- list( + m = prior("normal", list(0, 1)) + ) + data_autofit <- list( + x = c(-500), + N = 1 + ) + model_syntax_autofit <- + "model + { + l = 1 + for(i in 1:N){ + x[i] ~ dt(m, pow(.3, -2), 1) + } + }" + + runjags::runjags.options(silent.jags = TRUE, silent.runjags = TRUE) + + # First fit without autofit (should have poor convergence) + fit_no_autofit <- JAGS_fit(model_syntax_autofit, data_autofit, priors_autofit, + autofit = FALSE, + chains = 2, adapt = 100, burnin = 50, sample = 100, seed = 2) + result <- save_fit(fit_no_autofit, "fit_no_autofit", + simple_priors = TRUE, + note = "Model without autofit (poor convergence expected)") + model_registry[["fit_no_autofit"]] <<- result$registry_entry + fit_no_autofit <- result$fit + + summary_no_autofit <- suppressWarnings(summary(fit_no_autofit)) + # Check that convergence is poor + expect_true(summary_no_autofit[1,"MCerr"] > 0.069 || summary_no_autofit[1,"MC%ofSD"] > 8) + + # Now fit with autofit using max_error criterion + fit_autofit_error <- JAGS_fit(model_syntax_autofit, data_autofit, priors_autofit, + autofit = TRUE, + autofit_control = list(max_error = 0.05, sample_extend = 100), + chains = 2, adapt = 100, burnin = 50, sample = 100, seed = 2) + result <- save_fit(fit_autofit_error, "fit_autofit_error", + simple_priors = TRUE, autofit = TRUE, + note = "Autofit with max_error criterion (< 0.05)") + model_registry[["fit_autofit_error"]] <<- result$registry_entry + fit_autofit_error <- result$fit + + summary_autofit_error <- summary(fit_autofit_error) + # Should have better convergence + expect_true(summary_autofit_error[1,"MCerr"] < 0.05) + + # Test autofit with min_ESS criterion + fit_autofit_ess <- JAGS_fit(model_syntax_autofit, data_autofit, priors_autofit, + autofit = TRUE, + autofit_control = list(min_ESS = 200, sample_extend = 100), + chains = 2, adapt = 100, burnin = 50, sample = 100, seed = 3) + result <- save_fit(fit_autofit_ess, "fit_autofit_ess", + simple_priors = TRUE, autofit = TRUE, + note = "Autofit with min_ESS criterion (> 200)") + model_registry[["fit_autofit_ess"]] <<- result$registry_entry + fit_autofit_ess <- result$fit + + summary_autofit_ess <- summary(fit_autofit_ess) + expect_true(summary_autofit_ess[1,"SSeff"] > 200) + + # Test 3: parallel - running chains in parallel + # Note: parallel execution is tested but results should be the same as non-parallel + fit_parallel <- JAGS_fit(model_syntax, data, priors_list, + parallel = TRUE, cores = 2, + chains = 2, adapt = 100, burnin = 100, sample = 300, seed = 4) + result <- save_fit(fit_parallel, "fit_parallel", + simple_priors = TRUE, parallel = TRUE, + note = "Model fitted with parallel chains (cores=2)") + model_registry[["fit_parallel"]] <<- result$registry_entry + fit_parallel <- result$fit + + # Verify the fit worked and has the expected structure + expect_equal(length(fit_parallel$mcmc), 2) # 2 chains + expect_true(all(sapply(fit_parallel$mcmc, function(mcmc) ncol(mcmc) == 2))) # m and s +}) + + +# ============================================================================ # +# SECTION 15: MODELS FOR MARGINAL DISTRIBUTION TESTING +# ============================================================================ # +# These models test marginal_posterior, ensemble_inference, and mix_posteriors +# with complex formulas including interactions and multiply_by scaling. +test_that("Marginal distribution models fit correctly", { + + skip_if_not_installed("rjags") + + skip_if_not_installed("bridgesampling") + + set.seed(1) + data_formula_marg <- data.frame( + x_cont1 = rnorm(180), + x_fac2t = factor(rep(c("A", "B"), 90), levels = c("A", "B")), + x_fac3md = factor(rep(c("A", "B", "C"), 60), levels = c("A", "B", "C")) + ) + data_marg <- list( + y = rnorm(180, 0.1, 0.5) + 0.5 + 0.20 * data_formula_marg$x_cont1 + + ifelse(data_formula_marg$x_fac3md == "A", 0.15, ifelse(data_formula_marg$x_fac3md == "B", -0.15, 0)), + N = 180 + ) + + # Null model: spike priors on factor effects + prior_list_marg_0 <- list( + "intercept" = prior("normal", list(0, 1)), + "x_cont1" = prior("normal", list(0, 1)), + "x_fac2t" = prior_factor("spike", contrast = "treatment", list(0)), + "x_fac3md" = prior_factor("spike", contrast = "meandif", list(0)), + "x_cont1:x_fac3md" = prior_factor("spike", contrast = "meandif", list(0)) + ) + attr(prior_list_marg_0$x_cont1, "multiply_by") <- "sigma" + + # Alternative model: normal priors on factor effects + prior_list_marg_1 <- list( + "intercept" = prior("normal", list(0, 1)), + "x_cont1" = prior("normal", list(0, 1)), + "x_fac2t" = prior_factor("normal", contrast = "treatment", list(0, 1.00)), + "x_fac3md" = prior_factor("mnormal", contrast = "meandif", list(0, 0.25)), + "x_cont1:x_fac3md" = prior_factor("mnormal", contrast = "meandif", list(0, 0.25)) + ) + attr(prior_list_marg_1$x_cont1, "multiply_by") <- "sigma" + + prior_list_marg <- list( + "sigma" = prior("cauchy", list(0, 1), list(0, 5)) + ) + model_syntax_marg <- paste0( + "model{", + "for(i in 1:N){\n", + " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", + "}\n", + "}" + ) + log_posterior_marg <- function(parameters, data){ + return(sum(stats::dnorm(data$y, mean = parameters[["mu"]], sd = parameters[["sigma"]], log = TRUE))) + } + model_formula_marg <- list(mu = ~ x_cont1 + x_fac2t + x_cont1*x_fac3md) + + # Fit null model + fit_marginal_0 <- JAGS_fit( + model_syntax = model_syntax_marg, data = data_marg, + prior_list = prior_list_marg, + formula_list = model_formula_marg, + formula_prior_list = list(mu = prior_list_marg_0), + formula_data_list = list(mu = data_formula_marg), + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 1) + + marglik_marginal_0 <- JAGS_bridgesampling( + fit = fit_marginal_0, + log_posterior = log_posterior_marg, + data = data_marg, + prior_list = prior_list_marg, + formula_list = model_formula_marg, + formula_prior_list = list(mu = prior_list_marg_0), + formula_data_list = list(mu = data_formula_marg)) + + result <- save_fit(fit_marginal_0, "fit_marginal_0", + marglik = marglik_marginal_0, + formulas = TRUE, factor_priors = TRUE, interactions = TRUE, + note = "Marginal dist null model: spike priors on factors with interaction and multiply_by") + model_registry[["fit_marginal_0"]] <<- result$registry_entry + fit_marginal_0 <- result$fit + + # Fit alternative model + fit_marginal_1 <- JAGS_fit( + model_syntax = model_syntax_marg, data = data_marg, + prior_list = prior_list_marg, + formula_list = model_formula_marg, + formula_prior_list = list(mu = prior_list_marg_1), + formula_data_list = list(mu = data_formula_marg), + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 2) + + marglik_marginal_1 <- JAGS_bridgesampling( + fit = fit_marginal_1, + log_posterior = log_posterior_marg, + data = data_marg, + prior_list = prior_list_marg, + formula_list = model_formula_marg, + formula_prior_list = list(mu = prior_list_marg_1), + formula_data_list = list(mu = data_formula_marg)) + + result <- save_fit(fit_marginal_1, "fit_marginal_1", + marglik = marglik_marginal_1, + formulas = TRUE, factor_priors = TRUE, interactions = TRUE, + note = "Marginal dist alt model: normal priors on factors with interaction and multiply_by") + model_registry[["fit_marginal_1"]] <<- result$registry_entry + fit_marginal_1 <- result$fit + + # Spike-and-slab/mixture model for marginal distributions + prior_list_marg_ss <- list( + "intercept" = prior("normal", list(0, 1)), + "x_cont1" = prior_mixture(list( + prior("spike", list(0)), + prior("normal", list(0, 1)) + ), is_null = c(T, F)), + "x_fac2t" = prior_spike_and_slab(prior_factor("normal", contrast = "treatment", list(0, 1.00))), + "x_fac3md" = prior_spike_and_slab(prior_factor("mnormal", contrast = "meandif", list(0, 0.25))), + "x_cont1:x_fac3md" = prior_spike_and_slab(prior_factor("mnormal", contrast = "meandif", list(0, 0.25))) + ) + attr(prior_list_marg_ss$x_cont1, "multiply_by") <- "sigma" + + fit_marginal_ss <- JAGS_fit( + model_syntax = model_syntax_marg, data = data_marg, + prior_list = prior_list_marg, + formula_list = model_formula_marg, + formula_prior_list = list(mu = prior_list_marg_ss), + formula_data_list = list(mu = data_formula_marg), + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 3) + + result <- save_fit(fit_marginal_ss, "fit_marginal_ss", + formulas = TRUE, factor_priors = TRUE, interactions = TRUE, + spike_and_slab_priors = TRUE, mixture_priors = TRUE, + note = "Marginal dist model: spike-and-slab and mixture priors with interaction and multiply_by") + model_registry[["fit_marginal_ss"]] <<- result$registry_entry + fit_marginal_ss <- result$fit +}) + + +# ============================================================================ # +# SECTION: MODELS FOR ENSEMBLE PLOTS TESTING +# ============================================================================ # +test_that("PET-PEESE models fit correctly", { + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + set.seed(1) + data <- NULL + model_syntax <- "model{}" + log_posterior <- function(parameters, data){ return(0) } + + # PET model + priors_pet <- list( + mu = prior("spike", list(0)), + PET = prior_PET("normal", list(0, .2)) + ) + fit_pet <- suppressWarnings(JAGS_fit(model_syntax, data, priors_pet, chains = 1, adapt = 100, burnin = 150, sample = 2000, seed = 0)) + marglik_pet <- JAGS_bridgesampling(fit_pet, log_posterior = log_posterior, data = data, prior_list = priors_pet) + result <- save_fit(fit_pet, "fit_pet", marglik = marglik_pet, pub_bias_priors = TRUE, note = "PET prior only") + model_registry[["fit_pet"]] <<- result$registry_entry + + # PEESE model + priors_peese <- list( + mu = prior("spike", list(0)), + PEESE = prior_PEESE("normal", list(0, .8)) + ) + fit_peese <- suppressWarnings(JAGS_fit(model_syntax, data, priors_peese, chains = 1, adapt = 100, burnin = 150, sample = 2000, seed = 1)) + marglik_peese <- JAGS_bridgesampling(fit_peese, log_posterior = log_posterior, data = data, prior_list = priors_peese) + result <- save_fit(fit_peese, "fit_peese", marglik = marglik_peese, pub_bias_priors = TRUE, note = "PEESE prior only") + model_registry[["fit_peese"]] <<- result$registry_entry + + # Missing model (overwhelming) + priors_missing <- list( + mu = prior("normal", list(.2, .2), prior_weights = 4) + ) + fit_missing <- suppressWarnings(JAGS_fit(model_syntax, data, priors_missing, chains = 1, adapt = 100, burnin = 150, sample = 2000, seed = 1)) + marglik_missing <- JAGS_bridgesampling(fit_missing, log_posterior = log_posterior, data = data, prior_list = priors_missing) + result <- save_fit(fit_missing, "fit_missing", marglik = marglik_missing, simple_priors = TRUE, note = "Overwhelming missing model") + model_registry[["fit_missing"]] <<- result$registry_entry +}) + +test_that("Weightfunction models fit correctly", { + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + set.seed(1) + data <- NULL + model_syntax <- "model{}" + log_posterior <- function(parameters, data){ return(0) } + + # One-sided + priors_wf_onesided <- list( + omega = prior_weightfunction("one.sided", list(c(.025), c(1, 1))) + ) + fit_wf_onesided <- suppressWarnings(JAGS_fit(model_syntax, data, priors_wf_onesided, chains = 1, adapt = 100, burnin = 150, sample = 2000, seed = 0)) + marglik_wf_onesided <- JAGS_bridgesampling(fit_wf_onesided, log_posterior = log_posterior, data = data, prior_list = priors_wf_onesided) + result <- save_fit(fit_wf_onesided, "fit_wf_onesided", marglik = marglik_wf_onesided, weightfunction_priors = TRUE, note = "One-sided weightfunction") + model_registry[["fit_wf_onesided"]] <<- result$registry_entry + + # Two-sided + priors_wf_twosided <- list( + omega = prior_weightfunction("two.sided", list(c(.05), c(1, 1))) + ) + fit_wf_twosided <- suppressWarnings(JAGS_fit(model_syntax, data, priors_wf_twosided, chains = 1, adapt = 100, burnin = 150, sample = 2000, seed = 1)) + marglik_wf_twosided <- JAGS_bridgesampling(fit_wf_twosided, log_posterior = log_posterior, data = data, prior_list = priors_wf_twosided) + result <- save_fit(fit_wf_twosided, "fit_wf_twosided", marglik = marglik_wf_twosided, weightfunction_priors = TRUE, note = "Two-sided weightfunction") + model_registry[["fit_wf_twosided"]] <<- result$registry_entry + + # Missing model for WF (overwhelming) + priors_wf_missing <- list( + mu = prior("normal", list(0, .8), prior_weights = 4) + ) + fit_wf_missing <- suppressWarnings(JAGS_fit(model_syntax, data, priors_wf_missing, chains = 1, adapt = 100, burnin = 150, sample = 2000, seed = 1)) + marglik_wf_missing <- JAGS_bridgesampling(fit_wf_missing, log_posterior = log_posterior, data = data, prior_list = priors_wf_missing) + result <- save_fit(fit_wf_missing, "fit_wf_missing", marglik = marglik_wf_missing, simple_priors = TRUE, note = "Overwhelming missing model for WF") + model_registry[["fit_wf_missing"]] <<- result$registry_entry +}) + +test_that("Orthonormal contrast models fit correctly", { + skip_on_os(c("mac", "linux", "solaris")) + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + set.seed(1) + data_formula <- data.frame( + x_fac3o = factor(rep(c("A", "B", "C"), 40), levels = c("A", "B", "C")) + ) + data <- list( + y = rnorm(120, .4 + ifelse(data_formula$x_fac3o == "A", 0.0, ifelse(data_formula$x_fac3o == "B", -0.5, 0.5)), 1), + N = 120 + ) + + formula_list0 <- list(mu = ~ 1) + formula_list1 <- list(mu = ~ x_fac3o) + + formula_prior_list0 <- list( + mu = list( + "intercept" = prior("normal", list(0, 5)) + ) + ) + formula_prior_list1 <- list( + mu = list( + "intercept" = prior("normal", list(0, 5)), + "x_fac3o" = prior_factor("mnormal", contrast = "orthonormal", list(0, 0.5)) + ) + ) + + prior_list <- list(sigma = prior("lognormal", list(0, 1))) + formula_data_list <- list(mu = data_formula) + + model_syntax <- paste0( + "model{\n", + "for(i in 1:N){\n", + " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", + "}\n", + "}" + ) + + log_posterior <- function(parameters, data){ + sum(stats::dnorm(data$y, parameters[["mu"]], parameters[["sigma"]], log = TRUE)) + } + + fit_orthonormal_0 <- JAGS_fit( + model_syntax = model_syntax, data = data, prior_list = prior_list, + formula_list = formula_list0, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0, seed = 1) + marglik_orthonormal_0 <- JAGS_bridgesampling( + fit_orthonormal_0, log_posterior = log_posterior, data = data, prior_list = prior_list, + formula_list = formula_list0, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0) + result <- save_fit(fit_orthonormal_0, "fit_orthonormal_0", marglik = marglik_orthonormal_0, formulas = TRUE, factor_priors = TRUE, note = "Orthonormal null model") + model_registry[["fit_orthonormal_0"]] <<- result$registry_entry + + fit_orthonormal_1 <- JAGS_fit( + model_syntax = model_syntax, data = data, prior_list = prior_list, + formula_list = formula_list1, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1, seed = 2) + marglik_orthonormal_1 <- JAGS_bridgesampling( + fit_orthonormal_1, log_posterior = log_posterior, data = data, prior_list = prior_list, + formula_list = formula_list1, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1) + result <- save_fit(fit_orthonormal_1, "fit_orthonormal_1", marglik = marglik_orthonormal_1, formulas = TRUE, factor_priors = TRUE, note = "Orthonormal alternative model") + model_registry[["fit_orthonormal_1"]] <<- result$registry_entry +}) + + +# ============================================================================ # +# SECTION 2: COMPLEX MODELS FOR PLOTTING +# ============================================================================ # +test_that("Complex models for plotting fit correctly", { + + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + skip_if_not_installed("RoBMA") + require("RoBMA") + + set.seed(1) + + data_formula <- data.frame( + x_cont1 = rnorm(300), + x_fac2t = factor(rep(c("A", "B"), 150), levels = c("A", "B")), + x_fac3t = factor(rep(c("A", "B", "C"), 100), levels = c("A", "B", "C")) + ) + data <- list( + y = rnorm(300, -0.15 + 0.20 * data_formula$x_cont1 + ifelse(data_formula$x_fac3t == "A", 0.0, ifelse(data_formula$x_fac3t == "B", -0.2, 0.2)), ifelse(data_formula$x_fac2t == "A", 0.5, 1)), + N = 300 + ) + + # create model with mix of a formula and free parameters --- + formula_list1 <- list( + mu = ~ x_cont1 + x_fac2t + x_fac3t + ) + formula_data_list1 <- list( + mu = data_formula + ) + formula_prior_list1 <- list( + mu = list( + "intercept" = prior_mixture( + list( + prior("spike", list(0), prior_weights = 2), + prior("normal", list(-1, 0.5), prior_weights = 1), + prior("normal", list( 1, 0.5), prior_weights = 1) + ), + is_null = c(TRUE, FALSE, FALSE) + ), + "x_cont1" = prior_spike_and_slab(prior("normal", list(0, 1), prior_weights = 1)), + "x_fac2t" = prior_mixture(list( + prior("spike", list(0), prior_weights = 1), + prior_factor("mnormal", list(0, 1), contrast = "orthonormal") + ), + is_null = c(TRUE, FALSE) + ), + "x_fac3t" = prior_mixture(list( + prior("spike", list(0), prior_weights = 1), + prior_factor("mnormal", list(0, 1), contrast = "orthonormal") + ), + is_null = c(TRUE, FALSE) + ) + ) + ) + + attr(formula_prior_list1$mu$x_cont1, "multiply_by") <- "sigma" + prior_list1 <- list( + "sigma" = prior_mixture( + list( + prior("normal", list(0, 1), truncation = list(0, Inf)), + prior("lognormal", list(0, 1)) + ), + components = c("normal", "lognormal") + ), + "bias" = prior_mixture(list( + prior_none(prior_weights = 1), + prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/3), + prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/3), + prior_PET("normal", list(0, 1), prior_weights = 1/3) + ), is_null = c(TRUE, FALSE, FALSE, FALSE)) + ) + model_syntax1 <- paste0( + "model{\n", + "for(i in 1:N){\n", + " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", + "}\n", + "}" + ) + + fit_complex_mixed <- JAGS_fit( + model_syntax = model_syntax1, data = data, prior_list = prior_list1, + formula_list = formula_list1, formula_data_list = formula_data_list1, formula_prior_list = formula_prior_list1, + chains = 1, adapt = 100, burnin = 150, sample = 500, seed = 1) + + result <- save_fit(fit_complex_mixed, "fit_complex_mixed", + formulas = TRUE, mixture_priors = TRUE, spike_and_slab_priors = TRUE, + pub_bias_priors = TRUE, weightfunction_priors = TRUE, + note = "Complex model with formula, mixtures, spike and slab, and publication bias") + model_registry[["fit_complex_mixed"]] <<- result$registry_entry + fit_complex_mixed <- result$fit + + expect_true(file.exists(file.path(temp_fits_dir, "fit_complex_mixed.RDS"))) + + # Simple formula mixed model + formula_list_simple_mixed <- list( + mu = ~ x_cont1 + x_fac2t + x_fac3t + ) + formula_data_list_simple_mixed <- list( + mu = data_formula + ) + formula_prior_list_simple_mixed <- list( + mu = list( + "intercept" = prior("normal", list(-1, 0.5), prior_weights = 1), + "x_cont1" = prior("normal", list(0, 1), prior_weights = 1), + "x_fac2t" = prior_factor("mnormal", list(0, 1), contrast = "orthonormal"), + "x_fac3t" = prior_factor("mnormal", list(0, 1), contrast = "meandif") + ) + ) + + attr(formula_prior_list_simple_mixed$mu$x_cont1, "multiply_by") <- "sigma" + prior_list_simple_mixed <- list( + "sigma" = prior("lognormal", list(0, 1)) + ) + model_syntax_simple_mixed <- paste0( + "model{\n", + "for(i in 1:N){\n", + " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", + "}\n", + "}" + ) + + fit_simple_formula_mixed <- JAGS_fit( + model_syntax = model_syntax_simple_mixed, data = data, prior_list = prior_list_simple_mixed, + formula_list = formula_list_simple_mixed, formula_data_list = formula_data_list_simple_mixed, formula_prior_list = formula_prior_list_simple_mixed, + chains = 1, adapt = 100, burnin = 150, sample = 500, seed = 1) + + result <- save_fit(fit_simple_formula_mixed, "fit_simple_formula_mixed", + formulas = TRUE, factor_priors = TRUE, simple_priors = TRUE, + note = "Simple formula model with continuous, orthonormal factor, and meandif factor") + model_registry[["fit_simple_formula_mixed"]] <<- result$registry_entry + fit_simple_formula_mixed <- result$fit +}) + +# ============================================================================ # +# SECTION 3: COMPLEX BIAS ONLY MODEL FOR PLOTTING +# ============================================================================ # +test_that("Complex models for plotting fit correctly", { + + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + skip_if_not_installed("RoBMA") + require("RoBMA") + + set.seed(1) + + prior_list1 <- list( + "mu" = prior("gamma", list(3, 3)), + "bias" = prior_mixture(list( + prior_none(prior_weights = 1), + prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/3), + prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/3), + prior_PET("normal", list(0, 1), prior_weights = 1/3), + prior_PEESE("normal", list(0, 2), prior_weights = 1/3) + ), is_null = c(TRUE, FALSE, FALSE, FALSE, FALSE)) + ) + model_syntax1 <- "model{}" + + fit_complex_bias <- suppressWarnings(JAGS_fit( + model_syntax = model_syntax1, data = NULL, prior_list = prior_list1, + chains = 1, adapt = 100, burnin = 150, sample = 500, seed = 1)) + + result <- save_fit(fit_complex_bias, "fit_complex_bias", + formulas = FALSE, mixture_priors = TRUE, spike_and_slab_priors = FALSE, + pub_bias_priors = TRUE, weightfunction_priors = TRUE, + note = "Model with complex publication bias mixture prior") + model_registry[["fit_complex_bias"]] <<- result$registry_entry + fit_complex_bias <- result$fit +}) + + +# ============================================================================ # +# SECTION 4: DUAL PARAMETER REGRESSION WITH LOG(INTERCEPT) AND FORMULA_SCALE +# ============================================================================ # +test_that("Dual parameter regression with log(intercept) and formula_scale fits correctly", { + + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + set.seed(1) + + # Generate data with heteroscedastic variance + n <- 1000 + data_formula_dual <- data.frame( + x_mu = rnorm(n, mean = 5, sd = 2), + x_sigma = rnorm(n, mean = 3, sd = 1.5) + ) + + # True parameters + true_mu <- 1 + 0.3 * data_formula_dual$x_mu + true_sigma <- exp(log(0.5) - 0.2 * data_formula_dual$x_sigma) + y <- rnorm(n, mean = true_mu, sd = true_sigma) + + data_dual <- list(y = y, N = n) + + # Formula for mu (standard intercept) + formula_mu <- ~ x_mu + + # Formula for log_sigma with log(intercept) attribute + formula_log_sigma <- ~ x_sigma + attr(formula_log_sigma, "log(intercept)") <- TRUE + + formula_list_dual <- list( + mu = formula_mu, + log_sigma = formula_log_sigma + ) + + formula_data_list_dual <- list( + mu = data_formula_dual, + log_sigma = data_formula_dual + ) + + # Scale both continuous predictors + formula_scale_list_dual <- list( + mu = list(x_mu = TRUE), + log_sigma = list(x_sigma = TRUE) + ) + + formula_prior_list_dual <- list( + mu = list( + "intercept" = prior("normal", list(0, 2)), + "x_mu" = prior("normal", list(0, 1)) + ), + log_sigma = list( + "intercept" = prior("lognormal", list(0, 0.5)), + "x_sigma" = prior("normal", list(0, 0.5)) + ) + ) + + # Model syntax uses exp() on log_sigma to get positive sigma + model_syntax_dual <- paste0( + "model{\n", + "for(i in 1:N){\n", + " y[i] ~ dnorm(mu[i], 1/pow(exp(log_sigma[i]), 2))\n", + "}\n", + "}" + ) + + # Log posterior for marginal likelihood + log_posterior_dual <- function(parameters, data){ + sigma <- exp(parameters[["log_sigma"]]) + sum(stats::dnorm(data$y, parameters[["mu"]], sigma, log = TRUE)) + } + + fit_dual_param_regression <- JAGS_fit( + model_syntax = model_syntax_dual, + data = data_dual, + prior_list = NULL, + formula_list = formula_list_dual, + formula_data_list = formula_data_list_dual, + formula_prior_list = formula_prior_list_dual, + formula_scale_list = formula_scale_list_dual, + chains = 2, adapt = 100, burnin = 150, sample = 500, seed = 1) + + marglik_dual_param_regression <- JAGS_bridgesampling( + fit = fit_dual_param_regression, + log_posterior = log_posterior_dual, + data = data_dual, + prior_list = NULL, + formula_list = formula_list_dual, + formula_data_list = formula_data_list_dual, + formula_prior_list = formula_prior_list_dual, + formula_scale_list = formula_scale_list_dual) + + result <- save_fit(fit_dual_param_regression, "fit_dual_param_regression", + marglik = marglik_dual_param_regression, + formulas = TRUE, simple_priors = TRUE, + note = "Dual parameter regression: mu and log_sigma with log(intercept) and formula_scale") + model_registry[["fit_dual_param_regression"]] <<- result$registry_entry + fit_dual_param_regression <- result$fit + + # Verify the model has the expected structure + expect_true("mu_intercept" %in% colnames(fit_dual_param_regression$mcmc[[1]])) + expect_true("mu_x_mu" %in% colnames(fit_dual_param_regression$mcmc[[1]])) + expect_true("log_sigma_intercept" %in% colnames(fit_dual_param_regression$mcmc[[1]])) + expect_true("log_sigma_x_sigma" %in% colnames(fit_dual_param_regression$mcmc[[1]])) +}) + + +# ============================================================================ # +# SAVE MODEL REGISTRY +# ============================================================================ # +# Convert the model registry list to a data frame for easy inspection and querying +test_that("Model registry is created and saved", { + + skip_on_cran() + + # Combine all registry entries into a single data frame + model_registry_df <- do.call(rbind, model_registry) + rownames(model_registry_df) <- NULL + + # Save the registry alongside the fitted models + registry_file <- file.path(test_files_dir, "model_registry.RDS") + saveRDS(model_registry_df, registry_file) + + # Verify registry was created + expect_true(file.exists(registry_file)) + expect_s3_class(model_registry_df, "data.frame") + expect_true(nrow(model_registry_df) > 0) +}) diff --git a/tests/testthat/test-JAGS-diagnostic-plots.R b/tests/testthat/test-JAGS-diagnostic-plots.R new file mode 100644 index 00000000..a9161f7d --- /dev/null +++ b/tests/testthat/test-JAGS-diagnostic-plots.R @@ -0,0 +1,280 @@ +# ============================================================================ # +# TEST FILE: JAGS Diagnostic Plot Functions +# ============================================================================ # +# +# PURPOSE: +# Visual regression tests for JAGS diagnostic plots (density, trace, +# autocorrelation plots). +# +# DEPENDENCIES: +# - vdiffr: Visual regression testing +# - rjags: JAGS model fitting +# - common-functions.R: temp_fits_dir, skip_if_no_fits +# +# SKIP CONDITIONS: +# - skip_if_no_fits(): Pre-fitted models required +# - skip_if_not_installed("rjags"): JAGS dependency +# - skip_if_not_installed("vdiffr"): Visual regression +# - skip_on_os(): Multivariate sampling differs across OSes +# +# MODELS/FIXTURES: +# - fit_formula_interaction_mix, fit_formula_interaction_fac +# - fit_pet, fit_wf_onesided, fit_factor_independent +# - fit_marginal_1, fit_complex_mixed +# +# TAGS: @evaluation, @visual, @JAGS, @diagnostics +# ============================================================================ # + +# Load common test helpers +source(testthat::test_path("common-functions.R")) + +# File-level skips: JAGS models required +skip_if_no_fits() +skip_if_not_installed("rjags") +skip_if_not_installed("vdiffr") + +test_that("JAGS diagnostics work", { + + skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes + + # Load pre-fitted models + fit_formula_mix <- readRDS(file.path(temp_fits_dir, "fit_formula_interaction_mix.RDS")) + fit_formula_fac <- readRDS(file.path(temp_fits_dir, "fit_formula_interaction_fac.RDS")) + fit_pet <- readRDS(file.path(temp_fits_dir, "fit_pet.RDS")) + fit_wf <- readRDS(file.path(temp_fits_dir, "fit_wf_onesided.RDS")) + fit_independent <- readRDS(file.path(temp_fits_dir, "fit_factor_independent.RDS")) + fit_meandif <- readRDS(file.path(temp_fits_dir, "fit_marginal_1.RDS")) # Has meandif factor priors + + ### density plots + vdiffr::expect_doppelganger("diagnostics-plot-density-1", function() JAGS_diagnostics_density(fit_formula_mix, parameter = "mu_x_cont1", formula_prefix = FALSE)) + vdiffr::expect_doppelganger("diagnostics-plot-density-2", function() JAGS_diagnostics_density(fit_formula_mix, parameter = "mu_x_cont1", col = c("red", "green", "blue", "yellow"), formula_prefix = FALSE, transformations = list(mu_x_cont1 = list(fun = function(x) exp(x))))) + vdiffr::expect_doppelganger("diagnostics-plot-density-3", function() JAGS_diagnostics_density(fit_formula_fac, parameter = "mu_x_fac2t", main = "Treatment", xlab = "Values", formula_prefix = FALSE, ylab = "Smth")) + vdiffr::expect_doppelganger("diagnostics-plot-density-4", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) + par(mfrow = c(1, 2)) + JAGS_diagnostics_density(fit_formula_mix, parameter = "mu_x_fac3o") + }) + vdiffr::expect_doppelganger("diagnostics-plot-density-5", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) + par(mfrow = c(1, 3)) + JAGS_diagnostics_density(fit_formula_mix, parameter = "mu_x_fac3o", formula_prefix = FALSE, transform_factors = TRUE) + }) + vdiffr::expect_doppelganger("diagnostics-plot-density-6", function() JAGS_diagnostics_density(fit_pet, parameter = "PET")) + vdiffr::expect_doppelganger("diagnostics-plot-density-7", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) + par(mfrow = c(1, 2)) + JAGS_diagnostics_density(fit_wf, parameter = "omega") + }) + vdiffr::expect_doppelganger("diagnostics-plot-density-8", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) + par(mfrow = c(1, 2)) + # Using p1 from fit_independent (prior only model) + JAGS_diagnostics_density(fit_independent, parameter = "p1") + }) + + vdiffr::expect_doppelganger("diagnostics-ggplot-density-1", JAGS_diagnostics_density(fit_formula_mix, plot_type = "ggplot", parameter = "mu_x_cont1", col = c("red", "green", "blue", "yellow"), formula_prefix = FALSE, transformations = list(mu_x_cont1 = list(fun = function(x) exp(x))))) + temp_plot <- JAGS_diagnostics_density(fit_formula_mix, plot_type = "ggplot", parameter = "mu_x_fac3o", transform_factors = TRUE) + vdiffr::expect_doppelganger("diagnostics-ggplot-density-2.1",temp_plot[[1]]) + vdiffr::expect_doppelganger("diagnostics-ggplot-density-2.2",temp_plot[[2]]) + vdiffr::expect_doppelganger("diagnostics-ggplot-density-2.3",temp_plot[[3]]) + temp_plot <- JAGS_diagnostics_density(fit_wf, plot_type = "ggplot", parameter = "omega") + vdiffr::expect_doppelganger("diagnostics-ggplot-density-3.1",temp_plot) + + + ### trace plots + vdiffr::expect_doppelganger("diagnostics-plot-trace-1", function() JAGS_diagnostics_trace(fit_formula_mix, parameter = "mu_x_cont1", formula_prefix = FALSE)) + vdiffr::expect_doppelganger("diagnostics-plot-trace-2", function() JAGS_diagnostics_trace(fit_formula_mix, parameter = "mu_x_cont1", col = c("red", "green", "blue", "yellow"), formula_prefix = FALSE, transformations = list(mu_x_cont1 = list(fun = function(x) exp(x))))) + vdiffr::expect_doppelganger("diagnostics-plot-trace-3", function() JAGS_diagnostics_trace(fit_formula_fac, parameter = "mu_x_fac2t", main = "Treatment", xlab = "Values", formula_prefix = FALSE, ylab = "Smth")) + vdiffr::expect_doppelganger("diagnostics-plot-trace-4", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) + par(mfrow = c(1, 2)) + JAGS_diagnostics_trace(fit_formula_mix, parameter = "mu_x_fac3o", formula_prefix = FALSE) + }) + vdiffr::expect_doppelganger("diagnostics-plot-trace-5", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) + par(mfrow = c(1, 3)) + JAGS_diagnostics_trace(fit_formula_mix, parameter = "mu_x_fac3o", transform_factors = TRUE) + }) + vdiffr::expect_doppelganger("diagnostics-plot-trace-6", function() JAGS_diagnostics_trace(fit_pet, parameter = "PET")) + vdiffr::expect_doppelganger("diagnostics-plot-trace-7", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) + par(mfrow = c(1, 2)) + JAGS_diagnostics_trace(fit_wf, parameter = "omega") + }) + vdiffr::expect_doppelganger("diagnostics-plot-trace-8", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) + par(mfrow = c(1, 2)) + JAGS_diagnostics_trace(fit_independent, parameter = "p1") + }) + + vdiffr::expect_doppelganger("diagnostics-ggplot-trace-1", JAGS_diagnostics_trace(fit_formula_mix, plot_type = "ggplot", parameter = "mu_x_cont1", col = c("red", "green", "blue", "yellow"), formula_prefix = FALSE, transformations = list(mu_x_cont1 = list(fun = function(x) exp(x))))) + temp_plot <- JAGS_diagnostics_trace(fit_formula_mix, plot_type = "ggplot", parameter = "mu_x_fac3o", transform_factors = TRUE) + vdiffr::expect_doppelganger("diagnostics-ggplot-trace-2.1",temp_plot[[1]]) + vdiffr::expect_doppelganger("diagnostics-ggplot-trace-2.2",temp_plot[[2]]) + vdiffr::expect_doppelganger("diagnostics-ggplot-trace-2.3",temp_plot[[3]]) + temp_plot <- JAGS_diagnostics_trace(fit_wf, plot_type = "ggplot", parameter = "omega") + vdiffr::expect_doppelganger("diagnostics-ggplot-trace-3.1",temp_plot) + + + ### autocorrelation plots + vdiffr::expect_doppelganger("diagnostics-plot-autocorrelation-1", function() JAGS_diagnostics_autocorrelation(fit_formula_mix, parameter = "mu_x_cont1", formula_prefix = FALSE)) + vdiffr::expect_doppelganger("diagnostics-plot-autocorrelation-2", function() JAGS_diagnostics_autocorrelation(fit_formula_mix, parameter = "mu_x_cont1", col = c("red", "green", "blue", "yellow"), formula_prefix = FALSE, transformations = list(mu_x_cont1 = list(fun = function(x) exp(x))))) + vdiffr::expect_doppelganger("diagnostics-plot-autocorrelation-3", function() JAGS_diagnostics_autocorrelation(fit_formula_fac, parameter = "mu_x_fac2t", main = "Treatment", xlab = "Values", ylab = "Smth")) + vdiffr::expect_doppelganger("diagnostics-plot-autocorrelation-4", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) + par(mfrow = c(1, 2)) + JAGS_diagnostics_autocorrelation(fit_formula_mix, parameter = "mu_x_fac3o") + }) + vdiffr::expect_doppelganger("diagnostics-plot-autocorrelation-5", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) + par(mfrow = c(1, 3)) + JAGS_diagnostics_autocorrelation(fit_formula_mix, parameter = "mu_x_fac3o", formula_prefix = FALSE, transform_factors = TRUE) + }) + vdiffr::expect_doppelganger("diagnostics-plot-autocorrelation-6", function() JAGS_diagnostics_autocorrelation(fit_pet, parameter = "PET")) + vdiffr::expect_doppelganger("diagnostics-plot-autocorrelation-7", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) + par(mfrow = c(1, 2)) + JAGS_diagnostics_autocorrelation(fit_wf, parameter = "omega") + }) + vdiffr::expect_doppelganger("diagnostics-plot-autocorrelation-8", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) + par(mfrow = c(1, 2)) + JAGS_diagnostics_autocorrelation(fit_independent, parameter = "p1") + }) + + vdiffr::expect_doppelganger("diagnostics-ggplot-autocorrelation-1", JAGS_diagnostics_autocorrelation(fit_formula_mix, plot_type = "ggplot", parameter = "mu_x_cont1", col = c("red", "green", "blue", "yellow"), formula_prefix = FALSE, transformations = list(mu_x_cont1 = list(fun = function(x) exp(x))))) + temp_plot <- JAGS_diagnostics_autocorrelation(fit_formula_mix, plot_type = "ggplot", parameter = "mu_x_fac3o", transform_factors = TRUE) + vdiffr::expect_doppelganger("diagnostics-ggplot-autocorrelation-2.1",temp_plot[[1]]) + vdiffr::expect_doppelganger("diagnostics-ggplot-autocorrelation-2.2",temp_plot[[2]]) + vdiffr::expect_doppelganger("diagnostics-ggplot-autocorrelation-2.3",temp_plot[[3]]) + temp_plot <- JAGS_diagnostics_autocorrelation(fit_wf, plot_type = "ggplot", parameter = "omega") + vdiffr::expect_doppelganger("diagnostics-ggplot-autocorrelation-3.1",temp_plot) +}) + +test_that("JAGS diagnostics work (spike and slab)", { + + skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes + + # Use fit_complex_mixed which has spike and slab on x_cont1 + fit <- readRDS(file.path(temp_fits_dir, "fit_complex_mixed.RDS")) + + ### density plots + vdiffr::expect_doppelganger("diagnostics-plot-spike-and-slab-1", function() JAGS_diagnostics_density(fit, parameter = "mu_x_cont1")) + vdiffr::expect_doppelganger("diagnostics-plot-spike-and-slab-2", function() JAGS_diagnostics_autocorrelation(fit, parameter = "mu_x_cont1")) + vdiffr::expect_doppelganger("diagnostics-plot-spike-and-slab-3", function() JAGS_diagnostics_trace(fit, parameter = "mu_x_cont1")) +}) + +test_that("JAGS diagnostics work (mixture priors)", { + + skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes + + # Use fit_complex_mixed which has mixture on intercept and x_fac3t + fit <- readRDS(file.path(temp_fits_dir, "fit_complex_mixed.RDS")) + + ### density plots + # Using mu_intercept as the first mixture example (was mu_x_cont in original, but x_cont1 is spike/slab in this model) + vdiffr::expect_doppelganger("diagnostics-plot-mixture-1", function() JAGS_diagnostics_density(fit, parameter = "mu_intercept")) + vdiffr::expect_doppelganger("diagnostics-plot-mixture-2", function() JAGS_diagnostics_autocorrelation(fit, parameter = "mu_intercept")) + vdiffr::expect_doppelganger("diagnostics-plot-mixture-3", function() JAGS_diagnostics_trace(fit, parameter = "mu_intercept")) + + # Using mu_x_fac3t as the second mixture example + vdiffr::expect_doppelganger("diagnostics-plot-mixture-4", function() JAGS_diagnostics_density(fit, parameter = "mu_x_fac3t")) + vdiffr::expect_doppelganger("diagnostics-plot-mixture-5", function() JAGS_diagnostics_autocorrelation(fit, parameter = "mu_x_fac3t")) + vdiffr::expect_doppelganger("diagnostics-plot-mixture-6", function() JAGS_diagnostics_trace(fit, parameter = "mu_x_fac3t")) +}) + +test_that("JAGS diagnostics work (meandif and independent)", { + + skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes + + fit_independent <- readRDS(file.path(temp_fits_dir, "fit_factor_independent.RDS")) + fit_meandif <- readRDS(file.path(temp_fits_dir, "fit_marginal_1.RDS")) # Has meandif factor priors + + ### density plots + vdiffr::expect_doppelganger("diagnostics3-plot-density-1", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) + par(mfrow = c(1, 2)) + JAGS_diagnostics_density(fit_independent, parameter = "p1") + }) + vdiffr::expect_doppelganger("diagnostics3-plot-density-2", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) + par(mfrow = c(1, 2)) + JAGS_diagnostics_density(fit_meandif, parameter = "mu_x_fac3md") + }) + vdiffr::expect_doppelganger("diagnostics3-plot-density-3", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) + par(mfrow = c(1, 3)) + JAGS_diagnostics_density(fit_meandif, parameter = "mu_x_fac3md", formula_prefix = FALSE, transform_factors = TRUE) + }) + + + temp_plot <- JAGS_diagnostics_density(fit_independent, plot_type = "ggplot", parameter = "p1") + vdiffr::expect_doppelganger("diagnostics3-ggplot-density-1.1",temp_plot[[1]]) + vdiffr::expect_doppelganger("diagnostics3-ggplot-density-1.2",temp_plot[[2]]) + + temp_plot <- JAGS_diagnostics_density(fit_meandif, plot_type = "ggplot", parameter = "mu_x_fac3md", transform_factors = TRUE) + vdiffr::expect_doppelganger("diagnostics3-ggplot-density-2.1",temp_plot[[1]]) + vdiffr::expect_doppelganger("diagnostics3-ggplot-density-2.2",temp_plot[[2]]) + vdiffr::expect_doppelganger("diagnostics3-ggplot-density-2.3",temp_plot[[3]]) + + + ### trace plots + vdiffr::expect_doppelganger("diagnostics3-plot-trace-1", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) + par(mfrow = c(1, 2)) + JAGS_diagnostics_trace(fit_independent, parameter = "p1") + }) + vdiffr::expect_doppelganger("diagnostics3-plot-trace-2", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) + par(mfrow = c(1, 3)) + JAGS_diagnostics_trace(fit_meandif, parameter = "mu_x_fac3md", formula_prefix = FALSE, transform_factors = TRUE) + }) + + + ### autocorrelation plots + vdiffr::expect_doppelganger("diagnostics3-plot-autocorrelation-1", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) + par(mfrow = c(1, 2)) + JAGS_diagnostics_autocorrelation(fit_independent, parameter = "p1") + }) + vdiffr::expect_doppelganger("diagnostics3-plot-autocorrelation-2", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) + par(mfrow = c(1, 3)) + JAGS_diagnostics_autocorrelation(fit_meandif, parameter = "mu_x_fac3md", formula_prefix = FALSE, transform_factors = TRUE) + }) +}) + +test_that("JAGS diagnostics work (spike priors)", { + + skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes + + fit <- readRDS(file.path(temp_fits_dir, "fit_spike_factors.RDS")) + fit_simple <- readRDS(file.path(temp_fits_dir, "fit_spike_slab_simple.RDS")) + + ### density plots + vdiffr::expect_doppelganger("diagnostics4-ggplot-density-fit_simple",JAGS_diagnostics_density(fit_simple, parameter = "mu")) + + # fit_spike_factors has factor spikes + expect_message(JAGS_diagnostics_density(fit, parameter = "mu_x_fac2i"), "No diagnostic plots are produced for a spike prior distribution") + expect_message(JAGS_diagnostics_trace(fit, parameter = "mu_x_fac3md"), "No diagnostic plots are produced for a spike prior distribution") + +}) diff --git a/tests/testthat/test-JAGS-diagnostics.R b/tests/testthat/test-JAGS-diagnostics.R deleted file mode 100644 index d1c2e894..00000000 --- a/tests/testthat/test-JAGS-diagnostics.R +++ /dev/null @@ -1,458 +0,0 @@ -context("JAGS diagnostics") - -test_that("JAGS diagnostics work", { - - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes - skip_on_cran() - - set.seed(1) - - data_formula <- data.frame( - x_cont1 = rnorm(150), - x_fac2t = factor(rep(c("A", "B"), 75), levels = c("A", "B")), - x_fac3o = factor(rep(c("A", "B", "C"), 50), levels = c("A", "B", "C")) - ) - data <- list( - y = rnorm(150, .4 * data_formula$x_cont1 + ifelse(data_formula$x_fac3o == "A", 0.0, ifelse(data_formula$x_fac3o == "B", -0.2, 0.4)), ifelse(data_formula$x_fac2t == "A", 0.5, 1)), - N = 150 - ) - - - # create model with mix of a formula and free parameters --- - formula_list <- list( - mu = ~ x_cont1 + x_fac2t + x_fac3o - ) - formula_data_list <- list( - mu = data_formula - ) - formula_prior_list <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior("normal", list(0, 1)), - "x_fac2t" = prior_factor("normal", contrast = "treatment", list(0, 1)), - "x_fac3o" = prior_factor("mnormal", contrast = "orthonormal", list(0, 1)) - ) - ) - prior_list <- list( - sigma = prior("lognormal", list(0, 1)), - omega = prior_weightfunction("onesided", list(c(0.05, 0.10), c(1,1,1))), - PET = prior_PET("gamma", list(2, 2)), - fac2i = prior_factor("normal", contrast = "independent", list(0, 1/2)) - ) - attr(prior_list$fac2i, "levels") <- 2 - model_syntax <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) - - fit <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list) - - - ### density plots - vdiffr::expect_doppelganger("diagnostics-plot-density-1", function() JAGS_diagnostics_density(fit, parameter = "mu_x_cont1", formula_prefix = FALSE)) - vdiffr::expect_doppelganger("diagnostics-plot-density-2", function() JAGS_diagnostics_density(fit, parameter = "mu_x_cont1", col = c("red", "green", "blue", "yellow"), formula_prefix = FALSE, transformations = list(mu_x_cont1 = list(fun = function(x) exp(x))))) - vdiffr::expect_doppelganger("diagnostics-plot-density-3", function() JAGS_diagnostics_density(fit, parameter = "mu_x_fac2t", main = "Treatment", xlab = "Values", formula_prefix = FALSE, ylab = "Smth")) - vdiffr::expect_doppelganger("diagnostics-plot-density-4", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(1, 2)) - JAGS_diagnostics_density(fit, parameter = "mu_x_fac3o") - }) - vdiffr::expect_doppelganger("diagnostics-plot-density-5", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(1, 3)) - JAGS_diagnostics_density(fit, parameter = "mu_x_fac3o", formula_prefix = FALSE, transform_factors = TRUE) - }) - vdiffr::expect_doppelganger("diagnostics-plot-density-6", function()JAGS_diagnostics_density(fit, parameter = "PET")) - vdiffr::expect_doppelganger("diagnostics-plot-density-7", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(1, 2)) - JAGS_diagnostics_density(fit, parameter = "omega") - }) - vdiffr::expect_doppelganger("diagnostics-plot-density-8", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(1, 2)) - JAGS_diagnostics_density(fit, parameter = "fac2i") - }) - - vdiffr::expect_doppelganger("diagnostics-ggplot-density-1", JAGS_diagnostics_density(fit, plot_type = "ggplot", parameter = "mu_x_cont1", col = c("red", "green", "blue", "yellow"), formula_prefix = FALSE, transformations = list(mu_x_cont1 = list(fun = function(x) exp(x))))) - temp_plot <- JAGS_diagnostics_density(fit, plot_type = "ggplot", parameter = "mu_x_fac3o", transform_factors = TRUE) - vdiffr::expect_doppelganger("diagnostics-ggplot-density-2.1",temp_plot[[1]]) - vdiffr::expect_doppelganger("diagnostics-ggplot-density-2.2",temp_plot[[2]]) - vdiffr::expect_doppelganger("diagnostics-ggplot-density-2.3",temp_plot[[3]]) - temp_plot <- JAGS_diagnostics_density(fit, plot_type = "ggplot", parameter = "omega") - vdiffr::expect_doppelganger("diagnostics-ggplot-density-3.1",temp_plot[[1]]) - vdiffr::expect_doppelganger("diagnostics-ggplot-density-3.2",temp_plot[[2]]) - - - ### trace plots - vdiffr::expect_doppelganger("diagnostics-plot-trace-1", function() JAGS_diagnostics_trace(fit, parameter = "mu_x_cont1", formula_prefix = FALSE)) - vdiffr::expect_doppelganger("diagnostics-plot-trace-2", function() JAGS_diagnostics_trace(fit, parameter = "mu_x_cont1", col = c("red", "green", "blue", "yellow"), formula_prefix = FALSE, transformations = list(mu_x_cont1 = list(fun = function(x) exp(x))))) - vdiffr::expect_doppelganger("diagnostics-plot-trace-3", function() JAGS_diagnostics_trace(fit, parameter = "mu_x_fac2t", main = "Treatment", xlab = "Values", formula_prefix = FALSE, ylab = "Smth")) - vdiffr::expect_doppelganger("diagnostics-plot-trace-4", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(1, 2)) - JAGS_diagnostics_trace(fit, parameter = "mu_x_fac3o", formula_prefix = FALSE) - }) - vdiffr::expect_doppelganger("diagnostics-plot-trace-5", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(1, 3)) - JAGS_diagnostics_trace(fit, parameter = "mu_x_fac3o", transform_factors = TRUE) - }) - vdiffr::expect_doppelganger("diagnostics-plot-trace-6", function() JAGS_diagnostics_trace(fit, parameter = "PET")) - vdiffr::expect_doppelganger("diagnostics-plot-trace-7", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(1, 2)) - JAGS_diagnostics_trace(fit, parameter = "omega") - }) - vdiffr::expect_doppelganger("diagnostics-plot-trace-8", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(1, 2)) - JAGS_diagnostics_trace(fit, parameter = "fac2i") - }) - - vdiffr::expect_doppelganger("diagnostics-ggplot-trace-1", JAGS_diagnostics_trace(fit, plot_type = "ggplot", parameter = "mu_x_cont1", col = c("red", "green", "blue", "yellow"), formula_prefix = FALSE, transformations = list(mu_x_cont1 = list(fun = function(x) exp(x))))) - temp_plot <- JAGS_diagnostics_trace(fit, plot_type = "ggplot", parameter = "mu_x_fac3o", transform_factors = TRUE) - vdiffr::expect_doppelganger("diagnostics-ggplot-trace-2.1",temp_plot[[1]]) - vdiffr::expect_doppelganger("diagnostics-ggplot-trace-2.2",temp_plot[[2]]) - vdiffr::expect_doppelganger("diagnostics-ggplot-trace-2.3",temp_plot[[3]]) - temp_plot <- JAGS_diagnostics_trace(fit, plot_type = "ggplot", parameter = "omega") - vdiffr::expect_doppelganger("diagnostics-ggplot-trace-3.1",temp_plot[[1]]) - vdiffr::expect_doppelganger("diagnostics-ggplot-trace-3.2",temp_plot[[2]]) - - - ### autocorrelation plots - vdiffr::expect_doppelganger("diagnostics-plot-autocorrelation-1", function() JAGS_diagnostics_autocorrelation(fit, parameter = "mu_x_cont1", formula_prefix = FALSE)) - vdiffr::expect_doppelganger("diagnostics-plot-autocorrelation-2", function() JAGS_diagnostics_autocorrelation(fit, parameter = "mu_x_cont1", col = c("red", "green", "blue", "yellow"), formula_prefix = FALSE, transformations = list(mu_x_cont1 = list(fun = function(x) exp(x))))) - vdiffr::expect_doppelganger("diagnostics-plot-autocorrelation-3", function() JAGS_diagnostics_autocorrelation(fit, parameter = "mu_x_fac2t", main = "Treatment", xlab = "Values", ylab = "Smth")) - vdiffr::expect_doppelganger("diagnostics-plot-autocorrelation-4", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(1, 2)) - JAGS_diagnostics_autocorrelation(fit, parameter = "mu_x_fac3o") - }) - vdiffr::expect_doppelganger("diagnostics-plot-autocorrelation-5", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(1, 3)) - JAGS_diagnostics_autocorrelation(fit, parameter = "mu_x_fac3o", formula_prefix = FALSE, transform_factors = TRUE) - }) - vdiffr::expect_doppelganger("diagnostics-plot-autocorrelation-6", function() JAGS_diagnostics_autocorrelation(fit, parameter = "PET")) - vdiffr::expect_doppelganger("diagnostics-plot-autocorrelation-7", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(1, 2)) - JAGS_diagnostics_autocorrelation(fit, parameter = "omega") - }) - vdiffr::expect_doppelganger("diagnostics-plot-autocorrelation-8", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(1, 2)) - JAGS_diagnostics_autocorrelation(fit, parameter = "fac2i") - }) - - vdiffr::expect_doppelganger("diagnostics-ggplot-autocorrelation-1", JAGS_diagnostics_autocorrelation(fit, plot_type = "ggplot", parameter = "mu_x_cont1", col = c("red", "green", "blue", "yellow"), formula_prefix = FALSE, transformations = list(mu_x_cont1 = list(fun = function(x) exp(x))))) - temp_plot <- JAGS_diagnostics_autocorrelation(fit, plot_type = "ggplot", parameter = "mu_x_fac3o", transform_factors = TRUE) - vdiffr::expect_doppelganger("diagnostics-ggplot-autocorrelation-2.1",temp_plot[[1]]) - vdiffr::expect_doppelganger("diagnostics-ggplot-autocorrelation-2.2",temp_plot[[2]]) - vdiffr::expect_doppelganger("diagnostics-ggplot-autocorrelation-2.3",temp_plot[[3]]) - temp_plot <- JAGS_diagnostics_autocorrelation(fit, plot_type = "ggplot", parameter = "omega") - vdiffr::expect_doppelganger("diagnostics-ggplot-autocorrelation-3.1",temp_plot[[1]]) - vdiffr::expect_doppelganger("diagnostics-ggplot-autocorrelation-3.2",temp_plot[[2]]) -}) - -test_that("JAGS diagnostics work (spike and slab)", { - - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes - skip_on_cran() - - set.seed(1) - - data_formula <- data.frame( - x_cont = rnorm(300), - x_fac2t = factor(rep(c("A", "B"), 150), levels = c("A", "B")), - x_fac3t = factor(rep(c("A", "B", "C"), 100), levels = c("A", "B", "C")) - ) - data <- list( - y = rnorm(300, .20 * data_formula$x_cont + ifelse(data_formula$x_fac3t == "A", 0.0, ifelse(data_formula$x_fac3t == "B", -0.2, 0.4)), ifelse(data_formula$x_fac2t == "A", 0.5, 1)), - N = 300 - ) - - - # create model with mix of a formula and free parameters --- - formula_list <- list( - mu = ~ x_cont - ) - formula_data_list <- list( - mu = data_formula - ) - formula_prior_list <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont" = prior_spike_and_slab(prior("normal", list(0, 1)), prior_inclusion = prior("beta", list(1,1))) - ) - ) - prior_list <- list( - sigma = prior("lognormal", list(0, 1)) - ) - model_syntax <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) - - fit <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list) - - ### density plots - vdiffr::expect_doppelganger("diagnostics-plot-spike-and-slab-1", function() JAGS_diagnostics_density(fit, parameter = "mu_x_cont")) - vdiffr::expect_doppelganger("diagnostics-plot-spike-and-slab-2", function() JAGS_diagnostics_autocorrelation(fit, parameter = "mu_x_cont")) - vdiffr::expect_doppelganger("diagnostics-plot-spike-and-slab-3", function() JAGS_diagnostics_trace(fit, parameter = "mu_x_cont")) -}) - -test_that("JAGS diagnostics work (mixture priors)", { - - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes - skip_on_cran() - - set.seed(1) - - data_formula <- data.frame( - x_cont = rnorm(300), - x_fac2t = factor(rep(c("A", "B"), 150), levels = c("A", "B")), - x_fac3t = factor(rep(c("A", "B", "C"), 100), levels = c("A", "B", "C")) - ) - data <- list( - y = rnorm(300, .20 * data_formula$x_cont + ifelse(data_formula$x_fac3t == "A", 0.0, ifelse(data_formula$x_fac3t == "B", -0.2, 0.4)), ifelse(data_formula$x_fac2t == "A", 0.5, 1)), - N = 300 - ) - - - # create model with mix of a formula and free parameters --- - formula_list <- list( - mu = ~ x_cont + x_fac3t - ) - formula_data_list <- list( - mu = data_formula - ) - formula_prior_list <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont" = prior_mixture(list( - prior("normal", list(0, 1)), - prior("spike", list(0)) - )), - "x_fac3t" = prior_mixture(list( - prior("spike", list(0)), - prior_factor("mnormal", list(0, .3)) - )) - ) - ) - prior_list <- list( - sigma = prior("lognormal", list(0, 1)) - ) - model_syntax <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) - - fit <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list) - - ### density plots - vdiffr::expect_doppelganger("diagnostics-plot-mixture-1", function() JAGS_diagnostics_density(fit, parameter = "mu_x_cont")) - vdiffr::expect_doppelganger("diagnostics-plot-mixture-2", function() JAGS_diagnostics_autocorrelation(fit, parameter = "mu_x_cont")) - vdiffr::expect_doppelganger("diagnostics-plot-mixture-3", function() JAGS_diagnostics_trace(fit, parameter = "mu_x_cont")) - - vdiffr::expect_doppelganger("diagnostics-plot-mixture-4", function() JAGS_diagnostics_density(fit, parameter = "mu_x_fac3t")) - vdiffr::expect_doppelganger("diagnostics-plot-mixture-5", function() JAGS_diagnostics_autocorrelation(fit, parameter = "mu_x_fac3t")) - vdiffr::expect_doppelganger("diagnostics-plot-mixture-6", function() JAGS_diagnostics_trace(fit, parameter = "mu_x_fac3t")) -}) - -test_that("JAGS diagnostics work (meandif and independent)", { - - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes - skip_on_cran() - - set.seed(1) - - data_formula <- data.frame( - x_cont1 = rnorm(150), - x_fac2i = factor(rep(c("A", "B"), 75), levels = c("A", "B")), - x_fac3md = factor(rep(c("A", "B", "C"), 50), levels = c("A", "B", "C")) - ) - data <- list( - y = rnorm(150, .4 * data_formula$x_cont1 + ifelse(data_formula$x_fac3md == "A", 0.0, ifelse(data_formula$x_fac3md == "B", -0.2, 0.4)), ifelse(data_formula$x_fac2i == "A", 0.5, 1)), - N = 150 - ) - - - # create model with mix of a formula and free parameters --- - formula_list <- list( - mu = ~ x_cont1 + x_fac2i + x_fac3md - 1 - ) - formula_data_list <- list( - mu = data_formula - ) - formula_prior_list <- list( - mu = list( - "x_cont1" = prior("normal", list(0, 1)), - "x_fac2i" = prior_factor("normal", contrast = "independent", list(0, 1)), - "x_fac3md" = prior_factor("mnormal", contrast = "meandif", list(0, 1)) - ) - ) - prior_list <- list( - sigma = prior("lognormal", list(0, 1)) - ) - model_syntax <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) - - fit <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list) - - - ### density plots - vdiffr::expect_doppelganger("diagnostics3-plot-density-1", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(1, 2)) - JAGS_diagnostics_density(fit, parameter = "mu_x_fac2i") - }) - vdiffr::expect_doppelganger("diagnostics3-plot-density-2", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(1, 2)) - JAGS_diagnostics_density(fit, parameter = "mu_x_fac3md") - }) - vdiffr::expect_doppelganger("diagnostics3-plot-density-3", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(1, 3)) - JAGS_diagnostics_density(fit, parameter = "mu_x_fac3md", formula_prefix = FALSE, transform_factors = TRUE) - }) - - - temp_plot <- JAGS_diagnostics_density(fit, plot_type = "ggplot", parameter = "mu_x_fac2i") - vdiffr::expect_doppelganger("diagnostics3-ggplot-density-1.1",temp_plot[[1]]) - vdiffr::expect_doppelganger("diagnostics3-ggplot-density-1.2",temp_plot[[2]]) - - temp_plot <- JAGS_diagnostics_density(fit, plot_type = "ggplot", parameter = "mu_x_fac3md", transform_factors = TRUE) - vdiffr::expect_doppelganger("diagnostics3-ggplot-density-2.1",temp_plot[[1]]) - vdiffr::expect_doppelganger("diagnostics3-ggplot-density-2.2",temp_plot[[2]]) - vdiffr::expect_doppelganger("diagnostics3-ggplot-density-2.3",temp_plot[[3]]) - - - ### trace plots - vdiffr::expect_doppelganger("diagnostics3-plot-trace-1", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(1, 2)) - JAGS_diagnostics_trace(fit, parameter = "mu_x_fac2i") - }) - vdiffr::expect_doppelganger("diagnostics3-plot-trace-2", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(1, 3)) - JAGS_diagnostics_trace(fit, parameter = "mu_x_fac3md", formula_prefix = FALSE, transform_factors = TRUE) - }) - - - ### autocorrelation plots - vdiffr::expect_doppelganger("diagnostics3-plot-autocorrelation-1", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(1, 2)) - JAGS_diagnostics_autocorrelation(fit, parameter = "mu_x_fac2i") - }) - vdiffr::expect_doppelganger("diagnostics3-plot-autocorrelation-2", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(1, 3)) - JAGS_diagnostics_autocorrelation(fit, parameter = "mu_x_fac3md", formula_prefix = FALSE, transform_factors = TRUE) - }) -}) - -test_that("JAGS diagnostics work (spike priors)", { - - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes - skip_on_cran() - - set.seed(1) - - data_formula <- data.frame( - x_cont1 = rnorm(150), - x_fac2i = factor(rep(c("A", "B"), 75), levels = c("A", "B")), - x_fac3md = factor(rep(c("A", "B", "C"), 50), levels = c("A", "B", "C")), - x_fac2o = factor(rep(c("A", "B"), 75), levels = c("A", "B")), - x_fac3t = factor(rep(c("A", "B", "C"), 50), levels = c("A", "B", "C")) - ) - data <- list( - y = rnorm(150, 0.5, 1), - N = 150 - ) - - # create model with mix of a formula and free parameters --- - formula_list <- list( - mu = ~ x_cont1 + x_fac2i + x_fac3md + x_fac2o + x_fac3t - 1 - ) - formula_data_list <- list( - mu = data_formula - ) - formula_prior_list <- list( - mu = list( - "x_cont1" = prior("spike", list(0)), - "x_fac2i" = prior_factor("spike", contrast = "independent", list(1)), - "x_fac3md" = prior_factor("spike", contrast = "meandif", list(0)), - "x_fac2o" = prior_factor("spike", contrast = "orthonormal", list(0)), - "x_fac3t" = prior_factor("spike", contrast = "treatment", list(2)) - ) - ) - prior_list <- list( - sigma = prior("lognormal", list(0, 1)) - ) - model_syntax <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) - - fit <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list) - - - ### density plots - expect_message(JAGS_diagnostics_density(fit, parameter = "mu_x_cont1"), "No diagnostic plots are produced for a spike prior distribution") - expect_message(JAGS_diagnostics_density(fit, parameter = "mu_x_fac2i"), "No diagnostic plots are produced for a spike prior distribution") - expect_message(JAGS_diagnostics_trace(fit, parameter = "mu_x_fac3md"), "No diagnostic plots are produced for a spike prior distribution") - expect_message(JAGS_diagnostics_autocorrelation(fit, parameter = "mu_x_fac2o"), "No diagnostic plots are produced for a spike prior distribution") - expect_message(JAGS_diagnostics_density(fit, parameter = "mu_x_fac3t"), "No diagnostic plots are produced for a spike prior distribution") - -}) diff --git a/tests/testthat/test-JAGS-ensemble-plots.R b/tests/testthat/test-JAGS-ensemble-plots.R new file mode 100644 index 00000000..55f7b596 --- /dev/null +++ b/tests/testthat/test-JAGS-ensemble-plots.R @@ -0,0 +1,1631 @@ +# ============================================================================ # +# TEST FILE: JAGS Ensemble Plot Functions +# ============================================================================ # +# +# PURPOSE: +# Visual regression tests for ensemble plot functions (plot_prior_list, +# plot_posterior, plot_models, etc.). +# +# DEPENDENCIES: +# - vdiffr: Visual regression testing +# - rjags, bridgesampling: For tests using pre-fitted models +# - common-functions.R: temp_fits_dir, skip_if_no_fits +# +# SKIP CONDITIONS: +# - First section (prior plots): Can run on CRAN (pure R) +# - Second section (posterior plots): skip_if_no_fits(), skip_if_not_installed() +# - skip_on_os(): Multivariate sampling differs across OSes +# +# MODELS/FIXTURES: +# - fit_simple_spike, fit_simple_normal, fit_summary*, fit_marginal_* +# +# TAGS: @evaluation, @visual, @JAGS, @model-averaging, @plots +# ============================================================================ # + +REFERENCE_DIR <<- testthat::test_path("..", "results", "JAGS-ensemble-plots") +source(testthat::test_path("common-functions.R")) + +test_that("helper functions work", { + + # join duplicate + prior_list <- list( + p1 = prior("normal", list(0, 1)), + p2 = prior("lognormal", list(0, .5)), + p3 = prior("point", list(1)), + p4 = prior("normal", list(0, 1)) + ) + + simplified_list <- .simplify_prior_list(prior_list) + + expect_equal(simplified_list, list( + p1 = prior("normal", list(0, 1), prior_weights = 2), + p2 = prior("lognormal", list(0, .5)), + p3 = prior("point", list(1)) + )) + + + # no duplicate + prior_list <- list( + p1 = prior("normal", list(0, 1)), + p2 = prior("lognormal", list(0, .5)), + p3 = prior("point", list(1)) + ) + + simplified_list <- .simplify_prior_list(prior_list) + + expect_equal(simplified_list, list( + p1 = prior("normal", list(0, 1)), + p2 = prior("lognormal", list(0, .5)), + p3 = prior("point", list(1)) + )) + + + # multiple duplicates + prior_list <- list( + p1 = prior("normal", list(0, 1)), + p2 = prior("lognormal", list(0, .5)), + p3 = prior("point", list(1)), + p1 = prior("normal", list(0, 1)), + p4 = prior("normal", list(0, 1)), + p2 = prior("lognormal", list(0, .5)) + ) + + simplified_list <- .simplify_prior_list(prior_list) + + expect_equal(simplified_list, list( + p1 = prior("normal", list(0, 1), prior_weights = 3), + p2 = prior("lognormal", list(0, .5), prior_weights = 2), + p3 = prior("point", list(1)) + )) +}) + + +test_that("prior plot functions (simple) work", { + + ### simple cases + # continuous + prior_list <- list( + p1 = prior("normal", list(0, 1)) + ) + + vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-1", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4) + lines(prior_list$p1, col = "blue", lwd = 3, lty = 2) + }) + vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-2", { + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4) + geom_prior(prior_list$p1, col = "blue", lwd = 3, lty = 2) + }) + + # spike + prior_list <- list( + p1 = prior("spike", list(.5)) + ) + + vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-3", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4) + lines(prior_list$p1, col = "blue", lwd = 3, lty = 2) + }) + vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-4", { + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4) + geom_prior(prior_list$p1, col = "blue", lwd = 3, lty = 2) + }) + + + ### the prior joining should give the same prior (+ check truncation) + prior_list <- list( + p1 = prior("normal", list(0, 1), truncation = list(0, Inf)), + p2 = prior("normal", list(0, 1.001), truncation = list(0, Inf)) + ) + vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-5", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4) + lines(prior_list$p1, col = "blue", lwd = 3, lty = 2) + }) + vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-6", function(){ + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4) + geom_prior(prior_list$p1, col = "blue", lwd = 3, lty = 2) + }) + + ### mixtures + prior_list <- list( + p1 = prior("normal", list(0, 1)), + p2 = prior("normal", list(0, 1), list(1, Inf)), + p3 = prior("spike", list(.5)) + ) + vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-7", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_prior_list(prior_list) + }) + vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-8", function(){ + plot_prior_list(prior_list, plot_type = "ggplot") + }) + + # with additional settings + vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-9", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_prior_list(prior_list, xlab = "xlab", ylab = "ylab", ylab2 = "ylab2", main = "main") + }) + vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-10", function(){ + plot_prior_list(prior_list, plot_type = "ggplot", xlab = "xlab", ylab = "ylab", ylab2 = "ylab2", main = "main") + }) + + # and more spikes + prior_list <- list( + p1 = prior("normal", list(0, 1)), + p2 = prior("normal", list(0, 1), list(1, Inf)), + p3 = prior("spike", list(.5)), + p4 = prior("spike", list(-5)) + ) + vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-11", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_prior_list(prior_list) + }) + vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-12", function(){ + plot_prior_list(prior_list, plot_type = "ggplot") + }) + + # verify aggregation + prior_list <- list( + p1 = prior("normal", list(0, 1)), + p2 = prior("normal", list(0, 1)), + p3 = prior("spike", list(.5)), + p4 = prior("spike", list(.5)) + ) + vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-13", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_prior_list(prior_list, lwd = 2) + lines_prior_list(prior_list, lty = 2, col = "red", lwd = 2) + }) +}) + +# ============================================================================ # +# SECTION: Tests requiring pre-fitted models (skip on CRAN) +# ============================================================================ # +skip_on_cran() +skip_if_no_fits() +skip_if_not_installed("vdiffr") + +test_that("prior plot functions (PET-PEESE) work", { + + ### simple cases + # continuous + prior_list <- list( + p1 = prior_PET("cauchy", list(0, 1)) + ) + prior_list_mu <- list( + m1 = prior("spike", list(0)) + ) + vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-1", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4, col.fill = ggplot2::alpha("red", .20), n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + lines(prior_list$p1, col = "blue", lwd = 3, lty = 2, col.fill = ggplot2::alpha("blue", .20)) + }) + prior_list <- list( + p1 = prior_PEESE("cauchy", list(0, 2)) + ) + vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-2", { + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4, col.fill = scales::alpha("red", .20), n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + geom_prior(prior_list$p1, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20), n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + }) + + # spike + prior_list <- list( + p1 = prior_PET("point", list(.1)) + ) + vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-3", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4, n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + lines(prior_list$p1, col = "blue", lwd = 3, lty = 2) + }) + prior_list <- list( + p1 = prior_PEESE("point", list(.05)) + ) + vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-4", { + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4, n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + geom_prior(prior_list$p1, col = "blue", lwd = 3, lty = 2, n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + }) + + + ### the prior joining should give the same prior + prior_list <- list( + PET1 = prior_PET("cauchy", list(0, 1)), + PET2 = prior_PET("cauchy", list(0, 1.001)) + ) + prior_list_mu <- list( + m1 = prior("spike", list(0)), + m2 = prior("spike", list(0)) + ) + vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-5", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4, col.fill = scales::alpha("red", .20), n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + lines(prior_list$PET1, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20)) + }) + ### the prior joining should give the same prior + prior_list <- list( + PEESE1 = prior_PEESE("cauchy", list(0, 1)), + PEESE2 = prior_PEESE("cauchy", list(0, 1.001)) + ) + vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-6", function(){ + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", col.fill = scales::alpha("red", .20), lwd = 4, n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + geom_prior_list(prior_list, col = "blue", col.fill = scales::alpha("blue", .20), lwd = 3, lty = 2, n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + }) + + + ### mixtures + prior_list <- list( + p1 = prior_PET("cauchy", list(0, 1)), + p2 = prior_PEESE("cauchy", list(0, 5)) + ) + vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-7", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4, col.fill = scales::alpha("red", .20), n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + lines_prior_list(prior_list, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20), n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + }) + vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-8", function(){ + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4, col.fill = scales::alpha("red", .20), n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + geom_prior_list(prior_list, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20), n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + }) + + # with additional settings + vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-9", function(){ + plot_prior_list(prior_list, n_samples = 1000, n_points = 50, xlab = "xlab", ylab = "ylab", main = "main", prior_list_mu = prior_list_mu) + }) + vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-10", function(){ + plot_prior_list(prior_list, n_samples = 1000, n_points = 50, plot_type = "ggplot", xlab = "xlab", ylab = "ylab", main = "main", prior_list_mu = prior_list_mu) + }) + + + ### dealing with other type of priors + prior_list <- list( + p1 = prior_PET("cauchy", list(0, 1)), + p2 = prior_PEESE("cauchy", list(0, 5)), + p3 = prior_none(prior_weights = 4) + ) + prior_list_mu <- list( + m1 = prior("spike", list(0)), + m2 = prior("spike", list(0)), + m3 = prior("normal", list(.3, .15)) + ) + vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-11", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4, col.fill = scales::alpha("red", .20), n_samples = 1000, n_points = 50, ylim = c(0, .5), prior_list_mu = prior_list_mu) + lines_prior_list(prior_list, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20), n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + }) + vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-12", function(){ + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4, col.fill = scales::alpha("red", .20), n_samples = 1000, n_points = 50, ylim = c(0, .5), prior_list_mu = prior_list_mu) + geom_prior_list(prior_list, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20), n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + }) +}) + +test_that("prior plot functions (PET-PEESE) effect_direction works", { + + ### Test effect_direction parameter for PET-PEESE prior plots + prior_list <- list( + p1 = prior_PET("cauchy", list(0, 1)), + p2 = prior_PEESE("cauchy", list(0, 5)) + ) + prior_list_mu <- list( + m1 = prior("spike", list(0)), + m2 = prior("spike", list(0)) + ) + + # Test effect_direction = "positive" (default) + vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-effect-positive", function(){ + plot_prior_list(prior_list, effect_direction = "positive", col = "red", lwd = 4, col.fill = scales::alpha("red", .20), n_samples = 1000, n_points = 50, ylim = c(-0.5, 0.5), prior_list_mu = prior_list_mu) + }) + + # Test effect_direction = "negative" (flipped) + vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-effect-negative", function(){ + plot_prior_list(prior_list, effect_direction = "negative", col = "blue", lwd = 4, col.fill = scales::alpha("blue", .20), n_samples = 1000, n_points = 50, ylim = c(-0.5, 0.5), prior_list_mu = prior_list_mu) + }) + + # Test ggplot version with effect_direction + vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-effect-negative-ggplot", function(){ + plot_prior_list(prior_list, effect_direction = "negative", plot_type = "ggplot", col = "blue", lwd = 4, col.fill = scales::alpha("blue", .20), n_samples = 1000, n_points = 50, ylim = c(-0.5, 0.5), prior_list_mu = prior_list_mu) + }) + + # Test lines_prior_list with effect_direction + vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-effect-direction-overlay", function(){ + plot_prior_list(prior_list, effect_direction = "positive", col = "red", lwd = 4, col.fill = scales::alpha("red", .20), n_samples = 1000, n_points = 50, ylim = c(-0.5, 0.5), prior_list_mu = prior_list_mu) + lines_prior_list(prior_list, effect_direction = "negative", col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20), n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + }) +}) + +test_that("prior plot functions (weightfunctions) work", { + + ### simple cases + # continuous + prior_list <- list( + p1 = prior_weightfunction("one.sided", list(c(.05, 0.10), c(1, 1, 1))) + ) + + vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-1", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4, col.fill = scales::alpha("red", .20)) + lines(prior_list$p1, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20)) + }) + vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-2", { + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4, col.fill = scales::alpha("red", .20)) + geom_prior(prior_list$p1, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20)) + }) + + # spike + prior_list <- list( + p1 = prior_weightfunction("one.sided.fixed", list(c(.05), c(1, .5))) + ) + + vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-3", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4) + lines(prior_list$p1, col = "blue", lwd = 3, lty = 2) + }) + vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-4", { + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4) + geom_prior(prior_list$p1, col = "blue", lwd = 3, lty = 2) + }) + + + ### the prior joining should give the same prior + prior_list <- list( + p1 = prior_weightfunction("one.sided", list(c(.05, 0.10), c(1, 1, 1))), + p2 = prior_weightfunction("one.sided", list(c(.05, 0.10), c(1, 1, 1.0001))) + ) + vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-5", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4, col.fill = scales::alpha("red", .20)) + lines(prior_list$p1, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20)) + }) + vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-6", function(){ + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4, col.fill = scales::alpha("red", .20)) + geom_prior_list(prior_list, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20)) + }) + + + ### mixtures + prior_list <- list( + p1 = prior_weightfunction("one.sided", list(c(.025), c(1, 1))), + p2 = prior_weightfunction("two.sided", list(c(.05), c(1, 1))), + p3 = prior_weightfunction("one.sided.fixed", list(c(.05), c(1, .5)), prior_weights = 10) + ) + vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-7", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4, col.fill = scales::alpha("red", .20), rescale_x = TRUE) + lines_prior_list(prior_list, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20), rescale_x = TRUE) + }) + vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-8", function(){ + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4, col.fill = scales::alpha("red", .20), rescale_x = TRUE) + geom_prior_list(prior_list, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20), rescale_x = TRUE) + }) + + # with additional settings + vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-9", function(){ + plot_prior_list(prior_list, xlab = "xlab", ylab = "ylab", main = "main") + }) + vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-10", function(){ + plot_prior_list(prior_list, plot_type = "ggplot", xlab = "xlab", ylab = "ylab", main = "main") + }) + + + ### dealing with other type of priors + prior_list <- list( + p1 = prior_weightfunction("one.sided", list(c(.5), c(1, 1))), + p2 = prior_none(), + p3 = prior_none() + ) + vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-11", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4, col.fill = scales::alpha("red", .20), rescale_x = TRUE) + lines_prior_list(prior_list, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20), rescale_x = TRUE) + }) + vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-12", function(){ + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4, col.fill = scales::alpha("red", .20), rescale_x = TRUE) + geom_prior_list(prior_list, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20), rescale_x = TRUE) + }) + +}) + +test_that("prior plot functions (orthonormal) work", { + + ### simple cases + prior_list <- list( + p1 = prior_factor("mnormal", list(mean = 0, sd = 1), contrast = "orthonormal") + ) + prior_list$p1$parameters$K <- 3 + + vdiffr::expect_doppelganger("model-averaging-plot-prior-orthonormal-1", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4) + lines(prior_list$p1, col = "blue", lwd = 3, lty = 2) + }) + vdiffr::expect_doppelganger("model-averaging-plot-prior-orthonormal-2", { + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4) + + geom_prior(prior_list$p1, col = "blue", lwd = 3, lty = 2) + }) + + # spike & slab mixture + prior_list <- list( + p1 = prior_factor("mnormal", list(mean = 0, sd = 1), contrast = "orthonormal"), + p2 = prior("spike", list(0)) + ) + prior_list$p1$parameters$K <- 3 + + vdiffr::expect_doppelganger("model-averaging-plot-prior-orthonormal-3", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-prior-orthonormal-4", { + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4) + }) + + + ### mixtures + prior_list <- list( + p1 = prior_factor("mnormal", list(mean = 0, sd = 1), contrast = "orthonormal"), + p2 = prior_factor("mcauchy", list(location = 0, scale = 1), contrast = "orthonormal"), + p3 = prior("spike", list(0)) + ) + prior_list$p1$parameters$K <- 3 + prior_list$p2$parameters$K <- 3 + + vdiffr::expect_doppelganger("model-averaging-plot-prior-orthonormal-5", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4) + lines_prior_list(prior_list, col = "blue", lwd = 3, lty = 2) + }) + vdiffr::expect_doppelganger("model-averaging-plot-prior-orthonormal-6", function(){ + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 2) + + geom_prior_list(prior_list, col = "blue", lwd = 1, lty = 2) + }) + +}) + +test_that("prior plot functions (treatment) work", { + + ### simple cases + prior_list <- list( + p1 = prior_factor("beta", list(alpha = 4, beta = 5), contrast = "treatment") + ) + + vdiffr::expect_doppelganger("model-averaging-plot-prior-treatment-1", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4) + lines(prior_list$p1, col = "blue", lwd = 3, lty = 2) + }) + vdiffr::expect_doppelganger("model-averaging-plot-prior-treatment-2", { + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4) + + geom_prior(prior_list$p1, col = "blue", lwd = 3, lty = 2) + }) + + # spike & slab mixture + prior_list <- list( + p1 = prior_factor("beta", list(alpha = 4, beta = 5), contrast = "treatment"), + p2 = prior("spike", list(0)) + ) + + vdiffr::expect_doppelganger("model-averaging-plot-prior-treatment-3", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-prior-treatment-4", { + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 2) + }) + + + ### mixtures + prior_list <- list( + p1 = prior_factor("normal", list(mean = 0, sd = 1), contrast = "treatment"), + p2 = prior_factor("beta", list(alpha = 4, beta = 5), contrast = "treatment"), + p3 = prior("spike", list(0)) + ) + + vdiffr::expect_doppelganger("model-averaging-plot-prior-treatment-5", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4) + lines_prior_list(prior_list, col = "blue", lwd = 3, lty = 2) + }) + vdiffr::expect_doppelganger("model-averaging-plot-prior-treatment-6", function(){ + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 2) + + geom_prior_list(prior_list, col = "blue", lwd = 1, lty = 2) + }) + +}) + +test_that("prior plot functions (independent) work", { + + ### simple cases + prior_list <- list( + p1 = prior_factor("beta", list(alpha = 4, beta = 5), contrast = "independent") + ) + + vdiffr::expect_doppelganger("model-averaging-plot-prior-independent-1", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4) + lines(prior_list$p1, col = "blue", lwd = 3, lty = 2) + }) + + # spike & slab mixture + prior_list <- list( + p1 = prior_factor("beta", list(alpha = 4, beta = 5), contrast = "independent"), + p2 = prior("spike", list(0)) + ) + + vdiffr::expect_doppelganger("model-averaging-plot-prior-independent-2", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4) + }) + + ### mixtures + prior_list <- list( + p1 = prior_factor("normal", list(mean = 0, sd = 1), contrast = "independent"), + p2 = prior_factor("beta", list(alpha = 4, beta = 5), contrast = "independent"), + p3 = prior("spike", list(0)) + ) + + vdiffr::expect_doppelganger("model-averaging-plot-prior-independent-3", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4) + lines_prior_list(prior_list, col = "blue", lwd = 3, lty = 2) + }) + vdiffr::expect_doppelganger("model-averaging-plot-prior-independent-4", function(){ + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 2) + + geom_prior_list(prior_list, col = "blue", lwd = 1, lty = 2) + }) + +}) + +test_that("prior plot functions (meandif) work", { + + ### simple cases + prior_list <- list( + p1 = prior_factor("mnormal", list(mean = 0, sd = 1), contrast = "meandif") + ) + prior_list$p1$parameters$K <- 3 + + vdiffr::expect_doppelganger("model-averaging-plot-prior-meandif-1", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4) + lines(prior_list$p1, col = "blue", lwd = 3, lty = 2) + }) + vdiffr::expect_doppelganger("model-averaging-plot-prior-meandif-2", { + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4) + + geom_prior(prior_list$p1, col = "blue", lwd = 3, lty = 2) + }) + + # spike & slab mixture + prior_list <- list( + p1 = prior_factor("mnormal", list(mean = 0, sd = 1), contrast = "meandif"), + p2 = prior("spike", list(0)) + ) + prior_list$p1$parameters$K <- 3 + + vdiffr::expect_doppelganger("model-averaging-plot-prior-meandif-3", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-prior-meandif-4", { + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4) + }) + + + ### mixtures + prior_list <- list( + p1 = prior_factor("mnormal", list(mean = 0, sd = 1), contrast = "meandif"), + p2 = prior_factor("mcauchy", list(location = 0, scale = 1), contrast = "meandif"), + p3 = prior("spike", list(0)) + ) + prior_list$p1$parameters$K <- 3 + prior_list$p2$parameters$K <- 3 + + vdiffr::expect_doppelganger("model-averaging-plot-prior-meandif-5", function(){ + plot_prior_list(prior_list, col = "red", lwd = 4) + lines_prior_list(prior_list, col = "blue", lwd = 3, lty = 2) + }) + vdiffr::expect_doppelganger("model-averaging-plot-prior-meandif-6", function(){ + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 2) + + geom_prior_list(prior_list, col = "blue", lwd = 1, lty = 2) + }) + +}) + +test_that("posterior plot functions (simple) work", { + + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + fit0 <- readRDS(file.path(temp_fits_dir, "fit_simple_spike.RDS")) + marglik0 <- readRDS(file.path(temp_marglik_dir, "fit_simple_spike.RDS")) + + fit1 <- readRDS(file.path(temp_fits_dir, "fit_simple_normal.RDS")) + marglik1 <- readRDS(file.path(temp_marglik_dir, "fit_simple_normal.RDS")) + + # automatically mix posteriors + models <- list( + list(fit = fit0, marglik = marglik0, prior_weights = 1), + list(fit = fit1, marglik = marglik1, prior_weights = 1) + ) + mixed_posteriors <- mix_posteriors(model_list = models, parameters = c("m", "s"), is_null_list = list("m" = 1, "s" = 0), seed = 1) + + + vdiffr::expect_doppelganger("model-averaging-plot-posterior-simple-1", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors, "m", lwd = 2, col = "red", par_name = expression(mu)) + lines_prior_list(attr(mixed_posteriors$m, "prior_list"), col = "blue") + }) + vdiffr::expect_doppelganger("model-averaging-plot-posterior-simple-2", { + plot_posterior(mixed_posteriors, "m", plot_type = "ggplot", lwd = 2, col = "red") + geom_prior_list(attr(mixed_posteriors$m, "prior_list"), col = "blue") + }) + + # checks truncation + vdiffr::expect_doppelganger("model-averaging-plot-posterior-simple-3", function(){ + plot_posterior(mixed_posteriors, "s") + lines_prior_list(attr(mixed_posteriors$s, "prior_list"), col = "blue") + }) + vdiffr::expect_doppelganger("model-averaging-plot-posterior-simple-4", { + plot_posterior(mixed_posteriors, "s", plot_type = "ggplot") + }) + + # check transformation + vdiffr::expect_doppelganger("model-averaging-plot-posterior-simple-5", function(){ + plot_posterior(mixed_posteriors, "s", transformation = "exp") + lines_prior_list(attr(mixed_posteriors$s, "prior_list"), col = "blue", transformation = "exp") + }) + + # prior and posterior + vdiffr::expect_doppelganger("model-averaging-plot-posterior-simple-6", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors, "m", lwd = 2, col = "red", prior = TRUE, dots_prior = list(col = "blue", lty = 2)) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-posterior-simple-7", function(){ + plot_posterior(mixed_posteriors, "m", plot_type = "ggplot", lwd = 2, col = "red", prior = TRUE, dots_prior = list(col = "blue", lty = 2)) + }) + + # check transformation + vdiffr::expect_doppelganger("model-averaging-plot-posterior-simple-8", function(){ + plot_posterior(mixed_posteriors, "s", transformation = "exp", lwd = 2, col = "red", prior = TRUE, dots_prior = list(col = "blue", lty = 2)) + }) +}) + +test_that("posterior plot functions (PET-PEESE) work", { + + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + fit0 <- readRDS(file.path(temp_fits_dir, "fit_pet.RDS")) + marglik0 <- readRDS(file.path(temp_marglik_dir, "fit_pet.RDS")) + fit1 <- readRDS(file.path(temp_fits_dir, "fit_peese.RDS")) + marglik1 <- readRDS(file.path(temp_marglik_dir, "fit_peese.RDS")) + + # automatically mix posteriors + models <- list( + list(fit = fit0, marglik = marglik0, prior_weights = 1), + list(fit = fit1, marglik = marglik1, prior_weights = 1) + ) + mixed_posteriors <- mix_posteriors(model_list = models, parameters = c("mu", "PET", "PEESE"), is_null_list = list("mu" = c(T, T), "PET" = c(F,T), "PEESE" = c(T,F)), seed = 1) + + # Reconstruct priors for plotting (since we don't have them in the test scope directly) + priors_list0 <- list( + mu = prior("spike", list(0)), + PET = prior_PET("normal", list(0, .2)) + ) + priors_list1 <- list( + mu = prior("spike", list(0)), + PEESE = prior_PEESE("normal", list(0, .8)) + ) + + vdiffr::expect_doppelganger("model-averaging-plot-posterior-PETPEESE-1", function(){ + plot_posterior(mixed_posteriors, "PETPEESE", lwd = 2, col = "red", col.fill = scales::alpha("red", .20), par_name = "PET-PEESE", n_points = 50, ylim = c(0, 1)) + lines_prior_list(list(priors_list0$PET, priors_list1$PEESE), n_points = 50, n_samples = 1000, col = "blue", col.fill = scales::alpha("blue", .20), prior_list_mu = list(priors_list0$mu, priors_list1$mu)) + }) + vdiffr::expect_doppelganger("model-averaging-plot-posterior-PETPEESE-2", { + plot_posterior(mixed_posteriors, "PETPEESE", plot_type = "ggplot", lwd = 2, col = "red", col.fill = scales::alpha("red", .20), ylim = c(0, .5)) + geom_prior_list(list(priors_list0$PET, priors_list1$PEESE), n_points = 50, n_samples = 1000, col = "blue", col.fill = scales::alpha("blue", .20), prior_list_mu = list(priors_list0$mu, priors_list1$mu)) + }) + + # check transformation + vdiffr::expect_doppelganger("model-averaging-plot-posterior-PETPEESE-5", function(){ + plot_posterior(mixed_posteriors, "PETPEESE", transformation = "lin", transformation_arguments = list(a = 0, b = 0.5), main = "PET-PEESE (1/2x)") + lines_prior_list(list(priors_list0$PET, priors_list1$PEESE), n_points = 50, n_samples = 1000, col = "blue", col.fill = scales::alpha("blue", .20), transformation = "lin", transformation_arguments = list(a = 0, b = 0.5), prior_list_mu = list(priors_list0$mu, priors_list1$mu)) + }) + + # prior and posterior + vdiffr::expect_doppelganger("model-averaging-plot-posterior-PETPEESE-6", function(){ + plot_posterior(mixed_posteriors, "PETPEESE", lwd = 2, col = "red", col.fill = scales::alpha("red", .20), prior = TRUE, n_points = 50, n_samples = 1000, dots_prior = list(col = "blue", col.fill = scales::alpha("blue", .20), lty = 2)) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-posterior-PETPEESE-7", function(){ + plot_posterior(mixed_posteriors, "PETPEESE", plot_type = "ggplot", lwd = 2, col = "red", col.fill = scales::alpha("red", .20), n_points = 50, n_samples = 1000, prior = TRUE, dots_prior = list(col = "blue", col.fill = scales::alpha("blue", .20), lty = 2)) + }) + + # check transformation + vdiffr::expect_doppelganger("model-averaging-plot-posterior-PETPEESE-8", function(){ + plot_posterior(mixed_posteriors, "PETPEESE", transformation = "lin", transformation_arguments = list(a = 0, b = 0.5), lwd = 2, col = "red", n_points = 50, n_samples = 1000, col.fill = scales::alpha("red", .20), prior = TRUE, dots_prior = list(col = "blue", col.fill = scales::alpha("blue", .20), lty = 2)) + }) + + # add an overhelming missing model + fit2 <- readRDS(file.path(temp_fits_dir, "fit_missing.RDS")) + marglik2 <- readRDS(file.path(temp_marglik_dir, "fit_missing.RDS")) + + models <- list( + list(fit = fit0, marglik = marglik0, prior_weights = 1), + list(fit = fit1, marglik = marglik1, prior_weights = 1), + list(fit = fit2, marglik = marglik2, prior_weights = 4) + ) + mixed_posteriors <- mix_posteriors(model_list = models, parameters = c("mu" ,"PET", "PEESE"), is_null_list = list("mu" = c(T, T, F),"PET" = c(F,T,F), "PEESE" = c(T,F,F)), seed = 1) + vdiffr::expect_doppelganger("model-averaging-plot-posterior-PETPEESE-9", function(){ + plot_posterior(mixed_posteriors, "PETPEESE", ylim = c(0, 3), lwd = 2, col = "red", col.fill = scales::alpha("red", .20), n_points = 50, n_samples = 1000, prior = TRUE, dots_prior = list(col = "blue", col.fill = scales::alpha("blue", .20), lty = 2)) + }) + +}) + +test_that("posterior plot functions (PET-PEESE) effect_direction works", { + + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + fit0 <- readRDS(file.path(temp_fits_dir, "fit_pet.RDS")) + marglik0 <- readRDS(file.path(temp_marglik_dir, "fit_pet.RDS")) + fit1 <- readRDS(file.path(temp_fits_dir, "fit_peese.RDS")) + marglik1 <- readRDS(file.path(temp_marglik_dir, "fit_peese.RDS")) + + # automatically mix posteriors + models <- list( + list(fit = fit0, marglik = marglik0, prior_weights = 1), + list(fit = fit1, marglik = marglik1, prior_weights = 1) + ) + mixed_posteriors <- mix_posteriors(model_list = models, parameters = c("mu", "PET", "PEESE"), is_null_list = list("mu" = c(T, T), "PET" = c(F, T), "PEESE" = c(T, F)), seed = 1) + + # Reconstruct priors for plotting + priors_list0 <- list( + mu = prior("spike", list(0)), + PET = prior_PET("normal", list(0, .2)) + ) + priors_list1 <- list( + mu = prior("spike", list(0)), + PEESE = prior_PEESE("normal", list(0, .8)) + ) + + # Test effect_direction = "positive" (default behavior) + vdiffr::expect_doppelganger("model-averaging-plot-posterior-PETPEESE-effect-positive", function(){ + plot_posterior(mixed_posteriors, "PETPEESE", effect_direction = "positive", lwd = 2, col = "red", col.fill = scales::alpha("red", .20), par_name = "PET-PEESE (positive)", n_points = 50, ylim = c(-1, 1)) + }) + + # Test effect_direction = "negative" (flipped regression) + vdiffr::expect_doppelganger("model-averaging-plot-posterior-PETPEESE-effect-negative", function(){ + plot_posterior(mixed_posteriors, "PETPEESE", effect_direction = "negative", lwd = 2, col = "blue", col.fill = scales::alpha("blue", .20), par_name = "PET-PEESE (negative)", n_points = 50, ylim = c(-1, 1)) + }) + + # Test with prior overlay using effect_direction + vdiffr::expect_doppelganger("model-averaging-plot-posterior-PETPEESE-effect-positive-prior", function(){ + plot_posterior(mixed_posteriors, "PETPEESE", effect_direction = "positive", prior = TRUE, lwd = 2, col = "red", col.fill = scales::alpha("red", .20), n_points = 50, n_samples = 1000, ylim = c(-1, 1), dots_prior = list(col = "grey", col.fill = scales::alpha("grey", .20), lty = 2)) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-posterior-PETPEESE-effect-negative-prior", function(){ + plot_posterior(mixed_posteriors, "PETPEESE", effect_direction = "negative", prior = TRUE, lwd = 2, col = "blue", col.fill = scales::alpha("blue", .20), n_points = 50, n_samples = 1000, ylim = c(-1, 1), dots_prior = list(col = "grey", col.fill = scales::alpha("grey", .20), lty = 2)) + }) + + # Test ggplot version with effect_direction + vdiffr::expect_doppelganger("model-averaging-plot-posterior-PETPEESE-effect-negative-ggplot", function(){ + plot_posterior(mixed_posteriors, "PETPEESE", effect_direction = "negative", plot_type = "ggplot", lwd = 2, col = "blue", col.fill = scales::alpha("blue", .20), n_points = 50, ylim = c(-1, 1)) + }) +}) + +test_that("posterior plot functions (weightfunctions) work", { + + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + fit0 <- readRDS(file.path(temp_fits_dir, "fit_wf_onesided.RDS")) + marglik0 <- readRDS(file.path(temp_marglik_dir, "fit_wf_onesided.RDS")) + fit1 <- readRDS(file.path(temp_fits_dir, "fit_wf_twosided.RDS")) + marglik1 <- readRDS(file.path(temp_marglik_dir, "fit_wf_twosided.RDS")) + + # automatically mix posteriors + models <- list( + list(fit = fit0, marglik = marglik0, prior_weights = 1), + list(fit = fit1, marglik = marglik1, prior_weights = 1) + ) + mixed_posteriors <- mix_posteriors(model_list = models, parameters = "omega", is_null_list = list("omega" = c(F,F)), seed = 1) + + # Reconstruct priors + priors_list0 <- list( + omega = prior_weightfunction("one.sided", list(c(.025), c(1, 1))) + ) + priors_list1 <- list( + omega = prior_weightfunction("two.sided", list(c(.05), c(1, 1))) + ) + + vdiffr::expect_doppelganger("model-averaging-plot-posterior-wf-1", function(){ + plot_posterior(mixed_posteriors, "omega", lwd = 2, col = "red", col.fill = scales::alpha("red", .20), par_name = "Selection Models", n_points = 50, ylim = c(0, 1)) + lines_prior_list(list(priors_list0$omega, priors_list1$omega), n_points = 50, n_samples = 1000, col = "blue", col.fill = scales::alpha("blue", .20)) + }) + vdiffr::expect_doppelganger("model-averaging-plot-posterior-wf-2", { + plot_posterior(mixed_posteriors, "omega", plot_type = "ggplot", lwd = 2, col = "red", col.fill = scales::alpha("red", .20)) + geom_prior_list(list(priors_list0$omega, priors_list1$omega), n_points = 50, n_samples = 1000, col = "blue", col.fill = scales::alpha("blue", .20)) + }) + + # rescale-x + vdiffr::expect_doppelganger("model-averaging-plot-posterior-wf-3", function(){ + plot_posterior(mixed_posteriors, "omega", lwd = 2, rescale_x = TRUE, col = "red", col.fill = scales::alpha("red", .20), par_name = "Selection Models", n_points = 50, ylim = c(0, 1)) + lines_prior_list(list(priors_list0$omega, priors_list1$omega), rescale_x = TRUE, n_points = 50, n_samples = 1000, col = "blue", col.fill = scales::alpha("blue", .20)) + }) + vdiffr::expect_doppelganger("model-averaging-plot-posterior-wf-4", { + plot_posterior(mixed_posteriors, "omega", rescale_x = TRUE, plot_type = "ggplot", lwd = 2, col = "red", col.fill = scales::alpha("red", .20)) + geom_prior_list(list(priors_list0$omega, priors_list1$omega), rescale_x = TRUE, n_points = 50, n_samples = 1000, col = "blue", col.fill = scales::alpha("blue", .20)) + }) + + # prior and posterior + vdiffr::expect_doppelganger("model-averaging-plot-posterior-wf-6", function(){ + plot_posterior(mixed_posteriors, "omega", lwd = 2, col = "red", col.fill = scales::alpha("red", .20), prior = TRUE, n_points = 50, n_samples = 1000, dots_prior = list(col = "blue", col.fill = scales::alpha("blue", .20), lty = 2)) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-posterior-wf-7", function(){ + plot_posterior(mixed_posteriors, "omega", plot_type = "ggplot", lwd = 2, col = "red", col.fill = scales::alpha("red", .20), n_points = 50, n_samples = 1000, prior = TRUE, dots_prior = list(col = "blue", col.fill = scales::alpha("blue", .20), lty = 2)) + }) + + # rescale-x + vdiffr::expect_doppelganger("model-averaging-plot-posterior-wf-8", function(){ + plot_posterior(mixed_posteriors, "omega", rescale_x = TRUE, lwd = 2, col = "red", col.fill = scales::alpha("red", .20), prior = TRUE, n_points = 50, n_samples = 1000, dots_prior = list(col = "blue", col.fill = scales::alpha("blue", .20), lty = 2)) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-posterior-wf-9", function(){ + plot_posterior(mixed_posteriors, "omega", rescale_x = TRUE, plot_type = "ggplot", lwd = 2, col = "red", col.fill = scales::alpha("red", .20), n_points = 50, n_samples = 1000, prior = TRUE, dots_prior = list(col = "blue", col.fill = scales::alpha("blue", .20), lty = 2)) + }) + + # add an overhelming missing model + fit2 <- readRDS(file.path(temp_fits_dir, "fit_wf_missing.RDS")) + marglik2 <- readRDS(file.path(temp_marglik_dir, "fit_wf_missing.RDS")) + + models <- list( + list(fit = fit0, marglik = marglik0, prior_weights = 1), + list(fit = fit1, marglik = marglik1, prior_weights = 1), + list(fit = fit2, marglik = marglik2, prior_weights = 5) + ) + mixed_posteriors <- mix_posteriors(model_list = models, parameters = "omega", is_null_list = list("omega" = c(F,F,F)), seed = 1) + vdiffr::expect_doppelganger("model-averaging-plot-posterior-wf-10", function(){ + plot_posterior(mixed_posteriors, "omega", lwd = 2, col = "red", col.fill = scales::alpha("red", .20), n_points = 50, n_samples = 1000, prior = TRUE, dots_prior = list(col = "blue", col.fill = scales::alpha("blue", .20), lty = 2)) + }) + +}) + +test_that("posterior plot functions (orthonormal) work", { + + skip_on_os(c("mac", "linux", "solaris")) + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + fit0 <- readRDS(file.path(temp_fits_dir, "fit_orthonormal_0.RDS")) + marglik0 <- readRDS(file.path(temp_marglik_dir, "fit_orthonormal_0.RDS")) + fit1 <- readRDS(file.path(temp_fits_dir, "fit_orthonormal_1.RDS")) + marglik1 <- readRDS(file.path(temp_marglik_dir, "fit_orthonormal_1.RDS")) + + # mix posteriors + models <- list( + list(fit = fit0, marglik = marglik0, prior_weights = 1), + list(fit = fit1, marglik = marglik1, prior_weights = 1) + ) + + mixed_posteriors <- mix_posteriors( + model_list = models, + parameters = c("mu_intercept", "mu_x_fac3o"), + is_null_list = list( + "mu_intercept" = c(TRUE, TRUE), + "mu_x_fac3o" = c(FALSE, TRUE) + ), + seed = 1, n_samples = 10000) + mixed_posteriors2 <- mix_posteriors( + model_list = models, + parameters = c("mu_x_fac3o"), + conditional = TRUE, + is_null_list = list( + "mu_x_fac3o" = c(TRUE, FALSE) + ), + seed = 1, n_samples = 10000) + + vdiffr::expect_doppelganger("model-averaging-plot-posterior-o-1", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors, "mu_x_fac3o") + }) + vdiffr::expect_doppelganger("model-averaging-plot-posterior-o-2", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors, "mu_x_fac3o", col = c("red", "green", "blue")) + }) + vdiffr::expect_doppelganger("model-averaging-plot-posterior-o-3", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors, "mu_x_fac3o", lty = c(2, 3, 4), col = "blue", lwd = 2) + }) + vdiffr::expect_doppelganger("model-averaging-plot-posterior-o-4", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors, "mu_x_fac3o", legend = FALSE) + }) + vdiffr::expect_doppelganger("model-averaging-plot-posterior-o-5", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors2, "mu_x_fac3o") + }) +}) + +test_that("posterior plot functions (treatment) work", { + + skip_on_os(c("mac", "linux", "solaris")) + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + fit0 <- readRDS(file.path(temp_fits_dir, "fit_factor_treatment.RDS")) + # Create dummy marginal likelihood since this model doesn't have one + marglik0 <- structure(list(logml = -10), class = "bridge") + + # Create a second model with different prior for comparison + fit1 <- readRDS(file.path(temp_fits_dir, "fit_factor_treatment.RDS")) + marglik1 <- structure(list(logml = -12), class = "bridge") + + # mix posteriors + models <- list( + list(fit = fit0, marglik = marglik0, prior_weights = 1), + list(fit = fit1, marglik = marglik1, prior_weights = 1) + ) + + mixed_posteriors <- mix_posteriors( + model_list = models, + parameters = c("p1"), + is_null_list = list( + "p1" = c(TRUE, FALSE) + ), + seed = 1, n_samples = 10000) + mixed_posteriors2 <- mix_posteriors( + model_list = models, + parameters = c("p1"), + conditional = TRUE, + is_null_list = list( + "p1" = c(TRUE, FALSE) + ), + seed = 1, n_samples = 10000) + + vdiffr::expect_doppelganger("model-averaging-plot-posterior-t-1", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors, "p1") + }) + vdiffr::expect_doppelganger("model-averaging-plot-posterior-t-2", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors2, "p1") + }) +}) + +test_that("posterior plot functions (independent) work", { + + skip_on_os(c("mac", "linux", "solaris")) + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + fit0 <- readRDS(file.path(temp_fits_dir, "fit_factor_independent.RDS")) + # Create dummy marginal likelihood since this model doesn't have one + marglik0 <- structure(list(logml = -15), class = "bridge") + + # Create a second model with different prior for comparison + fit1 <- readRDS(file.path(temp_fits_dir, "fit_factor_independent.RDS")) + marglik1 <- structure(list(logml = -17), class = "bridge") + + # mix posteriors + models <- list( + list(fit = fit0, marglik = marglik0, prior_weights = 1), + list(fit = fit1, marglik = marglik1, prior_weights = 1) + ) + + mixed_posteriors <- mix_posteriors( + model_list = models, + parameters = c("p1"), + is_null_list = list( + "p1[1]" = c(TRUE, FALSE) + ), + seed = 1, n_samples = 10000) + + mixed_posteriors2 <- mix_posteriors( + model_list = models, + parameters = c("p1"), + conditional = TRUE, + is_null_list = list( + "p1[1]" = c(TRUE, FALSE) + ), + seed = 1, n_samples = 10000) + + vdiffr::expect_doppelganger("model-averaging-plot-posterior-i-1", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors, "p1") + }) + + vdiffr::expect_doppelganger("model-averaging-plot-posterior-i-2", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors2, "p1") + }) +}) + +test_that("posterior plot functions (meandif) work", { + + skip_on_os(c("mac", "linux", "solaris")) + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + fit0 <- readRDS(file.path(temp_fits_dir, "fit_factor_meandif.RDS")) + # Create dummy marginal likelihood since this model doesn't have one + marglik0 <- structure(list(logml = -20), class = "bridge") + + # Create a second model with different prior for comparison + fit1 <- readRDS(file.path(temp_fits_dir, "fit_factor_meandif.RDS")) + marglik1 <- structure(list(logml = -22), class = "bridge") + + # mix posteriors + models <- list( + list(fit = fit0, marglik = marglik0, prior_weights = 1), + list(fit = fit1, marglik = marglik1, prior_weights = 1) + ) + + mixed_posteriors <- mix_posteriors( + model_list = models, + parameters = c("p1"), + is_null_list = list( + "p" = c(TRUE, FALSE) + ), + seed = 1, n_samples = 10000) + mixed_posteriors2 <- mix_posteriors( + model_list = models, + parameters = c("p1"), + conditional = TRUE, + is_null_list = list( + "p" = c(TRUE, FALSE) + ), + seed = 1, n_samples = 10000) + + vdiffr::expect_doppelganger("model-averaging-plot-posterior-m-1", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors, "p1") + }) + vdiffr::expect_doppelganger("model-averaging-plot-posterior-m-2", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors2, "p1") + }) + +}) + +test_that("posterior plot model averaging based on complex single JAGS models (formulas + spike factors + mixture)", { + + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + fit1 <- readRDS(file.path(temp_fits_dir, "fit_complex_mixed.RDS")) + + mixed_posteriors <- as_mixed_posteriors( + mode = fit1, + parameters = names(attr(fit1, "prior_list")) + ) + + vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-intercept", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors, "mu_intercept", prior = T, dots_prior = list(col = "grey")) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-x_cont1", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors, "mu_x_cont1", prior = T, dots_prior = list(col = "grey")) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-x_fac2t", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors, "mu_x_fac2t", prior = T, dots_prior = list(col = "grey")) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-x_fac3t", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors, "mu_x_fac3t", prior = T, dots_prior = list(col = "grey")) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-sigma", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors, "sigma", prior = T, dots_prior = list(col = "grey")) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-bias-PETPEESE", function(){ + plot_posterior(mixed_posteriors, "PETPEESE", prior = T, dots_prior = list(col.fill = "orange"), ylim = c(-2, 2)) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-bias-PET", function(){ + plot_posterior(mixed_posteriors, "PET", prior = T, dots_prior = list(col.fill = "orange"), ylim = c(-2, 2)) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-bias-PET-ind", function(){ + plot_posterior(mixed_posteriors, "PET", prior = T, dots_prior = list(col = "grey"), individual = TRUE) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-bias-weighfunction", function(){ + plot_posterior(mixed_posteriors, "omega", prior = T, dots_prior = list(col.fill = "orange")) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-bias-omega", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4), mfrow = c(2, 2)) + plot_posterior(mixed_posteriors, "omega", prior = T, dots_prior = list(col = "grey"), individual = TRUE, show_figures = 1) + plot_posterior(mixed_posteriors, "omega", prior = T, dots_prior = list(col = "grey"), individual = TRUE, show_figures = 2) + plot_posterior(mixed_posteriors, "omega", prior = T, dots_prior = list(col = "grey"), individual = TRUE, show_figures = 3) + plot_posterior(mixed_posteriors, "omega", prior = T, dots_prior = list(col = "grey"), individual = TRUE, show_figures = 4) + }) + + + mixed_posteriors_conditional1 <- as_mixed_posteriors( + mode = fit1, + parameters = "mu_intercept", + conditional = "mu_intercept", + force_plots = TRUE + ) + + vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-intercept-con", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors_conditional1, "mu_intercept", prior = TRUE, dots_prior = list(col = "grey")) + }) + + mixed_posteriors_conditional2 <- as_mixed_posteriors( + mode = fit1, + parameters = "mu_x_cont1", + conditional = "mu_x_cont1", + force_plots = TRUE + ) + + vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-x_cont1-con", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors_conditional2, "mu_x_cont1", prior = TRUE, dots_prior = list(col = "grey")) + }) + + mixed_posteriors_conditional3 <- as_mixed_posteriors( + mode = fit1, + parameters = "mu_x_fac2t", + conditional = "mu_x_fac2t", + force_plots = TRUE + ) + + vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-x_fac2t-con", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors_conditional3, "mu_x_fac2t", prior = TRUE, dots_prior = list(col = "grey")) + }) + + mixed_posteriors_conditional4 <- as_mixed_posteriors( + mode = fit1, + parameters = "mu_x_fac3t", + conditional = "mu_x_fac3t", + force_plots = TRUE + ) + + vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-x_fac3t-con", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors_conditional4, "mu_x_fac3t", prior = TRUE, dots_prior = list(col = "grey")) + }) + + + mixed_posteriors_conditional5a <- as_mixed_posteriors( + mode = fit1, + parameters = c("mu_intercept", "bias") + ) + + mixed_posteriors_conditional5b <- as_mixed_posteriors( + mode = fit1, + parameters = c("mu_intercept", "bias"), + conditional = "bias", + force_plots = TRUE + ) + + mixed_posteriors_conditional6a <- as_mixed_posteriors( + mode = fit1, + parameters = c("mu_intercept", "bias"), + conditional = "PETPEESE", + force_plots = TRUE + ) + + mixed_posteriors_conditional6b <- as_mixed_posteriors( + mode = fit1, + parameters = c("mu_intercept", "bias"), + conditional = "PET", + force_plots = TRUE + ) + + mixed_posteriors_conditional6c <- as_mixed_posteriors( + mode = fit1, + parameters = "bias", + conditional = "omega", + force_plots = TRUE + ) + + vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-weightfunction", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors_conditional5a, parameter = "weightfunction", prior = TRUE, col = "black", col.fill = ggplot2::alpha("grey", 0.2), dots_prior = list(col = "red", col.fill = ggplot2::alpha("red", 0.5))) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-weightfunction-con", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors_conditional6c, parameter = "weightfunction", prior = TRUE, col = "black", col.fill = ggplot2::alpha("grey", 0.2), dots_prior = list(col = "red", col.fill = ggplot2::alpha("red", 0.5))) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-PETPEESE", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors_conditional5a, parameter = "PETPEESE", prior = TRUE, col = "black", col.fill = ggplot2::alpha("grey", 0.2), dots_prior = list(col = "red", col.fill = ggplot2::alpha("red", 0.5)), ylim = c(-2, 2)) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-PETPEESE-con", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors_conditional6a, parameter = "PETPEESE", prior = TRUE, col = "black", col.fill = ggplot2::alpha("grey", 0.2), dots_prior = list(col = "red", col.fill = ggplot2::alpha("red", 0.5)), ylim = c(-2, 2)) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-PET-con", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4), mfrow = c(3, 1)) + plot_posterior(mixed_posteriors_conditional5a, parameter = "PET", prior = TRUE, col = "black", col.fill = ggplot2::alpha("grey", 0.2), dots_prior = list(col = "red", col.fill = ggplot2::alpha("red", 0.5)), individual = TRUE) + plot_posterior(mixed_posteriors_conditional6b, parameter = "PET", prior = TRUE, col = "black", col.fill = ggplot2::alpha("grey", 0.2), dots_prior = list(col = "red", col.fill = ggplot2::alpha("red", 0.5)), individual = TRUE) + plot_posterior(mixed_posteriors_conditional6c, parameter = "PET", prior = TRUE, col = "black", col.fill = ggplot2::alpha("grey", 0.2), dots_prior = list(col = "red", col.fill = ggplot2::alpha("red", 0.5)), individual = TRUE) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-omega-con", function(){ + + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4), mfrow = c(3, 4)) + plot_posterior(mixed_posteriors_conditional5a, "omega", prior = T, dots_prior = list(col = "grey"), individual = TRUE, show_figures = 1) + plot_posterior(mixed_posteriors_conditional5a, "omega", prior = T, dots_prior = list(col = "grey"), individual = TRUE, show_figures = 2) + plot_posterior(mixed_posteriors_conditional5a, "omega", prior = T, dots_prior = list(col = "grey"), individual = TRUE, show_figures = 3) + plot_posterior(mixed_posteriors_conditional5a, "omega", prior = T, dots_prior = list(col = "grey"), individual = TRUE, show_figures = 4) + + plot_posterior(mixed_posteriors_conditional5b, "omega", prior = T, dots_prior = list(col = "grey"), individual = TRUE, show_figures = 1) + plot_posterior(mixed_posteriors_conditional5b, "omega", prior = T, dots_prior = list(col = "grey"), individual = TRUE, show_figures = 2) + plot_posterior(mixed_posteriors_conditional5b, "omega", prior = T, dots_prior = list(col = "grey"), individual = TRUE, show_figures = 3) + plot_posterior(mixed_posteriors_conditional5b, "omega", prior = T, dots_prior = list(col = "grey"), individual = TRUE, show_figures = 4) + + plot_posterior(mixed_posteriors_conditional6c, "omega", prior = T, dots_prior = list(col = "grey"), individual = TRUE, show_figures = 1) + plot_posterior(mixed_posteriors_conditional6c, "omega", prior = T, dots_prior = list(col = "grey"), individual = TRUE, show_figures = 2) + plot_posterior(mixed_posteriors_conditional6c, "omega", prior = T, dots_prior = list(col = "grey"), individual = TRUE, show_figures = 3) + plot_posterior(mixed_posteriors_conditional6c, "omega", prior = T, dots_prior = list(col = "grey"), individual = TRUE, show_figures = 4) + + }) + +}) + +test_that("posterior plot model averaging based on simple single JAGS models (formulas)", { + + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + fit1 <- readRDS(file.path(temp_fits_dir, "fit_simple_formula_mixed.RDS")) + + mixed_posteriors <- as_mixed_posteriors( + mode = fit1, + parameters = names(attr(fit1, "prior_list")) + ) + + vdiffr::expect_doppelganger("model-averaging-simple-plot-ss-posterior-intercept", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors, "mu_intercept", prior = T, dots_prior = list(col = "grey")) + }) + + vdiffr::expect_doppelganger("model-averaging-simple-plot-ss-posterior-x_cont1", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors, "mu_x_cont1", prior = T, dots_prior = list(col = "grey")) + }) + + vdiffr::expect_doppelganger("model-averaging-simple-plot-ss-posterior-x_fac2t", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors, "mu_x_fac2t", prior = T, dots_prior = list(col = "grey")) + }) + + vdiffr::expect_doppelganger("model-averaging-simple-plot-ss-posterior-x_fac3t", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors, "mu_x_fac3t", prior = T, dots_prior = list(col = "grey")) + }) + + vdiffr::expect_doppelganger("model-averaging-simple-plot-ss-posterior-sigma", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors, "sigma", prior = T, dots_prior = list(col = "grey")) + }) +}) + +test_that("posterior plot model averaging based on complex bias mixture model (PET + PEESE + weightfunction)", { + + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + fit1 <- readRDS(file.path(temp_fits_dir, "fit_complex_bias.RDS")) + + mixed_posteriors <- as_mixed_posteriors( + mode = fit1, + parameters = names(attr(fit1, "prior_list")) + ) + + vdiffr::expect_doppelganger("model-averaging-plot-complex-bias-posterior-mu", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors, "mu", prior = TRUE, dots_prior = list(col = "grey")) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-complex-bias-posterior-bias-PET", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors, "PET", prior = TRUE, dots_prior = list(col = "grey"), individual = T) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-complex-bias-posterior-bias-PEESE", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors, "PEESE", prior = TRUE, dots_prior = list(col = "grey"), individual = T) + }) + vdiffr::expect_doppelganger("model-averaging-plot-complex-bias-posterior-bias-PETPEESE", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors, "PETPEESE", prior = TRUE, dots_prior = list(col = "grey"), ylim = c(-1, 3)) + }) + + + mixed_posteriors_conditional1 <- as_mixed_posteriors( + mode = fit1, + parameters = "bias", + conditional = "PET", + force_plots = TRUE + ) + + mixed_posteriors_conditional2 <- as_mixed_posteriors( + mode = fit1, + parameters = "bias", + conditional = "PEESE", + force_plots = TRUE + ) + + mixed_posteriors_conditional3 <- as_mixed_posteriors( + mode = fit1, + parameters = "bias", + conditional = "PETPEESE", + force_plots = TRUE + ) + + mixed_posteriors_conditional4 <- as_mixed_posteriors( + mode = fit1, + parameters = "bias", + conditional = "omega", + force_plots = TRUE + ) + + vdiffr::expect_doppelganger("model-averaging-plot-complex-bias-conditional-posterior-PET", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors_conditional1, "PET", prior = TRUE, dots_prior = list(col = "grey"), individual = T) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-complex-bias-conditional-posterior-PEESE", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors_conditional2, "PEESE", prior = TRUE, dots_prior = list(col = "grey"), individual = T) + }) + +}) + +test_that("posterior plot based on as_mixed_posteriors (PET-PEESE) work", { + + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + fit0 <- readRDS(file.path(temp_fits_dir, "fit_pet.RDS")) + fit1 <- readRDS(file.path(temp_fits_dir, "fit_peese.RDS")) + + mixed_posteriors0 <- as_mixed_posteriors( + mode = fit0, + parameters = names(attr(fit0, "prior_list")), + force_plots = TRUE + ) + mixed_posteriors1 <- as_mixed_posteriors( + mode = fit1, + parameters = names(attr(fit1, "prior_list")), + force_plots = TRUE + ) + + vdiffr::expect_doppelganger("model-averaging-plot-simple-posterior-PET-ind-no-prior", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors0, "PET", individual = T) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-simple-posterior-PET-no-prior", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors0, "PET") + }) + + vdiffr::expect_doppelganger("model-averaging-plot-simple-posterior-PET-ind", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors0, "PET", prior = TRUE, dots_prior = list(col = "grey"), individual = T) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-simple-posterior-PET", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors0, "PET", prior = TRUE, dots_prior = list(col = "orange")) + }) + + + vdiffr::expect_doppelganger("model-averaging-plot-simple-posterior-PEESE-ind-no-prior", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors1, "PEESE", individual = T) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-simple-posterior-PEESE-no-prior", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors1, "PEESE") + }) + + vdiffr::expect_doppelganger("model-averaging-plot-simple-posterior-PEESE-ind", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors1, "PEESE", prior = TRUE, dots_prior = list(col = "grey"), individual = T) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-simple-posterior-PEESE", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors1, "PEESE", prior = TRUE, col.fill = scales::alpha("grey", .50), dots_prior = list(col.fill = "orange")) + }) +}) + +test_that("posterior plot based on as_mixed_posteriors (weightfunction) work", { + + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + fit1 <- readRDS(file.path(temp_fits_dir, "fit_wf_twosided.RDS")) + fit2 <- readRDS(file.path(temp_fits_dir, "fit_wf_onesided.RDS")) + fitmix <- readRDS(file.path(temp_fits_dir, "fit_complex_mixed.RDS")) + + mixed_posteriors1 <- as_mixed_posteriors( + mode = fit1, + parameters = names(attr(fit1, "prior_list")), + force_plots = TRUE + ) + mixed_posteriors2 <- as_mixed_posteriors( + mode = fit2, + parameters = names(attr(fit2, "prior_list")), + force_plots = TRUE + ) + mixed_posteriorsmix <- as_mixed_posteriors( + mode = fitmix, + parameters = names(attr(fitmix, "prior_list")), + force_plots = TRUE + ) + + vdiffr::expect_doppelganger("model-averaging-plot-simple-posterior-omega1-ind-no-prior", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4), mfrow = c(1, 2)) + plot_posterior(mixed_posteriors1, "omega", individual = T, show_figures = NULL) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-simple-posterior-omega1-no-prior", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors1, "omega") + }) + + vdiffr::expect_doppelganger("model-averaging-plot-simple-posterior-omega1-ind", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4), mfrow = c(1, 2)) + plot_posterior(mixed_posteriors1, "omega", prior = TRUE, dots_prior = list(col = "grey"), individual = T, show_figures = NULL) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-simple-posterior-omega1", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors1, "omega", prior = TRUE, dots_prior = list(col = "orange")) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-simple-posterior-omega2-ind-no-prior", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4), mfrow = c(1, 2)) + plot_posterior(mixed_posteriors2, "omega", individual = T, show_figures = NULL) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-simple-posterior-omega2-no-prior", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors2, "omega") + }) + + vdiffr::expect_doppelganger("model-averaging-plot-simple-posterior-omega2-ind", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4), mfrow = c(1, 2)) + plot_posterior(mixed_posteriors2, "omega", prior = TRUE, dots_prior = list(col = "grey"), individual = T, show_figures = NULL) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-simple-posterior-omega2", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriors2, "omega", prior = TRUE, dots_prior = list(col = "orange")) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-simple-posterior-omegamix-ind-no-prior", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4), mfrow = c(1, 4)) + plot_posterior(mixed_posteriorsmix, "omega", individual = T, show_figures = NULL) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-simple-posterior-omegamix-no-prior", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriorsmix, "omega") + }) + + vdiffr::expect_doppelganger("model-averaging-plot-simple-posterior-omegamix-ind", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4), mfrow = c(1, 4)) + plot_posterior(mixed_posteriorsmix, "omega", prior = TRUE, dots_prior = list(col = "grey"), individual = T, show_figures = NULL) + }) + + vdiffr::expect_doppelganger("model-averaging-plot-simple-posterior-omegamix", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_posterior(mixed_posteriorsmix, "omega", prior = TRUE, dots_prior = list(col = "orange")) + }) +}) + diff --git a/tests/testthat/test-JAGS-ensemble-tables.R b/tests/testthat/test-JAGS-ensemble-tables.R new file mode 100644 index 00000000..b76d5382 --- /dev/null +++ b/tests/testthat/test-JAGS-ensemble-tables.R @@ -0,0 +1,407 @@ +# ============================================================================ # +# TEST FILE: JAGS Ensemble Tables Functions +# ============================================================================ # +# +# PURPOSE: +# Tests for ensemble table generation functions (ensemble_estimates_table, +# ensemble_inference_table, ensemble_diagnostics_table). +# +# DEPENDENCIES: +# - rjags, bridgesampling: For tests using pre-fitted models +# - common-functions.R: temp_fits_dir, skip_if_no_fits, test_reference_table +# +# SKIP CONDITIONS: +# - First section (empty tables): Can run on CRAN (pure R) +# - Second section (advanced features): skip_if_no_fits() +# +# MODELS/FIXTURES: +# - fit_summary0/1/2, fit_formula_interaction_mix/fac +# +# TAGS: @evaluation, @JAGS, @model-averaging, @tables +# ============================================================================ # + +REFERENCE_DIR <<- testthat::test_path("..", "results", "JAGS-ensemble-tables") +source(testthat::test_path("common-functions.R")) + +# ============================================================================ # +# SECTION 1: Test Empty Tables (Can run on CRAN - pure R) +# ============================================================================ # +test_that("Empty summary tables work correctly", { + + ensemble_estimates_empty <- ensemble_estimates_empty_table() + ensemble_inference_empty <- ensemble_inference_empty_table() + ensemble_diagnostics_empty <- ensemble_diagnostics_empty_table() + + expect_equal(nrow(ensemble_estimates_empty), 0, ignore_attr = TRUE) + expect_equal(nrow(ensemble_inference_empty), 0, ignore_attr = TRUE) + expect_equal(nrow(ensemble_diagnostics_empty), 0, ignore_attr = TRUE) + + # Test that empty tables have correct structure + expect_s3_class(ensemble_estimates_empty, "BayesTools_table") + expect_s3_class(ensemble_inference_empty, "BayesTools_table") + expect_s3_class(ensemble_diagnostics_empty, "BayesTools_table") + + test_reference_table(ensemble_estimates_empty, "empty_ensemble_estimates.txt", "Empty ensemble_estimates table mismatch") + test_reference_table(ensemble_inference_empty, "empty_ensemble_inference.txt", "Empty ensemble_inference table mismatch") + test_reference_table(ensemble_diagnostics_empty, "empty_ensemble_diagnostics.txt", "Empty ensemble_diagnostics table mismatch") +}) + +# ============================================================================ # +# SECTION 2: Test Advanced Features (Requires pre-fitted models) +# ============================================================================ # +test_that("Summary table advanced features work correctly", { + + skip_if_no_fits() + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + # 1. Simple models (m, omega) + # -------------------------------------------------------------- # + fit_summary0 <- readRDS(file.path(temp_fits_dir, "fit_summary0.RDS")) + marglik_summary0 <- readRDS(file.path(temp_marglik_dir, "fit_summary0.RDS")) + + fit_summary1 <- readRDS(file.path(temp_fits_dir, "fit_summary1.RDS")) + marglik_summary1 <- readRDS(file.path(temp_marglik_dir, "fit_summary1.RDS")) + + fit_summary2 <- readRDS(file.path(temp_fits_dir, "fit_summary2.RDS")) + marglik_summary2 <- readRDS(file.path(temp_marglik_dir, "fit_summary2.RDS")) + + models <- list( + list(fit = fit_summary0, marglik = marglik_summary0, prior_weights = 1, fit_summary = runjags_estimates_table(fit_summary0)), + list(fit = fit_summary1, marglik = marglik_summary1, prior_weights = 1, fit_summary = runjags_estimates_table(fit_summary1)), + list(fit = fit_summary2, marglik = marglik_summary2, prior_weights = 1, fit_summary = runjags_estimates_table(fit_summary2)) + ) + models <- models_inference(models) + + # Create inference and mixed posteriors + inference <- ensemble_inference(model_list = models, parameters = c("m", "omega"), is_null_list = list("m" = c(F,F,F), "omega" = c(T,F,F)), conditional = FALSE) + mixed_posteriors <- mix_posteriors(model_list = models, parameters = c("m", "omega"), is_null_list = list("m" = c(F,F,F), "omega" = c(T,F,F)), seed = 1) + + # Test tables + estimates_table <- ensemble_estimates_table(mixed_posteriors, parameters = c("m", "omega"), probs = c(.025, 0.95)) + inference_table <- ensemble_inference_table(inference, names(inference)) + summary_table <- ensemble_summary_table(models, c("m", "omega")) + diagnostics_table <- ensemble_diagnostics_table(models, c("m", "omega")) + + # Check structure + expect_s3_class(estimates_table, "BayesTools_table") + expect_s3_class(inference_table, "BayesTools_table") + expect_s3_class(summary_table, "BayesTools_table") + expect_s3_class(diagnostics_table, "BayesTools_table") + + # Check content with reference files + test_reference_table(estimates_table, "simple_ensemble_estimates.txt") + test_reference_table(inference_table, "simple_ensemble_inference.txt") + test_reference_table(summary_table, "simple_ensemble_summary.txt") + test_reference_table(diagnostics_table, "simple_ensemble_diagnostics.txt") + + # Test remove_column on diagnostics table + diagnostics_table.trimmed <- remove_column(diagnostics_table, 2) + diagnostics_table.trimmed <- remove_column(diagnostics_table.trimmed, 2) + test_reference_table(diagnostics_table.trimmed, "simple_ensemble_diagnostics_trimmed.txt") + + # Test that trimmed diagnostics table matches empty table structure + ensemble_diagnostics_empty <- ensemble_diagnostics_empty_table() + expect_equal(colnames(ensemble_diagnostics_empty), colnames(diagnostics_table.trimmed)) + expect_equal(capture_output_lines(ensemble_diagnostics_empty, width = 150)[1], capture_output_lines(diagnostics_table.trimmed, width = 150)[1]) + + # # Test interpret + interpretation <- interpret(inference, mixed_posteriors, list( + list( + inference = "m", + samples = "m", + inference_name = "effect", + inference_BF_name = "BF_10", + samples_name = "y", + samples_units = NULL + ) + ), "Test") + + test_reference_text(interpretation, "simple_interpretation.txt") + + # Test interpret 2 (modified inference) + inference[["m"]][["BF"]] <- 1/5 + interpretation2 <- interpret(inference, mixed_posteriors, list( + list( + inference = "m", + samples = "m", + inference_name = "effect", + inference_BF_name = "BF_10", + samples_name = "y", + samples_units = "mm", + samples_conditional = TRUE + ), + list( + inference = "omega", + inference_name = "bias", + inference_BF_name = "BF_pb" + ) + ), "Test2") + + test_reference_text(interpretation2, "simple_interpretation2.txt") + + + # 2. Complex models (Formula) + # -------------------------------------------------------------- # + fit_formula_simple <- readRDS(file.path(temp_fits_dir, "fit_formula_simple.RDS")) + marglik_formula_simple <- readRDS(file.path(temp_marglik_dir, "fit_formula_simple.RDS")) + + fit_formula_treatment <- readRDS(file.path(temp_fits_dir, "fit_formula_treatment.RDS")) + marglik_formula_treatment <- readRDS(file.path(temp_marglik_dir, "fit_formula_treatment.RDS")) + + fit_formula_orthonormal <- readRDS(file.path(temp_fits_dir, "fit_formula_orthonormal.RDS")) + marglik_formula_orthonormal <- readRDS(file.path(temp_marglik_dir, "fit_formula_orthonormal.RDS")) + + models_complex <- list( + list(fit = fit_formula_simple, marglik = marglik_formula_simple, prior_weights = 1, fit_summary = runjags_estimates_table(fit_formula_simple)), + list(fit = fit_formula_treatment, marglik = marglik_formula_treatment, prior_weights = 1, fit_summary = runjags_estimates_table(fit_formula_treatment)), + list(fit = fit_formula_orthonormal, marglik = marglik_formula_orthonormal, prior_weights = 1, fit_summary = runjags_estimates_table(fit_formula_orthonormal)) + ) + models_complex <- models_inference(models_complex) + + parameters_complex <- c("mu_x_cont1", "mu_x_fac2t", "mu_x_fac3o") + is_null_list_complex <- list( + "mu_x_cont1" = c(FALSE, FALSE, FALSE), + "mu_x_fac2t" = c(TRUE, FALSE, TRUE), + "mu_x_fac3o" = c(TRUE, TRUE, FALSE) + ) + + inference_complex <- ensemble_inference( + model_list = models_complex, + parameters = parameters_complex, + is_null_list = is_null_list_complex, + conditional = FALSE + ) + + mixed_posteriors_complex <- mix_posteriors( + model_list = models_complex, + parameters = parameters_complex, + is_null_list = is_null_list_complex, + seed = 1, n_samples = 10000 + ) + + # Tables + estimates_table_complex <- ensemble_estimates_table(mixed_posteriors_complex, parameters = parameters_complex, probs = c(.025, 0.95)) + inference_table_complex <- ensemble_inference_table(inference_complex, names(inference_complex)) + summary_table_complex <- ensemble_summary_table(models_complex, parameters_complex) + diagnostics_table_complex <- ensemble_diagnostics_table(models_complex, parameters_complex) + + test_reference_table(estimates_table_complex, "complex_ensemble_estimates.txt") + test_reference_table(inference_table_complex, "complex_ensemble_inference.txt") + test_reference_table(summary_table_complex, "complex_ensemble_summary.txt") + test_reference_table(diagnostics_table_complex, "complex_ensemble_diagnostics.txt") + + # 3. Simple Spike vs Normal (Model Averaging) + # -------------------------------------------------------------- # + fit_simple_spike <- readRDS(file.path(temp_fits_dir, "fit_simple_spike.RDS")) + marglik_simple_spike <- readRDS(file.path(temp_marglik_dir, "fit_simple_spike.RDS")) + + fit_simple_normal <- readRDS(file.path(temp_fits_dir, "fit_simple_normal.RDS")) + marglik_simple_normal <- readRDS(file.path(temp_marglik_dir, "fit_simple_normal.RDS")) + + models_simple_ma <- list( + list(fit = fit_simple_spike, marglik = marglik_simple_spike, prior_weights = 1, fit_summary = runjags_estimates_table(fit_simple_spike)), + list(fit = fit_simple_normal, marglik = marglik_simple_normal, prior_weights = 1, fit_summary = runjags_estimates_table(fit_simple_normal)) + ) + models_simple_ma <- models_inference(models_simple_ma) + + inference_simple_ma <- ensemble_inference( + model_list = models_simple_ma, + parameters = c("m", "s"), + is_null_list = list("m" = 1, "s" = 0), # m is spike in model 1 (null), s is never null + conditional = FALSE + ) + + mixed_posteriors_simple_ma <- mix_posteriors( + model_list = models_simple_ma, + parameters = c("m", "s"), + is_null_list = list("m" = 1, "s" = 0), + seed = 1 + ) + + estimates_simple_ma <- ensemble_estimates_table(mixed_posteriors_simple_ma, parameters = c("m", "s")) + inference_simple_ma_table <- ensemble_inference_table(inference_simple_ma, names(inference_simple_ma)) + + test_reference_table(estimates_simple_ma, "simple_ma_estimates.txt") + test_reference_table(inference_simple_ma_table, "simple_ma_inference.txt") + + + # 4. Fixed Weightfunctions + # -------------------------------------------------------------- # + # Re-using summary models 0-2 and adding a fixed weightfunction model + fit_summary3 <- readRDS(file.path(temp_fits_dir, "fit_summary3.RDS")) + marglik_summary3 <- readRDS(file.path(temp_marglik_dir, "fit_summary3.RDS")) + + models_fixed_wf <- list( + list(fit = fit_summary0, marglik = marglik_summary0, prior_weights = 1, fit_summary = runjags_estimates_table(fit_summary0)), + list(fit = fit_summary1, marglik = marglik_summary1, prior_weights = 1, fit_summary = runjags_estimates_table(fit_summary1)), + list(fit = fit_summary2, marglik = marglik_summary2, prior_weights = 1, fit_summary = runjags_estimates_table(fit_summary2)), + list(fit = fit_summary3, marglik = marglik_summary3, prior_weights = 1, fit_summary = runjags_estimates_table(fit_summary3)) + ) + models_fixed_wf <- models_inference(models_fixed_wf) + + inference_fixed_wf <- ensemble_inference( + model_list = models_fixed_wf, + parameters = c("m", "omega"), + is_null_list = list("m" = 0, "omega" = 1), + conditional = FALSE + ) + + mixed_posteriors_fixed_wf <- mix_posteriors( + model_list = models_fixed_wf, + parameters = c("m", "omega"), + is_null_list = list("m" = 0, "omega" = 1), + seed = 1 + ) + + estimates_fixed_wf <- ensemble_estimates_table(mixed_posteriors_fixed_wf, parameters = c("m", "omega")) + inference_fixed_wf_table <- ensemble_inference_table(inference_fixed_wf, names(inference_fixed_wf)) + + test_reference_table(estimates_fixed_wf, "fixed_wf_estimates.txt") + test_reference_table(inference_fixed_wf_table, "fixed_wf_inference.txt") + + # 5. Interactions + # -------------------------------------------------------------- # + fit_formula_interaction_mix <- readRDS(file.path(temp_fits_dir, "fit_formula_interaction_mix.RDS")) + marglik_formula_interaction_mix <- structure(list(logml = -20), class = "bridge") + + fit_formula_interaction_mix_main <- readRDS(file.path(temp_fits_dir, "fit_formula_interaction_mix_main.RDS")) + marglik_formula_interaction_mix_main <- structure(list(logml = -22), class = "bridge") + + models_interaction <- list( + list(fit = fit_formula_interaction_mix_main, marglik = marglik_formula_interaction_mix_main, prior_weights = 1, fit_summary = runjags_estimates_table(fit_formula_interaction_mix_main)), + list(fit = fit_formula_interaction_mix, marglik = marglik_formula_interaction_mix, prior_weights = 1, fit_summary = runjags_estimates_table(fit_formula_interaction_mix)) + ) + models_interaction <- models_inference(models_interaction) + + parameters_int <- c("mu_x_cont1", "mu_x_fac3o", "mu_x_cont1__xXx__x_fac3o") + is_null_list_int <- list( + "mu_x_cont1" = c(FALSE, FALSE), + "mu_x_fac3o" = c(FALSE, FALSE), + "mu_x_cont1:x_fac3o" = c(TRUE, FALSE) + ) + + inference_interaction <- ensemble_inference( + model_list = models_interaction, + parameters = parameters_int, + is_null_list = is_null_list_int, + conditional = FALSE + ) + + mixed_posteriors_interaction <- mix_posteriors( + model_list = models_interaction, + parameters = parameters_int, + is_null_list = is_null_list_int, + seed = 1 + ) + + estimates_interaction <- ensemble_estimates_table(mixed_posteriors_interaction, parameters = parameters_int) + inference_interaction_table <- ensemble_inference_table(inference_interaction, names(inference_interaction)) + summary_interaction_table <- ensemble_summary_table(models_interaction, parameters_int) + + test_reference_table(estimates_interaction, "interaction_ensemble_estimates.txt") + test_reference_table(inference_interaction_table, "interaction_ensemble_inference.txt") + test_reference_table(summary_interaction_table, "interaction_ensemble_summary.txt") + + # 6. Spike Factors (using marginal distribution models) + # -------------------------------------------------------------- # + # Using fit_marginal_0 (spike) and fit_marginal_1 (normal) which have meandif factors + fit_spike_factors_null <- readRDS(file.path(temp_fits_dir, "fit_marginal_0.RDS")) + marglik_spike_factors_null <- readRDS(file.path(temp_marglik_dir, "fit_marginal_0.RDS")) + + fit_spike_factors_alt <- readRDS(file.path(temp_fits_dir, "fit_marginal_1.RDS")) + marglik_spike_factors_alt <- readRDS(file.path(temp_marglik_dir, "fit_marginal_1.RDS")) + + models_spike_factors <- list( + list(fit = fit_spike_factors_null, marglik = marglik_spike_factors_null, prior_weights = 1, fit_summary = runjags_estimates_table(fit_spike_factors_null)), + list(fit = fit_spike_factors_alt, marglik = marglik_spike_factors_alt, prior_weights = 1, fit_summary = runjags_estimates_table(fit_spike_factors_alt)) + ) + models_spike_factors <- models_inference(models_spike_factors) + + inference_spike_factors <- ensemble_inference( + model_list = models_spike_factors, + parameters = c("mu_x_fac3md"), + is_null_list = list("mu_x_fac3md" = c(TRUE, FALSE)), + conditional = FALSE + ) + + mixed_posteriors_spike_factors <- mix_posteriors( + model_list = models_spike_factors, + parameters = c("mu_x_fac3md"), + is_null_list = list("mu_x_fac3md" = c(TRUE, FALSE)), + seed = 1 + ) + + estimates_spike_factors <- ensemble_estimates_table(mixed_posteriors_spike_factors, parameters = c("mu_x_fac3md")) + inference_spike_factors_table <- ensemble_inference_table(inference_spike_factors, names(inference_spike_factors)) + + test_reference_table(estimates_spike_factors, "spike_factors_estimates.txt") + test_reference_table(inference_spike_factors_table, "spike_factors_inference.txt") + +}) + + +test_that("Simplified interpret2 function", { + + set.seed(1) + information <- list( + list( + inference_name = "Effect", + inference_BF_name = "BF10", + inference_BF = 3.5, + estimate_name = "mu", + estimate_samples = rnorm(1000, 0.3, 0.15), + estimate_units = "kg", + estimate_conditional = FALSE + ) + ) + + expect_equal( + interpret2(information, "RoBMA"), + "RoBMA found moderate evidence in favor of the Effect, BF10 = 3.50, with mean model-averaged estimate mu = 0.298 kg, 95% CI [-0.020, 0.601]." + ) + +}) + +test_that("as_mixed_posteriors works with ensemble tables", { + + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + # 1. Complex Mixed Model + fit_complex_mixed <- readRDS(file.path(temp_fits_dir, "fit_complex_mixed.RDS")) + + mixed_posteriors_complex <- as_mixed_posteriors( + mode = fit_complex_mixed, + parameters = names(attr(fit_complex_mixed, "prior_list")) + ) + + # Generate estimates table + estimates_table_complex <- ensemble_estimates_table( + mixed_posteriors_complex, + parameters = names(attr(fit_complex_mixed, "prior_list")), + probs = c(.025, 0.95) + ) + + test_reference_table(estimates_table_complex, "as_mixed_posteriors_complex_estimates.txt") + + + # 2. Simple Formula Mixed Model + fit_simple_formula_mixed <- readRDS(file.path(temp_fits_dir, "fit_simple_formula_mixed.RDS")) + + mixed_posteriors_simple <- as_mixed_posteriors( + mode = fit_simple_formula_mixed, + parameters = names(attr(fit_simple_formula_mixed, "prior_list")) + ) + + # Generate estimates table + estimates_table_simple <- ensemble_estimates_table( + mixed_posteriors_simple, + parameters = names(attr(fit_simple_formula_mixed, "prior_list")), + probs = c(.025, 0.95) + ) + + test_reference_table(estimates_table_simple, "as_mixed_posteriors_simple_estimates.txt") + +}) + diff --git a/tests/testthat/test-JAGS-fit-edge-cases.R b/tests/testthat/test-JAGS-fit-edge-cases.R new file mode 100644 index 00000000..dc8c3a20 --- /dev/null +++ b/tests/testthat/test-JAGS-fit-edge-cases.R @@ -0,0 +1,232 @@ +# ============================================================================ # +# TEST FILE: JAGS Fit Edge Cases +# ============================================================================ # +# +# PURPOSE: +# Edge case tests for JAGS fitting functions including input validation, +# error handling, and boundary conditions. +# +# DEPENDENCIES: +# - rjags: For JAGS model syntax generation and testing +# - common-functions.R: REFERENCE_DIR, test_reference_text, skip_if_no_fits +# +# SKIP CONDITIONS: +# - skip_if_not_installed("rjags"): For all tests +# +# MODELS/FIXTURES: +# - Some tests use pre-fitted models from test-00-model-fits.R +# +# TAGS: @edge-cases, @JAGS, @input-validation +# ============================================================================ # + +# Reference directory for text output comparisons +REFERENCE_DIR <<- testthat::test_path("..", "results", "JAGS-fit-edge-cases") + +source(testthat::test_path("common-functions.R")) + + +# ============================================================================ # +# SECTION 1: Input validation tests +# ============================================================================ # +test_that("JAGS_add_priors input validation works", { + + # Empty prior_list returns original syntax + expect_equal(JAGS_add_priors("model{}", list()), "model{}") + + # prior_list must be a list of priors + expect_error(JAGS_add_priors("model{}", list(x = 1)), "'prior_list' must be a list of priors.") + expect_error(JAGS_add_priors("model{}", prior("normal", list(0, 1))), "'prior_list' must be a list of priors.") + +}) + + +test_that("JAGS_get_inits input validation works", { + + # Empty prior_list returns empty list + expect_equal(JAGS_get_inits(list(), chains = 2, seed = 1), list()) + + # Input validation + expect_error(JAGS_get_inits(list(x = 1), chains = 2, seed = 1), "'prior_list' must be a list of priors.") + expect_error(JAGS_get_inits(prior("normal", list(0, 1)), chains = 2, seed = 1), "'prior_list' must be a list of priors.") + +}) + + +test_that("JAGS_to_monitor input validation works", { + + # Empty prior_list returns empty string + expect_equal(JAGS_to_monitor(list()), "") + + # Input validation + expect_error(JAGS_to_monitor(list(x = 1)), "'prior_list' must be a list of priors.") + expect_error(JAGS_to_monitor(prior("normal", list(0, 1))), "'prior_list' must be a list of priors.") + +}) + + +test_that(".check_JAGS_syntax validates syntax correctly", { + + # Test with valid syntax + expect_silent(JAGS_add_priors("model{}", list(mu = prior("normal", list(0, 1))))) + + # Test with missing "model" keyword + expect_error( + JAGS_add_priors("invalid{}", list(mu = prior("normal", list(0, 1)))), + "syntax must be a JAGS model syntax" + ) + + # Test with missing opening brace + expect_error( + JAGS_add_priors("model}", list(mu = prior("normal", list(0, 1)))), + "syntax must be a JAGS model syntax" + ) + + # Test with missing closing brace + expect_error( + JAGS_add_priors("model{", list(mu = prior("normal", list(0, 1)))), + "syntax must be a JAGS model syntax" + ) + + # Test with non-character input + expect_error( + JAGS_add_priors(123, list(mu = prior("normal", list(0, 1)))), + "must be a character" + ) + +}) + + +test_that("JAGS_extend error handling", { + + skip_if_not_installed("rjags") + skip_on_cran() + + # Test error when fit is not a BayesTools_fit + expect_error( + JAGS_extend(list(), autofit_control = list()), + "'fit' must be a 'BayesTools_fit'" + ) + +}) + + +# ============================================================================ # +# SECTION 2: Convergence edge cases +# ============================================================================ # +test_that("JAGS_check_convergence handles single chain (R-hat warning)", { + + skip_if_not_installed("rjags") + skip_on_cran() + + prior_list <- list(mu = prior("normal", list(0, 1))) + model_syntax <- JAGS_add_priors("model{}", prior_list) + + set.seed(1) + fit <- suppressWarnings(runjags::run.jags( + model = model_syntax, + monitor = "mu", + n.chains = 1, # Single chain - R-hat cannot be computed + adapt = 50, + burnin = 50, + sample = 100, + silent.jags = TRUE + )) + + # Should warn about single chain R-hat + expect_warning( + JAGS_check_convergence(fit, prior_list = prior_list, max_Rhat = 1.05), + "Only one chain was run" + ) + +}) + + +test_that("JAGS_check_convergence handles ESS and error checks", { + + skip_if_not_installed("rjags") + skip_on_cran() + + prior_list <- list(mu = prior("normal", list(0, 1))) + model_syntax <- JAGS_add_priors("model{}", prior_list) + + set.seed(1) + fit <- suppressWarnings(runjags::run.jags( + model = model_syntax, + monitor = "mu", + n.chains = 2, + adapt = 50, + burnin = 50, + sample = 50, # Small sample for testing convergence failures + silent.jags = TRUE + )) + + # Test with very strict ESS requirement (should fail) + result_ess <- JAGS_check_convergence(fit, prior_list = prior_list, max_Rhat = NULL, min_ESS = 10000, max_error = NULL, max_SD_error = NULL, fail_fast = FALSE) + expect_false(result_ess) + expect_true(!is.null(attr(result_ess, "errors"))) + + # Test with very strict error requirement + result_err <- JAGS_check_convergence(fit, prior_list = prior_list, max_Rhat = NULL, min_ESS = NULL, max_error = 0.00001, max_SD_error = NULL, fail_fast = FALSE) + expect_false(result_err) + + # Test with very strict SD error requirement + result_sd <- JAGS_check_convergence(fit, prior_list = prior_list, max_Rhat = NULL, min_ESS = NULL, max_error = NULL, max_SD_error = 0.00001, fail_fast = FALSE) + expect_false(result_sd) + +}) + + +# ============================================================================ # +# SECTION 3: JAGS_fit with is_JASP mode +# ============================================================================ # +test_that("JAGS_fit works with is_JASP mode", { + + skip_if_not_installed("rjags") + skip_on_cran() + + # Simple model for testing is_JASP mode + set.seed(1) + data <- list( + y = rnorm(20, 0.5, 1), + N = 20 + ) + + prior_list <- list( + mu = prior("normal", list(0, 1)), + sigma = prior("normal", list(0, 1), list(0, Inf)) + ) + + model_syntax <- "model{ + for(i in 1:N){ + y[i] ~ dnorm(mu, 1/pow(sigma, 2)) + } + }" + + # Mock JASP progress bar functions (they should be skipped if not available) + # The is_JASP mode should work but simply skip progress bars if functions don't exist + fit_jasp <- capture.output(tryCatch({ + suppressWarnings(JAGS_fit( + model_syntax = model_syntax, + data = data, + prior_list = prior_list, + chains = 1, + adapt = 50, + burnin = 50, + sample = 100, + seed = 1, + silent = TRUE, + is_JASP = TRUE, + is_JASP_prefix = "Test" + )) + }, error = function(e) { + # If JASP functions don't exist, this should still produce a fit + # or fail gracefully + if (grepl("JASP", e$message)) { + skip("JASP progress bar functions not available") + } + stop(e) + })) + + test_reference_text(paste0(fit_jasp, collapse = ","), "fit_jasp.txt") + +}) diff --git a/tests/testthat/test-JAGS-fit.R b/tests/testthat/test-JAGS-fit.R index ab65da4c..deadc98f 100644 --- a/tests/testthat/test-JAGS-fit.R +++ b/tests/testthat/test-JAGS-fit.R @@ -1,1490 +1,573 @@ -context("JAGS fit functions") - -test_that("JAGS model functions work (simple)", { +# ============================================================================ # +# TEST FILE: JAGS Fit Functions +# ============================================================================ # +# +# PURPOSE: +# Tests for JAGS fitting functions including JAGS_add_priors, JAGS_get_inits, +# JAGS_to_monitor, JAGS_check_convergence, JAGS_extend, and related utilities. +# +# DEPENDENCIES: +# - rjags: For JAGS model syntax generation and testing +# - common-functions.R: REFERENCE_DIR, test_reference_text, skip_if_no_fits +# +# SKIP CONDITIONS: +# - skip_if_not_installed("rjags"): For all tests +# - skip_if_no_fits(): For tests using pre-fitted models +# +# MODELS/FIXTURES: +# - Some tests use pre-fitted models from test-00-model-fits.R +# +# TAGS: @evaluation, @JAGS +# ============================================================================ # + +# Reference directory for text output comparisons +REFERENCE_DIR <<- testthat::test_path("..", "results", "JAGS-fit") + +source(testthat::test_path("common-functions.R")) + + +# ============================================================================ # +# SECTION 1: JAGS_add_priors tests +# ============================================================================ # +test_that("JAGS_add_priors handles various prior types", { skip_if_not_installed("rjags") - model_syntax <- "model{}" - priors <- list( - p1 = prior("normal", list(0, 1)), - p2 = prior("normal", list(0, 1), list(1, Inf)), - p3 = prior("lognormal", list(0, .5)), - p4 = prior("t", list(0, .5, 5)), - p5 = prior("Cauchy", list(1, 0.1), list(-10, 0)), - p6 = prior("gamma", list(2, 1)), - p7 = prior("invgamma", list(3, 2), list(1, 3)), - p8 = prior("exp", list(1.5)), - p9 = prior("beta", list(3, 2)), - p10 = prior("uniform", list(1, 5)), - p11 = prior("point", list(1)), - PET = prior_PET("normal", list(0, 1)), - PEESE = prior_PEESE("gamma", list(1, 1)), - p12 = prior("bernoulli", list(0.75)) - ) - - model_syntax <- JAGS_add_priors(model_syntax, priors) - monitor <- JAGS_to_monitor(priors) - inits <- JAGS_get_inits(priors, chains = 2, seed = 1) - - set.seed(1) - model <- rjags::jags.model(file = textConnection(model_syntax), inits = inits, n.chains = 2, quiet = TRUE) - samples <- rjags::coda.samples(model = model, variable.names = monitor, n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples <- do.call(rbind, samples) - - - for(i in seq_along(priors)){ - vdiffr::expect_doppelganger(paste0("JAGS-model-prior-",i), function(){ - if(is.prior.discrete(priors[[i]])){ - barplot(table(samples[,names(priors)[i]])/length(samples[,names(priors)[i]]), main = print(priors[[i]], plot = T), width = 1/(max(samples[,names(priors)[i]])+1), space = 0, xlim = c(-0.25, max(samples[,names(priors)[i]])+0.25)) - }else{ - hist(samples[,names(priors)[i]], breaks = 50, main = print(priors[[i]], plot = TRUE), freq = FALSE) - } - lines(priors[[i]], individual = TRUE) - }) - } -}) -# skip the rest as it takes too long -skip_on_cran() - -test_that("JAGS model functions work (vector)", { - - skip_if_not_installed("rjags") - model_syntax <- "model{}" - priors <- list( - p1 = prior("mnormal", list(mean = 0, sd = 1, K = 3),), - p2 = prior("mcauchy", list(location = 0, scale = 1.5, K = 2)), - p3 = prior("mt", list(location = 2, scale = 0.5, df = 5, K = 2)) + # Test with simple priors + syntax_simple <- "model{}" + priors_simple <- list( + mu = prior("normal", list(0, 1)), + sigma = prior("gamma", list(2, 1)) ) + result_simple <- JAGS_add_priors(syntax_simple, priors_simple) + test_reference_text(result_simple, "JAGS_add_priors_simple.txt") - model_syntax <- JAGS_add_priors(model_syntax, priors) - monitor <- JAGS_to_monitor(priors) - inits <- JAGS_get_inits(priors, chains = 2, seed = 1) - - set.seed(1) - model <- rjags::jags.model(file = textConnection(model_syntax), inits = inits, n.chains = 2, quiet = TRUE) - samples <- rjags::coda.samples(model = model, variable.names = monitor, n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples <- do.call(rbind, samples) - - vdiffr::expect_doppelganger("JAGS-model-prior-vector-1", function(){ - - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfcol = c(1, 2)) - - hist(samples[,"p1[1]"], breaks = 50, main = print(priors[[1]], plot = TRUE), freq = FALSE) - lines(prior("normal", list(0, 1))) - - plot(samples[,"p1[1]"], samples[,"p1[2]"], pch = 19, xlim = c(-3, 3), ylim = c(-3, 3), asp = 1, - xlab = "X1", ylab = "X2", main = print(priors[[1]], plot = TRUE)) - }) - - vdiffr::expect_doppelganger("JAGS-model-prior-vector-2", function(){ - - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfcol = c(1, 2)) - - hist(samples[,"p2[1]"][abs(samples[,"p2[1]"]) < 5], breaks = 20, main = print(priors[[2]], plot = TRUE), freq = FALSE) - lines(prior("cauchy", list(0, 1.5))) - - plot(samples[,"p2[1]"], samples[,"p2[2]"], pch = 19, xlim = c(-5, 5), ylim = c(-5, 5), asp = 1, - xlab = "X1", ylab = "X2", main = print(priors[[2]], plot = TRUE)) - }) - - vdiffr::expect_doppelganger("JAGS-model-prior-vector-3", function(){ - - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfcol = c(1, 2)) - - hist(samples[,"p3[1]"], breaks = 50, main = print(priors[[3]], plot = TRUE), freq = FALSE) - lines(prior("t", list(2, 0.5, 5))) - - plot(samples[,"p3[1]"], samples[,"p3[2]"], pch = 19, xlim = c(-3, 7), ylim = c(-3, 7), asp = 1, - xlab = "X1", ylab = "X2", main = print(priors[[3]], plot = TRUE)) - }) - -}) - -test_that("JAGS model functions work (factor)", { - - skip_if_not_installed("rjags") - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes - - model_syntax <- "model{}" - priors <- list( - p1 = prior_factor("mnorm", list(mean = 0, sd = 1), contrast = "orthonormal"), - p2 = prior_factor("beta", list(alpha = 1, beta = 1), contrast = "treatment"), - p3 = prior_factor("beta", list(alpha = 2, beta = 2), contrast = "treatment"), - p4 = prior_factor("gamma", list(shape = 2, rate = 3), contrast = "independent"), - p5 = prior_factor("uniform", list(a = -0.5, b = 1.5), contrast = "independent"), - p6 = prior_factor("mnorm", list(mean = 0, sd = 0.5), contrast = "meandif"), - p7 = prior_factor("spike", list(location = 1), contrast = "treatment"), - p8 = prior_factor("spike", list(location = 2), contrast = "independent"), - p9 = prior_factor("spike", list(location = 0), contrast = "orthonormal"), - p10 = prior_factor("spike", list(location = 0), contrast = "meandif") + # Test with truncated priors + priors_truncated <- list( + mu = prior("normal", list(0, 1), list(0, Inf)) ) - # add levels - attr(priors[[1]], "levels") <- 3 - attr(priors[[2]], "levels") <- 2 - attr(priors[[3]], "levels") <- 3 - attr(priors[[4]], "levels") <- 1 - attr(priors[[5]], "levels") <- 3 - attr(priors[[6]], "levels") <- 3 - attr(priors[[7]], "levels") <- 2 - attr(priors[[8]], "levels") <- 3 - attr(priors[[9]], "levels") <- 3 - attr(priors[[10]], "levels") <- 3 - - - model_syntax <- JAGS_add_priors(model_syntax, priors) - monitor <- JAGS_to_monitor(priors) - inits <- JAGS_get_inits(priors, chains = 2, seed = 1) - - set.seed(1) - model <- rjags::jags.model(file = textConnection(model_syntax), inits = inits, n.chains = 2, quiet = TRUE) - samples <- rjags::coda.samples(model = model, variable.names = monitor, n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples <- do.call(rbind, samples) - - expect_equal(colnames(samples), c("p1[1]", "p1[2]", "p10[1]", "p10[2]", "p2", "p3[1]", "p3[2]", "p4", "p5[1]", "p5[2]", "p5[3]", "p6[1]", "p6[2]", "p7", "p8[1]", "p8[2]", "p8[3]", "p9[1]", "p9[2]")) - - vdiffr::expect_doppelganger("JAGS-model-prior-factor-1", function(){ + result_truncated <- JAGS_add_priors(syntax_simple, priors_truncated) + test_reference_text(result_truncated, "JAGS_add_priors_truncated.txt") - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfcol = c(1, 2)) - - hist(samples[,"p1[1]"], breaks = 50, main = print(priors[[1]], plot = TRUE), freq = FALSE) - lines(prior("normal", list(0, 1))) - - plot(samples[,"p1[1]"], samples[,"p1[2]"], pch = 19, xlim = c(-3, 3), ylim = c(-3, 3), asp = 1, - xlab = "X1", ylab = "X2", main = print(priors[[1]], plot = TRUE)) - }) - - vdiffr::expect_doppelganger("JAGS-model-prior-factor-2", function(){ - - hist(samples[,"p2"], breaks = 20, main = print(priors[[2]], plot = TRUE), freq = FALSE) - lines(prior("beta", list(1, 1))) - }) - - vdiffr::expect_doppelganger("JAGS-model-prior-factor-3", function(){ - - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfcol = c(1, 2)) - - hist(samples[,"p3[1]"], breaks = 50, main = print(priors[[3]], plot = TRUE), freq = FALSE) - lines(prior("beta", list(2, 2))) - - plot(samples[,"p3[1]"], samples[,"p3[2]"], pch = 19, xlim = c(0, 1), ylim = c(0, 1), asp = 1, - xlab = "X1", ylab = "X2", main = print(priors[[3]], plot = TRUE), cex = .25) - }) - - vdiffr::expect_doppelganger("JAGS-model-prior-factor-4", function(){ - - hist(samples[,"p4"], breaks = 20, main = print(priors[[4]], plot = TRUE), freq = FALSE) - lines(prior("gamma", list(shape = 2, rate = 3))) - }) - - vdiffr::expect_doppelganger("JAGS-model-prior-factor-5", function(){ - - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfcol = c(1, 3)) - - hist(samples[,"p5[1]"], breaks = 50, main = print(priors[[5]], plot = TRUE), freq = FALSE) - lines(prior("uniform", list(a = -0.5, b = 1.5))) + # Test with point prior + priors_point <- list( + mu = prior("point", list(0)) + ) - hist(samples[,"p5[2]"], breaks = 50, main = print(priors[[5]], plot = TRUE), freq = FALSE) - lines(prior("uniform", list(a = -0.5, b = 1.5))) + result_point <- JAGS_add_priors(syntax_simple, priors_point) + test_reference_text(result_point, "JAGS_add_priors_point.txt") - hist(samples[,"p5[3]"], breaks = 50, main = print(priors[[5]], plot = TRUE), freq = FALSE) - lines(prior("uniform", list(a = -0.5, b = 1.5))) - }) + # Test with factor priors + priors_factor <- list( + p1 = prior_factor("mnorm", list(mean = 0, sd = 1), contrast = "orthonormal") + ) + attr(priors_factor[[1]], "levels") <- 3 - vdiffr::expect_doppelganger("JAGS-model-prior-factor-6", function(){ + result_factor <- JAGS_add_priors(syntax_simple, priors_factor) + test_reference_text(result_factor, "JAGS_add_priors_factor.txt") - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfcol = c(1, 2)) + # Test with weightfunction priors + priors_wf <- list( + omega = prior_weightfunction("one.sided", list(c(0.05), c(1, 1))) + ) - hist(samples[,"p6[1]"], breaks = 50, main = print(priors[[6]], plot = TRUE), freq = FALSE) - lines(prior("normal", list(mean = 0, sd = 0.5))) + result_wf <- JAGS_add_priors(syntax_simple, priors_wf) + test_reference_text(result_wf, "JAGS_add_priors_weightfunction.txt") - hist(samples[,"p6[2]"], breaks = 50, main = print(priors[[6]], plot = TRUE), freq = FALSE) - lines(prior("normal", list(mean = 0, sd = 0.5))) - }) +}) - vdiffr::expect_doppelganger("JAGS-model-prior-factor-7", function(){ - hist(samples[,"p7"], breaks = 50, main = print(priors[[7]], plot = TRUE), freq = FALSE) - lines(prior_factor("spike", list(location = 1), contrast = "treatment")) +test_that("JAGS_add_priors handles spike_and_slab priors", { - }) + skip_if_not_installed("rjags") - vdiffr::expect_doppelganger("JAGS-model-prior-factor-8", function(){ + priors_sas <- list( + mu = prior_spike_and_slab( + prior("normal", list(0, 1)), + prior_inclusion = prior("beta", list(1, 1)) + ) + ) - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfcol = c(1, 3)) + result <- JAGS_add_priors("model{}", priors_sas) + expect_true(grepl("mu_variable", result)) + expect_true(grepl("mu_inclusion", result)) + expect_true(grepl("mu_indicator", result)) - hist(samples[,"p8[1]"], breaks = 50, main = print(priors[[5]], plot = TRUE), freq = FALSE) - lines(prior_factor("spike", list(location = 2), contrast = "independent")) + # Test inits + inits <- JAGS_get_inits(priors_sas, chains = 2, seed = 1) + expect_true("mu_variable" %in% names(inits[[1]]) || "mu_inclusion" %in% names(inits[[1]])) - hist(samples[,"p8[2]"], breaks = 50, main = print(priors[[5]], plot = TRUE), freq = FALSE) - lines(prior_factor("spike", list(location = 2), contrast = "independent")) + # Test monitor + monitor <- JAGS_to_monitor(priors_sas) + expect_true("mu_indicator" %in% monitor) - hist(samples[,"p8[3]"], breaks = 50, main = print(priors[[5]], plot = TRUE), freq = FALSE) - lines(prior_factor("spike", list(location = 2), contrast = "independent")) - }) +}) - vdiffr::expect_doppelganger("JAGS-model-prior-factor-9", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfcol = c(1, 2)) +test_that("JAGS_add_priors handles standard prior_mixture (non-bias)", { - hist(samples[,"p9[1]"], breaks = 50, main = print(priors[[5]], plot = TRUE), freq = FALSE) - lines(prior_factor("spike", list(location = 0), contrast = "orthonormal")) + skip_if_not_installed("rjags") - hist(samples[,"p9[2]"], breaks = 50, main = print(priors[[5]], plot = TRUE), freq = FALSE) - lines(prior_factor("spike", list(location = 0), contrast = "orthonormal")) - }) + # Standard mixture (not bias mixture) + mix <- prior_mixture(list( + prior("normal", list(0, 0.5)), + prior("normal", list(0, 1)) + ), is_null = c(TRUE, FALSE)) - vdiffr::expect_doppelganger("JAGS-model-prior-factor-10", function(){ + priors_mix <- list(mu = mix) - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfcol = c(1, 2)) + result <- JAGS_add_priors("model{}", priors_mix) + expect_true(grepl("mu_indicator", result)) + expect_true(grepl("mu_component_1", result)) + expect_true(grepl("mu_component_2", result)) - hist(samples[,"p10[1]"], breaks = 50, main = print(priors[[5]], plot = TRUE), freq = FALSE) - lines(prior_factor("spike", list(location = 0), contrast = "meandif")) + # Test inits + inits <- JAGS_get_inits(priors_mix, chains = 2, seed = 1) + expect_true("mu_indicator" %in% names(inits[[1]])) - hist(samples[,"p10[2]"], breaks = 50, main = print(priors[[5]], plot = TRUE), freq = FALSE) - lines(prior_factor("spike", list(location = 0), contrast = "meandif")) - }) + # Test monitor + monitor <- JAGS_to_monitor(priors_mix) + expect_true("mu_indicator" %in% monitor) + expect_true("mu" %in% monitor) }) -test_that("JAGS model functions work (weightfunctions)", { - skip_if_not_installed("rjags") - priors <- list( - prior_weightfunction("one.sided", list(c(.05), c(1, 1))), - prior_weightfunction("one.sided", list(c(.05, 0.10), c(1, 2, 3))), - prior_weightfunction("one.sided", list(c(.05, 0.60), c(1, 1), c(1, 5))), - prior_weightfunction("two.sided", list(c(.05), c(1, 1))), - prior_weightfunction("one.sided.fixed", list(c(.05), c(1, .5))), - prior_weightfunction("two.sided.fixed", list(c(.05, 0.10), c(1, .2, .5))) - ) - - for(i in 1:length(priors)){ - model_syntax <- "model{}" - model_syntax <- JAGS_add_priors(model_syntax, priors[i]) - monitor <- JAGS_to_monitor(priors[i]) - inits <- JAGS_get_inits(priors[i], chains = 2, seed = 1) - - set.seed(1) - model <- rjags::jags.model(file = textConnection(model_syntax), inits = inits, n.chains = 2, quiet = TRUE) - samples <- rjags::coda.samples(model = model, variable.names = monitor, n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples <- do.call(rbind, samples) - - - vdiffr::expect_doppelganger(paste0("JAGS-model-weightfunction-",i), function(){ - densities <- density(priors[[i]], individual = TRUE) - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(1, length(densities))) - for(j in seq_along(densities)){ - hist(samples[,paste0("omega[",j,"]")], breaks = 50, freq = FALSE) - lines(densities[[j]]) - } - }) - } -}) - -test_that("JAGS model functions work (spike and slab)", { +test_that("JAGS_add_priors handles mixture with PEESE prior", { skip_if_not_installed("rjags") - priors <- list( - "mu" = prior_spike_and_slab(prior("normal", list(0, 1)), prior_inclusion = prior("beta", list(1,1))), - "beta" = prior_spike_and_slab(prior_factor("mnormal", list(0, 1), contrast = "orthonormal"), - prior_inclusion = prior("beta", list(1,1))) - ) - - # Set levels attribute on the variable component within the spike_and_slab mixture - components <- attr(priors$beta, "components") - alternative_idx <- which(components == "alternative") - attr(priors$beta[[alternative_idx]], "levels") <- 3 - - for(i in 1:length(priors)){ - model_syntax <- "model{}" - model_syntax <- JAGS_add_priors(model_syntax, priors[i]) - monitor <- JAGS_to_monitor(priors[i]) - inits <- JAGS_get_inits(priors[i], chains = 2, seed = 1) - - set.seed(1) - model <- rjags::jags.model(file = textConnection(model_syntax), inits = inits, n.chains = 2, quiet = TRUE) - samples <- rjags::coda.samples(model = model, variable.names = monitor, n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples <- do.call(rbind, samples) - - - if(i == 1){ - vdiffr::expect_doppelganger(paste0("JAGS-model-prior_spike-and-slab-",i), function(){ - temp_samples <- samples[,names(priors)[i]] - hs <- hist(temp_samples[temp_samples != 0], breaks = 50, plot = FALSE) - hs$density <- hs$density * mean(temp_samples != 0) - plot(hs, main = print(priors[[i]], plot = TRUE), freq = FALSE, ylim = range(c(0, max(hs$density), mean(temp_samples == 0)))) - lines(priors[[i]], individual = TRUE) - }) - }else{ - vdiffr::expect_doppelganger(paste0("JAGS-model-prior_spike-and-slab-",i), function(){ - - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(1, 3)) - - temp_samples <- samples[,paste0(names(priors)[i], "[", 1:2, "]")] %*% t(contr.orthonormal(1:3)) - - hs1 <- hist(temp_samples[temp_samples[,1] != 0, 1], breaks = 50, plot = FALSE) - hs1$density <- hs1$density * mean(temp_samples[,1] != 0) - plot(hs1, main = print(priors[[i]], plot = TRUE), freq = FALSE, ylim = range(c(0, max(hs1$density), mean(temp_samples == 0)))) - lines(priors[[i]], individual = TRUE) - - hs2 <- hist(temp_samples[temp_samples[,2] != 0, 2], breaks = 50, plot = FALSE) - hs2$density <- hs2$density * mean(temp_samples[,1] != 0) - plot(hs2, main = print(priors[[i]], plot = TRUE), freq = FALSE, ylim = range(c(0, max(hs2$density), mean(temp_samples == 0)))) - lines(priors[[i]], individual = TRUE) - - hs3 <- hist(temp_samples[temp_samples[,3] != 0, 3], breaks = 50, plot = FALSE) - hs3$density <- hs3$density * mean(temp_samples[,1] != 0) - plot(hs3, main = print(priors[[i]], plot = TRUE), freq = FALSE, ylim = range(c(0, max(hs3$density), mean(temp_samples == 0)))) - lines(priors[[i]], individual = TRUE) - }) - } - } -}) -test_that("JAGS model functions work (mixture)", { + # Create a bias mixture with PEESE prior + bias_mixture <- prior_mixture(list( + prior_none(prior_weights = 1), + prior_PEESE("normal", list(0, 1), prior_weights = 1) + )) - skip_if_not_installed("rjags") - priors <- list( - "mu" = prior_mixture( - list( - prior("normal", list(0, 1), prior_weights = 1), - prior("normal", list(-3, 1), prior_weights = 5), - prior("gamma", list(5, 10), prior_weights = 1) - ), - is_null = c(T, F, T) - ), - "beta" = prior_mixture( - list( - prior("normal", list(0, 1), prior_weights = 1), - prior("normal", list(-3, 1), prior_weights = 5) - ), - components = c("b", "a") - ), - "gamma" = prior_mixture( - list( - prior("spike", list(2)), - prior("normal", list(-3, 1)) - ) - ), - "bias" = prior_mixture(list( - prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), - prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.10)), prior_weights = 1/12), - prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12), - prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12), - prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.5)), prior_weights = 1/12), - prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)), prior_weights = 1/12), - prior_PET(distribution = "Cauchy", parameters = list(0,1), truncation = list(0, Inf), prior_weights = 1/4), - prior_PEESE(distribution = "Cauchy", parameters = list(0,5), truncation = list(0, Inf), prior_weights = 1/4) - )) + priors_peese <- list( + bias = bias_mixture ) + result_peese <- JAGS_add_priors("model{}", priors_peese) + test_reference_text(result_peese, "JAGS_add_priors_peese_mixture.txt") - - for(i in 1:length(priors)){ - model_syntax <- "model{}" - model_syntax <- JAGS_add_priors(model_syntax, priors[i]) - monitor <- JAGS_to_monitor(priors[i]) - inits <- JAGS_get_inits(priors[i], chains = 2, seed = 1) - - if(i == 4){ - if("RoBMA" %in% rownames(installed.packages())){ - require("RoBMA") - }else{ - next - } - } - - set.seed(1) - model <- rjags::jags.model(file = textConnection(model_syntax), inits = inits, n.chains = 2, quiet = TRUE) - samples <- rjags::coda.samples(model = model, variable.names = monitor, n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples <- do.call(rbind, samples) - - - if(i != 4){ - vdiffr::expect_doppelganger(paste0("JAGS-model-prior_mixture-",i), function(){ - temp_samples <- samples[,names(priors)[i]] - hist(temp_samples, breaks = 100, freq = FALSE, main = print(priors[[i]], plot = TRUE)) - lines(density(rng(priors[[i]], 1000000))) - }) - }else{ - vdiffr::expect_doppelganger(paste0("JAGS-model-prior_mixture-",i), function(){ - - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(3, 3)) - - samples_PET <- samples[,"PET"] - samples_PEESE <- samples[,"PEESE"] - samples_omega <- samples[,paste0("omega[",1:6,"]")] - samples_bias <- samples[,"bias_indicator"] - - barplot(table(samples_bias)/length(samples_bias), main = "Bias componenets") - - hist(samples_PET[samples_PET != 0 & samples_PET < 10], breaks = 50, main = "PET", freq = FALSE) - lines(priors$bias[[7]], individual = TRUE) - - hist(samples_PEESE[samples_PEESE != 0 & samples_PEESE < 20], breaks = 50, main = "PEESE", freq = FALSE) - lines(priors$bias[[8]], individual = TRUE) - - hist(samples_omega[samples_bias == 2, 1], breaks = 50, main = "omega[2:1]", freq = FALSE) - hist(samples_omega[samples_bias == 2, 2], breaks = 50, main = "omega[2:2]", freq = FALSE) - hist(samples_omega[samples_bias == 2, 3], breaks = 50, main = "omega[2:3]", freq = FALSE) - hist(samples_omega[samples_bias == 2, 4], breaks = 50, main = "omega[2:4]", freq = FALSE) - hist(samples_omega[samples_bias == 2, 5], breaks = 50, main = "omega[2:5]", freq = FALSE) - hist(samples_omega[samples_bias == 2, 6], breaks = 50, main = "omega[2:6]", freq = FALSE) - - }) - } - } }) -test_that("JAGS fit function works" , { - set.seed(1) - data <- list( - x = rnorm(50, 0, .5), - N = 50 - ) - priors_list <- list( - m = prior("normal", list(0, 1)), - s = prior("normal", list(0, 1), list(0, Inf)) - ) - model_syntax <- - "model - { - for(i in 1:N){ - x[i] ~ dnorm(m, pow(s, -2)) - } - }" - - ### checking the default settings - set.seed(1) - fit <- JAGS_fit(model_syntax, data, priors_list) - expect_equal(length(fit$mcmc), 4) - expect_true(all(sapply(fit$mcmc, function(mcmc)dim(mcmc) == c(4000, 2)))) - vdiffr::expect_doppelganger("JAGS-fit-posterior", function(){ - samples <- do.call(rbind, fit$mcmc) - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(1, 2)) - for(i in seq_along(priors_list)){ - hist(samples[,i], breaks = 50, freq = FALSE) - } - }) - - ### checking control the main control arguments - fit1 <- JAGS_fit(model_syntax, data, priors_list, chains = 1, adapt = 100, burnin = 150, sample = 175, thin = 3, seed = 2) - expect_equal(length(fit1$mcmc), 1) - expect_true(all(sapply(fit1$mcmc, function(mcmc)dim(mcmc) == c(175, 2)))) - expect_equal(fit1$burnin, 250) # adapt + burnin - expect_equal(fit1$sample, 175) - expect_equal(fit1$thin, 3) - - ### adding custom parameters - model_syntax3 <- - "model - { - g ~ dnorm(0, 1) - for(i in 1:N){ - x[i] ~ dnorm(m, pow(s, -2)) - } - }" - fit3 <- JAGS_fit(model_syntax3, data, priors_list, add_parameters = "g", - chains = 1, adapt = 100, burnin = 100, sample = 100, seed = 3) - expect_equal(colnames(fit3$mcmc[[1]]), c("m", "s", "g")) - - ### checking mcmc_error autofit - priors_list4 <- list( - m = prior("normal", list(0, 1)) - ) - data4 <- list( - x = c(-500), - N = 1 - ) - model_syntax4 <- - "model - { - l = 1 - for(i in 1:N){ - x[i] ~ dt(m, pow(.3, -2), 1) - } - }" - runjags::runjags.options(silent.jags = TRUE, silent.runjags = TRUE) - fit4 <- JAGS_fit(model_syntax4, data4, priors_list4, autofit = FALSE, - chains = 2, adapt = 100, burnin = 50, sample = 100, seed = 4) - summary_4 <- suppressWarnings(summary(fit4)) - expect_true(summary_4[1,"MCerr"] > 0.069) - expect_true(summary_4[1,"MC%ofSD"] > 8) - expect_true(summary_4[1,"SSeff"] < 150) - expect_true(summary_4[1,"psrf"] > 1.007) - - convergence <- JAGS_check_convergence(fit4, prior_list = priors_list4, max_Rhat = 1.001, min_ESS = 500, max_error = 0.01, max_SD_error = 0.05) - expect_true(!convergence) - expect_equal(attr(convergence, "errors")[1], "R-hat 1.053 is larger than the set target (1.001).") - expect_equal(attr(convergence, "errors")[2], "ESS 149 is lower than the set target (500).") - expect_equal(attr(convergence, "errors")[3], "MCMC error 0.07422 is larger than the set target (0.01).") - expect_equal(attr(convergence, "errors")[4], "MCMC SD error 0.087 is larger than the set target (0.05).") - - fit4b <- JAGS_fit(model_syntax4, data4, priors_list4, autofit = TRUE, autofit_control = list(max_error = 0.05, sample_extend = 100), - chains = 2, adapt = 100, burnin = 50, sample = 100, seed = 4) - summary_4b <- summary(fit4b) - expect_true(summary_4b[1,"MCerr"] < 0.05) - - fit4c <- JAGS_fit(model_syntax4, data4, priors_list4, autofit = TRUE, autofit_control = list(max_Rhat = 1.001, sample_extend = 100), - chains = 2, adapt = 100, burnin = 50, sample = 100, seed = 4) - summary_4c <- summary(fit4c) - expect_true(summary_4c[1,"psrf"] < 1.001) - - fit4d <- JAGS_fit(model_syntax4, data4, priors_list4, autofit = TRUE, autofit_control = list(min_ESS = 200, sample_extend = 100), - chains = 2, adapt = 100, burnin = 50, sample = 100, seed = 4) - summary_4d <- summary(fit4d) - expect_true(summary_4d[1,"SSeff"] > 200) - - fit4e <- JAGS_fit(model_syntax4, data4, priors_list4, autofit = TRUE, autofit_control = list(max_SD_error = 0.06, sample_extend = 100), - chains = 2, adapt = 100, burnin = 50, sample = 100, seed = 4) - summary_4e <- summary(fit4e) - expect_true(summary_4e[1,"MC%ofSD"] < 6) - - fit4f <- JAGS_fit(model_syntax4, data4, priors_list4, autofit = TRUE, autofit_control = list(max_error = 0.0001, sample_extend = 100, max_time = list(time = 5, unit = "secs")), - chains = 2, adapt = 100, burnin = 50, sample = 100, seed = 4) - summary_4f <- summary(fit4f) - expect_true(summary_4f[1,"MCerr"] > 0.0001) - expect_true(fit4f$timetaken < 5) - - # test extending the fit - fite <- JAGS_extend(fit) - expect_equal(length(fite$mcmc), 4) - expect_true(all(sapply(fite$mcmc, function(mcmc)dim(mcmc) == c(5000, 2)))) -}) +test_that("JAGS_add_priors handles mixture with PET prior", { -test_that("JAGS fit function integration with formula works" , { - - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes - skip_on_cran() - - set.seed(1) - - data_formula <- data.frame( - x_cont1 = rnorm(300), - x_fac2t = factor(rep(c("A", "B"), 150), levels = c("A", "B")), - x_fac3t = factor(rep(c("A", "B", "C"), 100), levels = c("A", "B", "C")) - ) - data <- list( - y = rnorm(300, .4 * data_formula$x_cont1 + ifelse(data_formula$x_fac3t == "A", 0.0, ifelse(data_formula$x_fac3t == "B", -0.2, 0.4)), ifelse(data_formula$x_fac2t == "A", 0.5, 1)), - N = 300 - ) + skip_if_not_installed("rjags") + # Create a bias mixture with PET prior + bias_mixture <- prior_mixture(list( + prior_none(prior_weights = 1), + prior_PET("normal", list(0, 1), prior_weights = 1) + )) - # create model with mix of a formula and free parameters --- - formula_list1 <- list( - mu = ~ x_cont1 + x_fac3t - ) - formula_data_list1 <- list( - mu = data_formula - ) - formula_prior_list1 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior("normal", list(0, 1)), - "x_fac3t" = prior_factor("normal", contrast = "treatment", list(0, 1)) - ) - ) - prior_list1 <- list( - sigma = prior("lognormal", list(0, 1)) - ) - model_syntax1 <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" + priors_pet <- list( + bias = bias_mixture ) - fit1 <- JAGS_fit( - model_syntax = model_syntax1, data = data, prior_list = prior_list1, - formula_list = formula_list1, formula_data_list = formula_data_list1, formula_prior_list = formula_prior_list1) + result_pet <- JAGS_add_priors("model{}", priors_pet) + test_reference_text(result_pet, "JAGS_add_priors_pet_mixture.txt") - posterior1 <- suppressWarnings(coda::as.mcmc(fit1)) +}) - lm_1 <- stats::lm(y ~ x_cont1 + x_fac3t, data = cbind(data_formula, y = data$y)) - # verify against the frequentist fit - vdiffr::expect_doppelganger("JAGS-fit-formula-1", function(){ +# ============================================================================ # +# SECTION 2: JAGS_get_inits tests +# ============================================================================ # +test_that("JAGS_get_inits handles various prior types", { - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfrow = c(2, 2)) + skip_if_not_installed("rjags") - hist(posterior1[,"mu_intercept"], freq = FALSE, main = "Intercept") - curve(dnorm(x, mean = coef(lm_1)["(Intercept)"], sd = summary(lm_1)$coefficients["(Intercept)", "Std. Error"]), add = TRUE, lwd = 2) + # Test with simple priors + priors_simple <- list( + mu = prior("normal", list(0, 1)), + sigma = prior("gamma", list(2, 1)) + ) - hist(posterior1[,"mu_x_cont1"], freq = FALSE, main = "mu_x_cont1") - curve(dnorm(x, mean = coef(lm_1)["x_cont1"], sd = summary(lm_1)$coefficients["x_cont1", "Std. Error"]), add = TRUE, lwd = 2) + inits1 <- JAGS_get_inits(priors_simple, chains = 2, seed = 1) + expect_equal(length(inits1), 2) + expect_true("mu" %in% names(inits1[[1]])) + expect_true("sigma" %in% names(inits1[[1]])) - hist(posterior1[,"mu_x_fac3t[1]"], freq = FALSE, main = "mu_x_fac3t") - curve(dnorm(x, mean = coef(lm_1)["x_fac3tB"], sd = summary(lm_1)$coefficients["x_fac3tB", "Std. Error"]), add = TRUE, lwd = 2) + # Same seed should give same results + inits2 <- JAGS_get_inits(priors_simple, chains = 2, seed = 1) + expect_equal(inits1, inits2) - hist(posterior1[,"mu_x_fac3t[2]"], freq = FALSE, main = "mu_x_fac3t") - curve(dnorm(x, mean = coef(lm_1)["x_fac3tC"], sd = summary(lm_1)$coefficients["x_fac3tC", "Std. Error"]), add = TRUE, lwd = 2) - }) + # Different seeds should give different results + inits3 <- JAGS_get_inits(priors_simple, chains = 2, seed = 123) + expect_false(isTRUE(all.equal(inits1, inits3))) - # create model with two formulas - formula_list2 <- list( - mu = ~ x_cont1 + x_fac3t, - sigma = ~ x_fac2t + # Test with truncated priors + priors_truncated <- list( + mu = prior("normal", list(0, 1), list(0, Inf)) ) - formula_data_list2 <- list( - mu = data_formula, - sigma = data_formula - ) + inits_truncated <- JAGS_get_inits(priors_truncated, chains = 2, seed = 1) + expect_true(all(sapply(inits_truncated, function(i) i$mu >= 0))) - formula_prior_list2 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior("normal", list(0, 1)), - "x_fac3t" = prior_factor("normal", contrast = "treatment", list(0, 1)) - ), - sigma = list( - "intercept" = prior("normal", list(0, 1)), - "x_fac2t" = prior_factor("normal", contrast = "treatment", list(0, 1)) - ) + # Test with point prior + priors_point <- list( + mu = prior("point", list(5)) ) - model_syntax2 <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(exp(sigma[i]), 2))\n", - "}\n", - "}" - ) - - fit2 <- JAGS_fit( - model_syntax = model_syntax2, data = data, prior_list = NULL, - formula_list = formula_list2, formula_data_list = formula_data_list2, formula_prior_list = formula_prior_list2) + inits_point <- JAGS_get_inits(priors_point, chains = 2, seed = 1) + # Point priors should not generate inits (they're fixed) + expect_true(!("mu" %in% names(inits_point[[1]])) || all(sapply(inits_point, function(i) i$mu == 5))) - posterior2 <- suppressWarnings(coda::as.mcmc(fit2)) + # Test with factor priors + priors_factor <- list( + p1 = prior_factor("mnorm", list(mean = 0, sd = 1), contrast = "orthonormal") + ) + attr(priors_factor[[1]], "levels") <- 3 - # verify against the true values - vdiffr::expect_doppelganger("JAGS-fit-formula-2", function(){ + inits_factor <- JAGS_get_inits(priors_factor, chains = 2, seed = 1) + expect_true("p1" %in% names(inits_factor[[1]])) - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfrow = c(2, 3)) +}) - hist(posterior2[,"mu_intercept"], freq = FALSE, main = "Intercept") - abline(v = 0, lwd = 3, col = "blue") - hist(posterior2[,"mu_x_cont1"], freq = FALSE, main = "mu_x_cont1") - abline(v = .4, lwd = 3, col = "blue") +# ============================================================================ # +# SECTION 3: JAGS_check_convergence tests +# ============================================================================ # +test_that("JAGS_check_convergence works with fitted models", { - hist(posterior2[,"mu_x_fac3t[1]"], freq = FALSE, main = "mu_x_fac3t") - abline(v = -0.2, lwd = 3, col = "blue") + skip_if_not_installed("rjags") + skip_if_no_fits() - hist(posterior2[,"mu_x_fac3t[2]"], freq = FALSE, main = "mu_x_fac3t") - abline(v = 0.4, lwd = 3, col = "blue") + fit_simple <- readRDS(file.path(temp_fits_dir, "fit_simple_normal.RDS")) + prior_list <- attr(fit_simple, "prior_list") - hist(exp(posterior2[,"sigma_intercept"]), freq = FALSE, main = "sigma_intercept") - abline(v = 0.5, lwd = 3, col = "blue") + # Test convergence check with prior_list + convergence <- JAGS_check_convergence(fit_simple, prior_list = prior_list) + expect_true(is.logical(convergence) || is.list(convergence)) - hist(exp(posterior2[,"sigma_intercept"] + posterior2[,"sigma_x_fac2t"]), freq = FALSE, main = "sigma_x_fac2t") - abline(v = 1, lwd = 3, col = "blue") - }) + # Test with NULL prior_list + convergence_null <- JAGS_check_convergence(fit_simple, prior_list = NULL) + expect_true(is.logical(convergence_null) || is.list(convergence_null)) }) -test_that("JAGS fit function integration with formula and spike and slab works" , { - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes - skip_on_cran() +# ============================================================================ # +# SECTION 4: JAGS_to_monitor tests +# ============================================================================ # +test_that("JAGS_to_monitor generates correct monitor strings", { - set.seed(1) + skip_if_not_installed("rjags") - data_formula <- data.frame( - x_cont1 = rnorm(300), - x_fac2t = factor(rep(c("A", "B"), 150), levels = c("A", "B")), - x_fac3t = factor(rep(c("A", "B", "C"), 100), levels = c("A", "B", "C")) - ) - data <- list( - y = rnorm(300, .20 * data_formula$x_cont1 + ifelse(data_formula$x_fac3t == "A", 0.0, ifelse(data_formula$x_fac3t == "B", -0.2, 0.2)), ifelse(data_formula$x_fac2t == "A", 0.5, 1)), - N = 300 + # Test with simple priors + priors_simple <- list( + mu = prior("normal", list(0, 1)), + sigma = prior("gamma", list(2, 1)) ) + monitor <- JAGS_to_monitor(priors_simple) + test_reference_text(paste(sort(monitor), collapse = ","), "JAGS_to_monitor_simple.txt") - # create model with mix of a formula and free parameters --- - formula_list1 <- list( - mu = ~ x_cont1 + x_fac3t - ) - formula_data_list1 <- list( - mu = data_formula - ) - formula_prior_list1 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior_spike_and_slab(prior("normal", list(0, 1)), prior_inclusion = prior("beta", list(1,1))), - "x_fac3t" = prior_spike_and_slab(prior_factor("mnormal", list(0, 1), contrast = "orthonormal"), - prior_inclusion = prior("spike", list(0.5))) - ) - ) - attr(formula_prior_list1$mu$x_fac3t, "multiply_by") <- "sigma" - prior_list1 <- list( - sigma = prior("lognormal", list(0, 1)) + # Test with point prior + priors_with_point <- list( + mu = prior("normal", list(0, 1)), + fixed = prior("point", list(0)) ) - model_syntax1 <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) - - fit1 <- JAGS_fit( - model_syntax = model_syntax1, data = data, prior_list = prior_list1, - formula_list = formula_list1, formula_data_list = formula_data_list1, formula_prior_list = formula_prior_list1) - - posterior1 <- suppressWarnings(coda::as.mcmc(fit1)) - - vdiffr::expect_doppelganger("JAGS-fit-formula-spike-and-slab-1", function(){ - - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfrow = c(1, 3)) - hist(posterior1[,"mu_x_cont1"], freq = FALSE, main = "x_cont1") + monitor_point <- JAGS_to_monitor(priors_with_point) + test_reference_text(paste(sort(monitor), collapse = ", "), "JAGS_to_monitor_point.txt") - hist(posterior1[,"mu_x_cont1_variable"], freq = FALSE, main = "x_cont1_variable") - - hist(posterior1[,"mu_x_cont1_inclusion"], freq = FALSE, main = "x_cont1_inclusion") - }) - - vdiffr::expect_doppelganger("JAGS-fit-formula-spike-and-slab-2", function(){ - - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfrow = c(2, 3)) - - temp_samples <- posterior1[,paste0("mu_x_fac3t[", 1:2, "]")] %*% t(contr.orthonormal(1:3)) - temp_samples_variable <- posterior1[,paste0("mu_x_fac3t_variable[", 1:2, "]")] %*% t(contr.orthonormal(1:3)) - - hist(temp_samples[,1], freq = FALSE, main = "x_fac3t[A]") - hist(temp_samples[,2], freq = FALSE, main = "x_fac3t[B]") - hist(temp_samples[,3], freq = FALSE, main = "x_fac3t[C]") + # Test with factor priors + priors_factor <- list( + p1 = prior_factor("mnorm", list(mean = 0, sd = 1), contrast = "orthonormal") + ) + attr(priors_factor[[1]], "levels") <- 3 - hist(temp_samples_variable[,1], freq = FALSE, main = "x_fac3t_variable[A]") - hist(temp_samples_variable[,2], freq = FALSE, main = "x_fac3t_variable[B]") - hist(temp_samples_variable[,3], freq = FALSE, main = "x_fac3t_variable[C]") - }) + monitor_factor <- JAGS_to_monitor(priors_factor) + test_reference_text(paste(sort(monitor_factor), collapse = ","), "JAGS_to_monitor_factor.txt") }) -test_that("JAGS fit function integration with formula, spike and slab works, and mixture works" , { - - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes - skip_on_cran() - set.seed(1) - - data_formula <- data.frame( - x_cont1 = rnorm(300), - x_fac2t = factor(rep(c("A", "B"), 150), levels = c("A", "B")), - x_fac3t = factor(rep(c("A", "B", "C"), 100), levels = c("A", "B", "C")) - ) - data <- list( - y = rnorm(300, -0.15 + 0.20 * data_formula$x_cont1 + ifelse(data_formula$x_fac3t == "A", 0.0, ifelse(data_formula$x_fac3t == "B", -0.2, 0.2)), ifelse(data_formula$x_fac2t == "A", 0.5, 1)), - N = 300 - ) - - # create model with mix of a formula and free parameters --- - formula_list1 <- list( - mu = ~ x_cont1 + x_fac3t - ) - formula_data_list1 <- list( - mu = data_formula - ) - formula_prior_list1 <- list( - mu = list( - "intercept" = prior_mixture( - list( - prior("spike", list(0), prior_weights = 2), - prior("normal", list(-1, 0.5), prior_weights = 1), - prior("normal", list( 1, 0.5), prior_weights = 1) - ), - is_null = c(T, F, F) - ), - "x_cont1" = prior_mixture( - list( - prior("spike", list(0), prior_weights = 1), - prior("normal", list(0, 1), prior_weights = 1) - ), - is_null = c(T, F) - ), - "x_fac3t" = prior_spike_and_slab(prior_factor("mnormal", list(0, 1), contrast = "orthonormal"), - prior_inclusion = prior("spike", list(0.5))) - ) - ) - attr(formula_prior_list1$mu$x_cont1, "multiply_by") <- "sigma" - prior_list1 <- list( - "sigma" = prior_mixture( - list( - prior("normal", list(0, 1), truncation = list(0, Inf)), - prior("lognormal", list(0, 1)) - ), - is_null = c(T, F) - ) - ) - model_syntax1 <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) +# ============================================================================ # +# SECTION 5: JAGS_fit attribute preservation +# ============================================================================ # +test_that("JAGS_fit preserves attributes", { - fit1 <- JAGS_fit( - model_syntax = model_syntax1, data = data, prior_list = prior_list1, - formula_list = formula_list1, formula_data_list = formula_data_list1, formula_prior_list = formula_prior_list1) + skip_if_not_installed("rjags") + skip_on_cran() + skip_if_no_fits() - posterior1 <- suppressWarnings(coda::as.mcmc(fit1)) + fit_simple <- readRDS(file.path(temp_fits_dir, "fit_simple_normal.RDS")) - vdiffr::expect_doppelganger("JAGS-fit-formula-mixture-1", function(){ + # Check that prior_list attribute is preserved + prior_list <- attr(fit_simple, "prior_list") + expect_true(!is.null(prior_list)) + expect_true(is.list(prior_list)) - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfrow = c(3, 3)) + # Check class + expect_true(inherits(fit_simple, "BayesTools_fit") || inherits(fit_simple, "runjags")) - barplot(table(posterior1[,"mu_intercept_indicator"]) / nrow(posterior1), main = "Intercept indicator") - barplot(table(posterior1[,"mu_x_cont1_indicator"]) / nrow(posterior1), main = "x_cont1 indicator") - barplot(table(posterior1[,"sigma_indicator"]) / nrow(posterior1), main = "sigma indicator") +}) - hist(posterior1[,"mu_intercept"], freq = FALSE, main = "mu_intercept") - hist(posterior1[,"mu_x_cont1"], freq = FALSE, main = "x_cont1") - hist(posterior1[,"sigma"], freq = FALSE, main = "sigma") - }) - vdiffr::expect_doppelganger("JAGS-fit-formula-mixture-2", function(){ +# ============================================================================ # +# SECTION 6: runjags_estimates_table tests +# ============================================================================ # +test_that("runjags_estimates_table works with fitted models", { - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfrow = c(2, 3)) + skip_if_not_installed("rjags") + skip_on_cran() + skip_if_no_fits() - temp_samples <- posterior1[,paste0("mu_x_fac3t[", 1:2, "]")] %*% t(contr.orthonormal(1:3)) - temp_samples_variable <- posterior1[,paste0("mu_x_fac3t_variable[", 1:2, "]")] %*% t(contr.orthonormal(1:3)) + fit_simple <- readRDS(file.path(temp_fits_dir, "fit_simple_normal.RDS")) - hist(temp_samples[,1], freq = FALSE, main = "x_fac3t[A]") - hist(temp_samples[,2], freq = FALSE, main = "x_fac3t[B]") - hist(temp_samples[,3], freq = FALSE, main = "x_fac3t[C]") + # Test basic estimates table + estimates_table <- runjags_estimates_table(fit_simple) + test_reference_table(estimates_table, "runjags_estimates_simple.txt") - hist(temp_samples_variable[,1], freq = FALSE, main = "x_fac3t_variable[A]") - hist(temp_samples_variable[,2], freq = FALSE, main = "x_fac3t_variable[B]") - hist(temp_samples_variable[,3], freq = FALSE, main = "x_fac3t_variable[C]") - }) + # Test without specific parameters + estimates_table_param <- runjags_estimates_table(fit_simple, remove_parameters = "m") + test_reference_table(estimates_table_param, "runjags_estimates_param_m.txt") }) -test_that("JAGS fit with priors expressions", { - - skip_if_not_installed("rjags") - # a simple prior - model_syntax <- "model{}" - priors <- list( - x = prior("normal", list(0, expression(x_sigma))), - x_sigma = prior("invgamma", list(1/2, 1/2)) - ) +# ============================================================================ # +# SECTION 7: JAGS_extend tests +# ============================================================================ # +test_that("JAGS_extend works correctly", { - model_syntax <- JAGS_add_priors(model_syntax, priors) - monitor <- JAGS_to_monitor(priors) - inits <- JAGS_get_inits(priors, chains = 2, seed = 1) - - set.seed(1) - model <- rjags::jags.model(file = textConnection(model_syntax), inits = inits, n.chains = 2, quiet = TRUE) - samples <- rjags::coda.samples(model = model, variable.names = monitor, n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples <- do.call(rbind, samples) - - vdiffr::expect_doppelganger("JAGS-model-prior-e1", function(){ - x_samples <- samples[,"x"] - hist(x_samples[abs(x_samples) < 10], breaks = 50, main = print(priors[[1]], plot = TRUE), freq = FALSE) - lines(prior("Cauchy", list(0, 1), list(-10, 10))) - }) - - # a spike and slab prior - model_syntax <- "model{}" - priors <- list( - x = prior_spike_and_slab( - prior("normal", list(0, expression(x_sigma))) + skip_if_not_installed("rjags") + skip_on_cran() + skip_if_no_fits() + + fit_simple <- readRDS(file.path(temp_fits_dir, "fit_simple_normal.RDS")) + + # Test extending a fitted model + fit_extended <- JAGS_extend( + fit_simple, + autofit_control = list( + max_Rhat = 1.05, + min_ESS = 100, + max_error = 0.01, + max_SD_error = 0.05, + max_time = list(time = 1, unit = "mins"), + sample_extend = 100, + restarts = 2, + max_extend = 2 ), - x_sigma = prior("invgamma", list(1/2, 1/2)) - ) - - model_syntax <- JAGS_add_priors(model_syntax, priors) - monitor <- JAGS_to_monitor(priors) - inits <- JAGS_get_inits(priors, chains = 2, seed = 1) - - set.seed(1) - model <- rjags::jags.model(file = textConnection(model_syntax), inits = inits, n.chains = 2, quiet = TRUE) - samples <- rjags::coda.samples(model = model, variable.names = monitor, n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples <- do.call(rbind, samples) - - vdiffr::expect_doppelganger("JAGS-model-prior-e2", function(){ - x_samples <- samples[,"x"] - x_samples <- x_samples[x_samples != 0] - hist(x_samples[abs(x_samples) < 10], breaks = 50, main = print(priors[[1]], plot = TRUE), freq = FALSE) - lines(prior("Cauchy", list(0, 1), list(-10, 10))) - }) - - # a mixture prior - model_syntax <- "model{}" - priors <- list( - x = prior_mixture(list( - prior("normal", list(0, expression(x_sigma))), - prior("cauchy", list(0, 1)) - ), is_null = c(T, F)), - x_sigma = prior("invgamma", list(1/2, 1/2)) - ) - - model_syntax <- JAGS_add_priors(model_syntax, priors) - monitor <- JAGS_to_monitor(priors) - inits <- JAGS_get_inits(priors, chains = 2, seed = 1) - - set.seed(1) - model <- rjags::jags.model(file = textConnection(model_syntax), inits = inits, n.chains = 2, quiet = TRUE) - samples <- rjags::coda.samples(model = model, variable.names = monitor, n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples <- do.call(rbind, samples) - - vdiffr::expect_doppelganger("JAGS-model-prior-e3", function(){ - x_samples <- samples[,"x"] - hist(x_samples[abs(x_samples) < 10], breaks = 50, main = print(priors[[1]], plot = TRUE), freq = FALSE) - lines(prior("Cauchy", list(0, 1), list(-10, 10))) - }) + silent = TRUE, + seed = 1 + ) + + # Test extending a fitted model with parallel + fit_extended2 <- JAGS_extend( + fit_simple, + autofit_control = list( + max_Rhat = 1.05, + min_ESS = 100, + max_error = 0.01, + max_SD_error = 0.05, + max_time = list(time = 1, unit = "mins"), + sample_extend = 100, + restarts = 2, + max_extend = 2 + ), + parallel = TRUE, + cores = 2, + silent = TRUE, + seed = 1 + ) + + # Check that the extended fit is still a BayesTools_fit + expect_true(inherits(fit_extended, "BayesTools_fit")) + expect_true(inherits(fit_extended, "runjags")) + expect_true(inherits(fit_extended2, "BayesTools_fit")) + expect_true(inherits(fit_extended2, "runjags")) + + # Check that attributes are preserved + expect_true(!is.null(attr(fit_extended, "prior_list"))) + expect_true(!is.null(attr(fit_extended, "model_syntax"))) + expect_true(!is.null(attr(fit_extended2, "prior_list"))) + expect_true(!is.null(attr(fit_extended2, "model_syntax"))) + + # Check that the extended fit has more samples + original_samples <- nrow(suppressWarnings(coda::as.mcmc(fit_simple))) + extended_samples <- nrow(suppressWarnings(coda::as.mcmc(fit_extended))) + extended_samples2 <- nrow(suppressWarnings(coda::as.mcmc(fit_extended2))) + expect_true(extended_samples >= original_samples) + expect_true(extended_samples2 >= original_samples) }) -test_that("JAGS fit function integration with formula and priors expressions", { - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes +# ============================================================================ # +# SECTION 8: JAGS handles specific prior types +# ============================================================================ # +test_that("JAGS handles invgamma prior", { + skip_if_not_installed("rjags") - skip_on_cran() - set.seed(1) + priors_inv <- list(tau = prior("invgamma", list(3, 2))) - data_formula <- data.frame( - x_cont1 = rnorm(100), - x_fac2t = factor(rep(c("A", "B"), 50), levels = c("A", "B")) - ) - data <- list( - y = rnorm(100, .4 * data_formula$x_cont1 + ifelse(data_formula$x_fac2t == "A", 0.25, 0.50)), - N = 100 - ) + # Test syntax + result <- JAGS_add_priors("model{}", priors_inv) + expect_true(grepl("inv_tau", result)) + expect_true(grepl("dgamma", result)) - # create model with mix of a formula and free parameters --- - formula_list <- list( - mu = ~ x_cont1 + x_fac2t - ) - formula_data_list <- list( - mu = data_formula - ) - formula_prior_list1 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior("normal", list(0, 1)), - "x_fac2t" = prior_spike_and_slab(prior_factor("cauchy", contrast = "treatment", list(0, 1))) - ) - ) - formula_prior_list2 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior("normal", list(0, 1)), - "x_fac2t" = prior_spike_and_slab(prior_factor("normal", contrast = "treatment", list(0, expression(tau)))) - ) - ) - prior_list1 <- list( - sigma = prior("lognormal", list(0, 1)) - ) - prior_list2 <- list( - sigma = prior("lognormal", list(0, 1)), - tau = prior("invgamma", list(1/2, 1/2)) - ) + # Test inits + inits <- JAGS_get_inits(priors_inv, chains = 2, seed = 1) + expect_true("inv_tau" %in% names(inits[[1]])) - model_syntax <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) - - # fit1 <- JAGS_fit( - # model_syntax = model_syntax, data = data, prior_list = prior_list1, - # formula_list = formula_list, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1, adapt = 1000) - fit2 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list2, - formula_list = formula_list, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list2, adapt = 1000) - - # runjags_estimates_table(fit1) - # verified against the simpler model directly sampling cauchy - expect_equal(capture_output_lines(print(runjags_estimates_table(fit2, conditional = FALSE, remove_parameters = "tau")), width = 150), c( - " Mean SD lCI Median uCI error(MCMC) error(MCMC)/SD ESS R-hat", - "(mu) intercept 0.304 0.125 0.024 0.312 0.527 0.00197 0.016 4128 1.001", - "(mu) x_cont1 0.385 0.111 0.169 0.383 0.604 0.00091 0.008 14631 1.000", - "(mu) x_fac2t (inclusion) 0.280 NA NA NA NA NA NA NA NA", - "(mu) x_fac2t[B] 0.068 0.148 -0.002 0.000 0.494 0.00309 0.021 2299 1.002", - "sigma 0.980 0.071 0.854 0.975 1.133 0.00074 0.010 9500 1.001" - )) + # Test monitor + monitor <- JAGS_to_monitor(priors_inv) + expect_true("tau" %in% monitor) }) -test_that("JAGS fit function integration with formula expressions", { - - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes - skip_if_not_installed("rjags") - skip_on_cran() - set.seed(1) +test_that("JAGS handles weightfunction one.sided with alpha1/alpha2", { - data_formula <- data.frame( - x_cont1 = rnorm(200), - x_fac2t = factor(rep(LETTERS[1:10], 20)) - ) - x_fac2t_values <- rnorm(10, 0, 0.3) - names(x_fac2t_values) <- LETTERS[1:10] - data <- list( - y = rnorm(200, .4 * data_formula$x_cont1 + x_fac2t_values[data_formula[["x_fac2t"]]]), - N = 200 - ) - - # add id mapping - data[["mapping_id"]] <- as.integer(data_formula$x_fac2t) + skip_if_not_installed("rjags") - # create model with mix of a formula and free parameters --- - formula_list <- list( - mu = ~ x_cont1 + expression(mu_id[mapping_id[i]]) - ) - formula_data_list <- list( - mu = data_formula - ) - formula_prior_list <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior("normal", list(0, 1)) - ) - ) - prior_list <- list( - sigma = prior("lognormal", list(0, 1)), - mu_id = prior_factor("normal", list(0, expression(tau)), contrast = "independent"), - tau = prior("normal", list(0, 10), list(0, Inf)) - ) + # One-sided with steps crossing 0.5 uses alpha1/alpha2 parametrization + priors_wf2 <- list(omega = prior_weightfunction("one.sided", list(c(0.05, 0.60), c(1, 1), c(1, 1)))) - attr(prior_list$mu_id, "levels") <- 10 + # Test syntax + result <- JAGS_add_priors("model{}", priors_wf2) + expect_true(grepl("eta1", result)) + expect_true(grepl("eta2", result)) - model_syntax <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) + # Test inits + inits <- JAGS_get_inits(priors_wf2, chains = 2, seed = 1) + expect_true("eta1" %in% names(inits[[1]])) + expect_true("eta2" %in% names(inits[[1]])) - fit <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list) - - fit_sumary <- runjags_estimates_table(fit, formula_prefix = FALSE) - expect_true(cor(fit_sumary[grepl("id", rownames(fit_sumary)),"Mean"], x_fac2t_values) > 0.8) - expect_equal(capture_output_lines(print(fit_sumary), width = 150), c( - " Mean SD lCI Median uCI error(MCMC) error(MCMC)/SD ESS R-hat", - "intercept 0.179 0.173 -0.169 0.179 0.525 0.00476 0.028 1319 1.003", - "x_cont1 0.423 0.077 0.272 0.424 0.575 0.00064 0.008 14330 1.000", - "sigma 0.996 0.051 0.903 0.994 1.103 0.00055 0.011 8587 1.000", - "id[1] 0.107 0.246 -0.376 0.106 0.602 0.00470 0.019 2763 1.002", - "id[2] 0.359 0.250 -0.118 0.351 0.867 0.00479 0.019 2724 1.003", - "id[3] 0.213 0.250 -0.272 0.207 0.726 0.00466 0.019 2873 1.001", - "id[4] -0.340 0.249 -0.853 -0.331 0.127 0.00481 0.019 2699 1.001", - "id[5] -0.476 0.257 -1.001 -0.468 0.009 0.00492 0.019 2804 1.001", - "id[6] 0.643 0.260 0.152 0.634 1.180 0.00507 0.019 2692 1.002", - "id[7] -0.213 0.247 -0.723 -0.208 0.260 0.00462 0.019 2882 1.001", - "id[8] -0.069 0.245 -0.551 -0.069 0.413 0.00465 0.019 2781 1.001", - "id[9] -0.362 0.249 -0.873 -0.354 0.107 0.00489 0.020 2656 1.001", - "id[10] 0.125 0.245 -0.347 0.122 0.624 0.00446 0.018 3012 1.002", - "tau 0.474 0.172 0.229 0.444 0.903 0.00319 0.019 2915 1.001" - )) + # Test monitor + monitor <- JAGS_to_monitor(priors_wf2) + expect_true("eta1" %in% monitor) + expect_true("eta2" %in% monitor) }) -test_that("JAGS fit function with random effects", { - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes +test_that("JAGS handles weightfunction fixed prior", { + skip_if_not_installed("rjags") - skip_on_cran() - set.seed(1) + priors_wf_fixed <- list(omega = prior_weightfunction("one.sided.fixed", list(steps = c(0.05), omega = c(1, 0.5)))) - data_formula <- data.frame( - x_cont1 = rnorm(200), - x_fac3 = as.factor(sample(LETTERS[1:3], 200, replace = TRUE)), - id = factor(rep(LETTERS[1:10], 20)) - ) - id_values <- rnorm(10, 0, 0.5) - names(id_values) <- LETTERS[1:10] - id_x_cont1_values <- rnorm(10, 0, 0.3) - names(id_x_cont1_values) <- LETTERS[1:10] - - data <- list( - y = rnorm(200, (0.4 + id_x_cont1_values[data_formula$id]) * data_formula$x_cont1 + id_values[data_formula$id]), - N = 200 - ) + # Test syntax - fixed weightfunction has no eta parameters to sample + result <- JAGS_add_priors("model{}", priors_wf_fixed) + expect_true(grepl("omega", result)) - # # the full model correspond to this lme4 call - # summary(lme4::lmer(y ~ x_cont1 + (1 + x_cont1||id), data = cbind(y = data$y, data_formula))) + # Test inits - fixed weightfunction should return empty inits for eta + inits <- JAGS_get_inits(priors_wf_fixed, chains = 2, seed = 1) + # Should not have eta since it's fixed + expect_true(!("eta" %in% names(inits[[1]]))) - # intercept only model ---- - formula_list <- list( - mu = ~ 1 + (1 ||id) - ) - formula_data_list <- list( - mu = data_formula - ) - formula_prior_list <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "intercept|id" = prior("normal", list(0, 1), list(0, 1)) - ) - ) - prior_list <- list( - sigma = prior("lognormal", list(0, 1)) - ) + # Test monitor + monitor <- JAGS_to_monitor(priors_wf_fixed) + expect_true("omega" %in% monitor) - model_syntax <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) +}) - fit <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list) - fit_summary <- runjags_estimates_table(fit, formula_prefix = FALSE) +test_that("JAGS handles factor treatment/independent priors", { - # summary(lme4::lmer(y ~ 1 + (1 | id), data = cbind(y = data$y, data_formula))) - expect_equal(capture_output_lines(print(fit_summary), width = 150), c( - " Mean SD lCI Median uCI error(MCMC) error(MCMC)/SD ESS R-hat", - "intercept -0.044 0.156 -0.360 -0.044 0.272 0.00297 0.019 2785 1.004", - "sd(intercept|id) 0.377 0.161 0.090 0.361 0.745 0.00260 0.016 3832 1.001", - "sigma 1.231 0.064 1.114 1.228 1.364 0.00069 0.011 8608 1.000" - )) + skip_if_not_installed("rjags") - # random slope (no intercept) ---- - formula_list <- list( - mu = ~ 1 + (0 + x_cont1 ||id) - ) - formula_prior_list <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1|id" = prior("normal", list(0, 1), list(0, 1)) - ) - ) + # Treatment contrast + prior_treat <- prior_factor("normal", list(0, 1), contrast = "treatment") + attr(prior_treat, "levels") <- 3 - fit <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list) + priors_treat <- list(fac = prior_treat) + result_treat <- JAGS_add_priors("model{}", priors_treat) + expect_true(grepl("fac\\[i\\]", result_treat)) - fit_summary <- runjags_estimates_table(fit, formula_prefix = FALSE) + # Independent contrast + prior_indep <- prior_factor("gamma", list(2, 1), contrast = "independent") + attr(prior_indep, "levels") <- 2 - # summary(lme4::lmer(y ~ 1 + (0 + x_cont1 | id), data = cbind(y = data$y, data_formula))) - expect_equal(capture_output_lines(print(fit_summary), width = 150), c( - " Mean SD lCI Median uCI error(MCMC) error(MCMC)/SD ESS R-hat", - "intercept -0.069 0.081 -0.227 -0.069 0.089 0.00067 0.008 14456 1.000", - "sd(x_cont1|id) 0.660 0.147 0.401 0.651 0.956 0.00271 0.019 2922 1.003", - "sigma 1.113 0.058 1.009 1.110 1.232 0.00061 0.011 8972 1.000" - )) + priors_indep <- list(fac = prior_indep) + result_indep <- JAGS_add_priors("model{}", priors_indep) + expect_true(grepl("dgamma", result_indep)) - # random factor slope ---- - formula_list <- list( - mu = ~ 1 + x_cont1 + (x_fac3 ||id) - ) - formula_prior_list <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior("normal", list(0, 1)), - "intercept|id" = prior("normal", list(0, 1), list(0, 1)), - "x_fac3|id" = prior("normal", list(0, 1), list(0, 1)) - ) - ) +}) - fit <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list) - - fit_summary <- runjags_estimates_table(fit, formula_prefix = FALSE) - - # this is probably the closest alternative - # summary(lme4::lmer(y ~ 1 + x_cont1 + (x_fac3 ||id), data = cbind(y = data$y, data_formula))) - expect_equal(capture_output_lines(print(fit_summary), width = 150), c( - " Mean SD lCI Median uCI error(MCMC) error(MCMC)/SD ESS R-hat", - "intercept -0.050 0.127 -0.306 -0.050 0.203 0.00215 0.017 3485 1.001", - "x_cont1 0.544 0.089 0.367 0.544 0.718 0.00074 0.008 14246 1.000", - "sd(intercept|id) 0.243 0.148 0.016 0.229 0.578 0.00263 0.018 3196 1.002", - "sd(x_fac3[B]|id) 0.223 0.170 0.009 0.186 0.637 0.00210 0.012 6576 1.000", - "sd(x_fac3[C]|id) 0.277 0.192 0.014 0.247 0.729 0.00271 0.014 5047 1.000", - "sigma 1.130 0.059 1.020 1.127 1.254 0.00062 0.011 9075 1.000" - )) +test_that("JAGS handles vector mt prior", { - # full spike and slab model ---- - formula_list <- list( - mu = ~ x_cont1 + x_fac3 + (x_cont1 + x_fac3||id) - ) - formula_prior_list <- list( - mu = list( - "intercept" = prior_spike_and_slab(prior("normal", list(0, 5))), - "x_cont1" = prior_spike_and_slab(prior("normal", list(0, 1))), - "x_fac3" = prior_spike_and_slab(prior_factor("mnormal", list(0, 1))), - "intercept|id" = prior_spike_and_slab(prior("normal", list(0, 1), list(0, 1))), - "x_cont1|id" = prior_mixture(list( - prior("normal", list(0, 1), list(0, 1)), - prior("beta", list(1, 2)) - ), is_null = c(TRUE, FALSE)), - "x_fac3|id" = prior_spike_and_slab(prior("normal", list(0, 1), list(0, 1))) - ) - ) + skip_if_not_installed("rjags") - fit <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list) - - fit_summary <- runjags_estimates_table(fit, formula_prefix = FALSE, remove_inclusion = TRUE) - fit_inference <- runjags_inference_table(fit, formula_prefix = FALSE) - - expect_equal(capture_output_lines(print(fit_summary), width = 150), c( - " Mean SD lCI Median uCI error(MCMC) error(MCMC)/SD ESS R-hat", - "intercept -0.002 0.019 0.000 0.000 0.000 0.00026 0.014 5629 1.004", - "x_cont1 0.471 0.201 0.000 0.498 0.797 0.00730 0.036 769 1.005", - "x_fac3[1] 0.001 0.026 0.000 0.000 0.010 0.00025 0.010 11328 1.000", - "x_fac3[2] 0.008 0.043 0.000 0.000 0.166 0.00064 0.015 4569 1.002", - "sd(intercept|id) 0.125 0.154 0.000 0.021 0.481 0.00381 0.025 1650 1.001", - "sd(x_cont1|id) 0.387 0.169 0.094 0.367 0.773 0.00404 0.024 1822 1.003", - "sd(x_fac3|id) 0.030 0.072 0.000 0.000 0.258 0.00116 0.016 3928 1.003", - "sigma 1.105 0.059 0.996 1.103 1.226 0.00070 0.012 7094 1.000" - )) - expect_equal(capture_output_lines(print(fit_inference), width = 150), c( - " Prior prob. Post. prob. Inclusion BF", - "intercept 0.500 0.023 0.024", - "x_cont1 0.500 0.923 11.924", - "x_fac3 0.500 0.043 0.045", - "sd(intercept|id) 0.500 0.512 1.050", - "sd(x_cont1|id) 0.500 0.521 1.089", - "sd(x_fac3|id) 0.500 0.217 0.278" - )) + prior_mt <- prior("mt", list(location = 0, scale = 1, df = 5, K = 2)) + priors_mt <- list(p = prior_mt) - # independent factor priors ---- - formula_list <- list( - mu = ~ x_fac3 + (x_fac3||id) - ) - formula_prior_list <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_fac3" = prior_factor("normal", list(0, 1), contrast = "independent"), - "intercept|id" = prior("normal", list(0, 1), list(0, 1)), - "x_fac3|id" = prior("normal", list(0, 1), list(0, 1)) - ) - ) + # Test syntax + result <- JAGS_add_priors("model{}", priors_mt) + expect_true(grepl("prior_par_s_p", result)) + expect_true(grepl("prior_par_z_p", result)) - fit <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list) - - fit_summary <- runjags_estimates_table(fit, formula_prefix = FALSE, remove_inclusion = TRUE) - - expect_equal(capture_output_lines(print(fit_summary), width = 150), c( - " Mean SD lCI Median uCI error(MCMC) error(MCMC)/SD ESS R-hat", - "intercept -0.168 0.582 -1.305 -0.158 0.968 0.04375 0.075 187 1.006", - "x_fac3[A] 0.227 0.580 -0.891 0.216 1.364 0.04318 0.074 207 1.004", - "x_fac3[B] 0.079 0.582 -1.050 0.068 1.221 0.04162 0.072 219 1.005", - "x_fac3[C] 0.007 0.580 -1.112 -0.001 1.161 0.03938 0.068 233 1.004", - "sd(intercept|id) 0.312 0.177 0.022 0.300 0.710 0.00348 0.020 2585 1.004", - "sd(x_fac3[A]|id) 0.410 0.226 0.027 0.399 0.883 0.00377 0.017 3632 1.000", - "sd(x_fac3[B]|id) 0.272 0.196 0.011 0.238 0.727 0.00260 0.013 5669 1.000", - "sd(x_fac3[C]|id) 0.330 0.216 0.016 0.304 0.827 0.00316 0.015 4694 1.000", - "sigma 1.213 0.065 1.096 1.210 1.349 0.00072 0.011 8195 1.001" - )) + # Test inits + inits <- JAGS_get_inits(priors_mt, chains = 2, seed = 1) + expect_true("prior_par_s_p" %in% names(inits[[1]])) + expect_true("prior_par_z_p" %in% names(inits[[1]])) }) -test_that("JAGS fit function integration with multiple formulas" , { - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes - skip_on_cran() - - set.seed(1) +test_that("JAGS handles bias mixture with weightfunction", { - data_formula <- data.frame( - x_cont1 = rnorm(300), - x_fac2o = factor(rep(c("A", "B"), 150), levels = c("A", "B")) - ) - data_mu <- 0.20 * data_formula$x_cont1 - data_sigma <- 0.50 * exp(ifelse(data_formula$x_fac2o == "A", -0.5, 0.5)) - data <- list( - y = rnorm(300, data_mu, data_sigma), - N = 300 - ) - - - # create model with mix of a formula and free parameters --- - formula_list1 <- list( - mu = ~ x_cont1 + x_fac2o, - sigma_exp = ~ x_cont1 + x_fac2o - ) - formula_data_list1 <- list( - mu = data_formula, - sigma_exp = data_formula - ) - formula_prior_list1 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior_spike_and_slab(prior("normal", list(0, 1))), - "x_fac2o" = prior_spike_and_slab(prior_factor("mnormal", list(0, 1), contrast = "meandif")) - ), - sigma_exp = list( - "intercept" = prior("spike", list(0)), - "x_cont1" = prior_spike_and_slab(prior("normal", list(0, 1))), - "x_fac2o" = prior_spike_and_slab(prior_factor("mnormal", list(0, 1), contrast = "meandif")) - ) - ) - prior_list1 <- list( - "sigma" = prior("normal", list(0, 5), list(0, Inf)) - ) - model_syntax1 <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma * exp(sigma_exp[i]), 2))\n", - "}\n", - "}" - ) + skip_if_not_installed("rjags") - fit1 <- suppressWarnings(JAGS_fit( - model_syntax = model_syntax1, data = data, prior_list = prior_list1, - formula_list = formula_list1, formula_data_list = formula_data_list1, formula_prior_list = formula_prior_list1, - chains = 2, adapt = 500, burnin = 500, sample = 1000)) - - expect_equal(capture_output_lines(print(JAGS_estimates_table(fit1, remove_inclusion = TRUE)), width = 150), c( - " Mean SD lCI Median uCI error(MCMC) error(MCMC)/SD ESS R-hat", - "(mu) intercept -0.013 0.026 -0.063 -0.014 0.038 0.00066 0.025 1624 1.000", - "(mu) x_cont1 0.204 0.027 0.152 0.204 0.255 0.00059 0.022 2102 1.001", - "(mu) x_fac2o 0.001 0.009 0.000 0.000 0.017 0.00039 0.043 658 1.027", - "(sigma_exp) x_cont1 -0.001 0.009 -0.014 0.000 0.000 0.00028 0.031 1099 1.023", - "(sigma_exp) x_fac2o 0.436 0.041 0.348 0.433 0.520 0.00225 0.055 333 1.005", - "sigma 0.523 0.021 0.484 0.522 0.570 0.00067 0.032 1020 1.000" - )) - expect_equal(capture_output_lines(print(JAGS_inference_table(fit1)), width = 150), c( - " Prior prob. Post. prob. Inclusion BF", - "(mu) x_cont1 0.500 1.000 Inf", - "(mu) x_fac2o 0.500 0.040 0.042", - "(sigma_exp) x_cont1 0.500 0.045 0.047", - "(sigma_exp) x_fac2o 0.500 1.000 Inf" + bias_mix_wf <- prior_mixture(list( + prior_none(prior_weights = 1), + prior_weightfunction("one.sided", list(c(0.05), c(1, 1)), prior_weights = 1) )) -}) + priors_bias_wf <- list(bias = bias_mix_wf) -test_that("JAGS parallel fit function works", { + result <- JAGS_add_priors("model{}", priors_bias_wf) + expect_true(grepl("bias_indicator", result)) + expect_true(grepl("omega", result)) + expect_true(grepl("eta", result)) - skip("requires parallel processing") - skip_on_cran() - skip_on_travis() - skip_on_ci() + # Test inits + inits <- JAGS_get_inits(priors_bias_wf, chains = 2, seed = 1) + expect_true("bias_indicator" %in% names(inits[[1]])) - priors_list <- list( - m = prior("normal", list(0, 1)) - ) - data <- list( - x = c(-500), - N = 1 - ) - model_syntax <- - "model - { - l = 1 - for(i in 1:N){ - x[i] ~ dt(m, pow(.3, -2), 1) - } - }" - - - fit <- JAGS_fit(model_syntax, data, priors_list, autofit = FALSE, parallel = TRUE, - chains = 4, adapt = 100, burnin = 50, sample = 100, seed = 4) - expect_equal(length(fit$mcmc), 4) - expect_true(all(sapply(fit$mcmc, function(mcmc)dim(mcmc) == c(100, 1)))) - - - ### checking mcmc_error autofit - runjags::runjags.options(silent.jags = TRUE, silent.runjags = TRUE) - fit1 <- JAGS_fit(model_syntax, data, priors_list, parallel = TRUE, - autofit = TRUE, autofit_control = list(max_error = 0.05, sample_extend = 100), - chains = 4, adapt = 100, burnin = 50, sample = 100, seed = 4) - expect_equal(length(fit1$mcmc), 4) - expect_true(all(sapply(fit1$mcmc, function(mcmc)dim(mcmc) == c(200, 1)))) + # Test monitor + monitor <- JAGS_to_monitor(priors_bias_wf) + expect_true("bias_indicator" %in% monitor) + expect_true("omega" %in% monitor) }) -test_that("JAGS fit function with JASP works" , { - set.seed(1) - data <- list( - x = rnorm(50, 0, .5), - N = 50 +# ============================================================================ # +# SECTION 9: JAGS_check_and_list_autofit_settings +# ============================================================================ # +test_that("JAGS_check_and_list_autofit_settings validates all parameters", { + + # Valid settings + valid_settings <- list( + max_Rhat = 1.05, + min_ESS = 500, + max_error = 0.01, + max_SD_error = 0.05, + max_time = list(time = 1, unit = "mins"), + sample_extend = 100, + restarts = 3, + max_extend = 10 ) - priors_list <- list( - m = prior("normal", list(0, 1)), - s = prior("normal", list(0, 1), list(0, Inf)) + expect_silent(JAGS_check_and_list_autofit_settings(valid_settings)) + + # max_time without names - should auto-assign + unnamed_time <- list( + max_Rhat = 1.05, min_ESS = 500, max_error = 0.01, max_SD_error = 0.05, + max_time = list(1, "mins"), sample_extend = 100 ) - model_syntax <- - "model - { - for(i in 1:N){ - x[i] ~ dnorm(m, pow(s, -2)) - } - }" - - ### checking the default settings - set.seed(1) - - ### checking control the main control arguments - fit <- capture.output(JAGS_fit(model_syntax, data, priors_list, chains = 1, adapt = 100, burnin = 150, sample = 175, thin = 3, seed = 2, is_JASP = TRUE)) - expect_equal(fit, c( - "Adapting and burnin the model(1)" , - ".Sampling the model(5)" , - "....." , - "JAGS model with 176 samples (thin = 3; adapt+burnin = 250)" , - "" , - "Full summary statistics have not been pre-calculated - use either the summary method or add.summary to calculate summary statistics", - "" - )) + expect_silent(JAGS_check_and_list_autofit_settings(unnamed_time)) }) diff --git a/tests/testthat/test-JAGS-formula-scale.R b/tests/testthat/test-JAGS-formula-scale.R new file mode 100644 index 00000000..cca317d8 --- /dev/null +++ b/tests/testthat/test-JAGS-formula-scale.R @@ -0,0 +1,1398 @@ +# ============================================================================ # +# TEST FILE: JAGS Formula Standardization +# ============================================================================ # +# +# PURPOSE: +# Tests for automatic standardization of continuous predictors in JAGS_formula +# +# DEPENDENCIES: +# - common-functions.R: Test helpers +# +# SKIP CONDITIONS: +# - None (pure R tests, no JAGS fitting required) +# +# TAGS: @formula, @standardization, @fast +# ============================================================================ # + +# Load common test helpers +source(testthat::test_path("common-functions.R")) + +test_that("JAGS_formula accepts and validates formula_scale parameter", { + + # Setup test data + set.seed(1) + df <- data.frame( + y = rnorm(60), + x_cont = rnorm(60, mean = 3, sd = 5), + x_fac = factor(rep(c("A", "B", "C"), 20), levels = c("A", "B", "C")) + ) + + # Test 1: formula_scale = NULL (no standardization) + prior_list <- list( + "intercept" = prior("normal", list(0, 1)), + "x_cont" = prior("normal", list(0, 1)), + "x_fac" = prior_factor("normal", list(0, 1), contrast = "treatment") + ) + + result <- JAGS_formula( + formula = ~ x_cont + x_fac, + parameter = "mu", + data = df, + prior_list = prior_list, + formula_scale = NULL + ) + + expect_false("formula_scale" %in% names(result)) + expect_equal(unname(result$data$mu_data_x_cont), as.numeric(df$x_cont)) + + # Test 2: formula_scale with standardization + result_scaled <- JAGS_formula( + formula = ~ x_cont + x_fac, + parameter = "mu", + data = df, + prior_list = prior_list, + formula_scale = list(x_cont = TRUE) + ) + + expect_true("formula_scale" %in% names(result_scaled)) + expect_true("mu_x_cont" %in% names(result_scaled$formula_scale)) + expect_equal(names(result_scaled$formula_scale$mu_x_cont), c("mean", "sd")) + expect_equal(unname(result_scaled$data$mu_data_x_cont), as.numeric(scale(df$x_cont))) + + # Test 3: Check that scale info is correct + original_mean <- mean(df$x_cont) + original_sd <- sd(df$x_cont) + + expect_equal(result_scaled$formula_scale$mu_x_cont$mean, original_mean) + expect_equal(result_scaled$formula_scale$mu_x_cont$sd, original_sd) + + # Test 4: formula_scale with FALSE should not standardize + result_not_scaled <- JAGS_formula( + formula = ~ x_cont + x_fac, + parameter = "mu", + data = df, + prior_list = prior_list, + formula_scale = list(x_cont = FALSE) + ) + + expect_false("formula_scale" %in% names(result_not_scaled)) + expect_equal(unname(result_not_scaled$data$mu_data_x_cont), as.numeric(df$x_cont)) + +}) + +test_that("JAGS_formula standardization preserves data correctly", { + + set.seed(2) + df <- data.frame( + x1 = rnorm(50, mean = 10, sd = 3), + x2 = rnorm(50, mean = -5, sd = 2) + ) + + prior_list <- list( + "intercept" = prior("normal", list(0, 1)), + "x1" = prior("normal", list(0, 1)), + "x2" = prior("normal", list(0, 1)) + ) + + ### Standardize both predictors + result <- JAGS_formula( + formula = ~ x1 + x2, + parameter = "beta", + data = df, + prior_list = prior_list, + formula_scale = list(x1 = TRUE, x2 = TRUE) + ) + + # Check that both predictors are standardized + expect_length(result$formula_scale, 2) + expect_true("beta_x1" %in% names(result$formula_scale)) + expect_true("beta_x2" %in% names(result$formula_scale)) + + # Verify scale parameters + expect_equal(result$formula_scale$beta_x1$mean, 10, tolerance = 0.5) + expect_equal(result$formula_scale$beta_x1$sd, 3, tolerance = 0.5) + expect_equal(result$formula_scale$beta_x2$mean, -5, tolerance = 0.5) + expect_equal(result$formula_scale$beta_x2$sd, 2, tolerance = 0.5) + + ### Standardize both predictors (lazily) + result <- JAGS_formula( + formula = ~ x1 + x2, + parameter = "beta", + data = df, + prior_list = prior_list, + formula_scale = TRUE + ) + + # Check that both predictors are standardized + expect_length(result$formula_scale, 2) + expect_true("beta_x1" %in% names(result$formula_scale)) + expect_true("beta_x2" %in% names(result$formula_scale)) + + # Verify scale parameters + expect_equal(result$formula_scale$beta_x1$mean, 10, tolerance = 0.5) + expect_equal(result$formula_scale$beta_x1$sd, 3, tolerance = 0.5) + expect_equal(result$formula_scale$beta_x2$mean, -5, tolerance = 0.5) + expect_equal(result$formula_scale$beta_x2$sd, 2, tolerance = 0.5) + + ### Standardize one predictors + result <- JAGS_formula( + formula = ~ x1 + x2, + parameter = "beta", + data = df, + prior_list = prior_list, + formula_scale = list(x1 = FALSE, x2 = TRUE) + ) + + # Check that both predictors are standardized + expect_length(result$formula_scale, 1) + expect_true(!"beta_x1" %in% names(result$formula_scale)) + expect_true("beta_x2" %in% names(result$formula_scale)) + + # Verify scale parameters + expect_equal(result$formula_scale$beta_x2$mean, -5, tolerance = 0.5) + expect_equal(result$formula_scale$beta_x2$sd, 2, tolerance = 0.5) + expect_equal(unname(result$data$beta_data_x1), as.numeric(df$x1)) + expect_equal(unname(result$data$beta_data_x2), as.numeric(scale(df$x2))) +}) + +test_that("transform_scale_samples transforms coefficients correctly", { + + # Create mock posterior samples + set.seed(3) + n_samples <- 100 + + # Simulated standardized coefficients + posterior <- matrix( + c( + rnorm(n_samples, mean = 0.5, sd = 0.1), # mu_intercept + rnorm(n_samples, mean = 0.3, sd = 0.05), # mu_x_cont (standardized) + rnorm(n_samples, mean = 0.2, sd = 0.05) # mu_x_fac (not standardized) + ), + nrow = n_samples, + ncol = 3 + ) + colnames(posterior) <- c("mu_intercept", "mu_x_cont", "mu_x_fac") + + # Scale information (x_cont was standardized with mean=5, sd=2) + # Use nested structure keyed by parameter name + formula_scale <- list( + mu = list( + mu_x_cont = list(mean = 5, sd = 2) + ) + ) + + # Transform back to original scale + posterior_original <- transform_scale_samples(posterior, formula_scale) + + # Check that x_cont coefficient is rescaled (divided by sd) + expect_equal(posterior_original[, "mu_x_cont"], posterior[, "mu_x_cont"] / 2) + + # Check that x_fac is unchanged (not in formula_scale) + expect_equal(posterior_original[, "mu_x_fac"], posterior[, "mu_x_fac"]) + + # Check that intercept is adjusted + # intercept_original = intercept_std - beta_original * mean + # where beta_original = beta_std / sd (already done above) + expected_intercept <- posterior[, "mu_intercept"] - (posterior[, "mu_x_cont"] / 2 * 5) + expect_equal(posterior_original[, "mu_intercept"], expected_intercept) +}) + +test_that("transform_scale_samples handles interaction terms correctly", { + + # Create mock posterior samples with interaction + set.seed(4) + n_samples <- 100 + + # Simulated standardized coefficients + posterior <- matrix( + c( + rnorm(n_samples, mean = 1.0, sd = 0.1), # mu_intercept + rnorm(n_samples, mean = 0.3, sd = 0.05), # mu_x1 (standardized) + rnorm(n_samples, mean = 0.2, sd = 0.05), # mu_x2 (standardized) + rnorm(n_samples, mean = 0.1, sd = 0.02) # mu_x1__xXx__x2 (interaction) + ), + nrow = n_samples, + ncol = 4 + ) + colnames(posterior) <- c("mu_intercept", "mu_x1", "mu_x2", "mu_x1__xXx__x2") + + # Scale information - use nested structure keyed by parameter name + formula_scale <- list( + mu = list( + mu_x1 = list(mean = 5, sd = 2), + mu_x2 = list(mean = 10, sd = 4) + ) + ) + + # Transform back to original scale + posterior_original <- transform_scale_samples(posterior, formula_scale) + + # The interaction coefficient should be divided by (sd_x1 * sd_x2) = 2 * 4 = 8 + expect_equal( + posterior_original[, "mu_x1__xXx__x2"], + posterior[, "mu_x1__xXx__x2"] / (2 * 4), + tolerance = 1e-10 + ) + + # The main effect x1 should be: beta_x1_orig = beta_x1_z/sd_x1 - beta_int_orig * mean_x2 + beta_int_orig <- posterior[, "mu_x1__xXx__x2"] / 8 + beta_x1_z_div_sd <- posterior[, "mu_x1"] / 2 + expected_beta_x1 <- beta_x1_z_div_sd - beta_int_orig * 10 + expect_equal(posterior_original[, "mu_x1"], expected_beta_x1, tolerance = 1e-10) + + # The main effect x2 should be: beta_x2_orig = beta_x2_z/sd_x2 - beta_int_orig * mean_x1 + beta_x2_z_div_sd <- posterior[, "mu_x2"] / 4 + expected_beta_x2 <- beta_x2_z_div_sd - beta_int_orig * 5 + expect_equal(posterior_original[, "mu_x2"], expected_beta_x2, tolerance = 1e-10) + + # The intercept should be: + # alpha_orig = alpha_z - (beta_x1_z/sd_x1)*mean_x1 - (beta_x2_z/sd_x2)*mean_x2 + beta_int_orig*mean_x1*mean_x2 + # Note: uses beta_z/sd (intermediate values), not beta_orig (interaction-adjusted values) + expected_intercept <- posterior[, "mu_intercept"] - + beta_x1_z_div_sd * 5 - beta_x2_z_div_sd * 10 + beta_int_orig * 5 * 10 + expect_equal(posterior_original[, "mu_intercept"], expected_intercept, tolerance = 1e-10) +}) + +test_that("Manual and automatic scaling produce equivalent results", { + + skip_if_no_fits() + + # Load pre-fitted models + fit_manual <- readRDS(file.path(temp_fits_dir, "fit_formula_manual_scaled.RDS")) + fit_auto <- readRDS(file.path(temp_fits_dir, "fit_formula_auto_scaled.RDS")) + + # Check that automatic scaling has formula_scale attribute with nested structure + expect_true(!is.null(attr(fit_auto, "formula_scale"))) + expect_true("mu" %in% names(attr(fit_auto, "formula_scale"))) + expect_true("mu_x_cont1" %in% names(attr(fit_auto, "formula_scale")$mu)) + expect_true("mu_x_cont2" %in% names(attr(fit_auto, "formula_scale")$mu)) + + # Check that manual scaling has the scale info stored + expect_true(!is.null(attr(fit_manual, "manual_scale"))) + + # Compare scaling parameters + # The automatic and manual scaling should have stored the same mean/sd + manual_scale <- attr(fit_manual, "manual_scale") + auto_scale <- attr(fit_auto, "formula_scale")$mu + + expect_equal(manual_scale$mu_x_cont1$mean, auto_scale$mu_x_cont1$mean, tolerance = 1e-10) + expect_equal(manual_scale$mu_x_cont1$sd, auto_scale$mu_x_cont1$sd, tolerance = 1e-10) + expect_equal(manual_scale$mu_x_cont2$mean, auto_scale$mu_x_cont2$mean, tolerance = 1e-10) + expect_equal(manual_scale$mu_x_cont2$sd, auto_scale$mu_x_cont2$sd, tolerance = 1e-10) + + # Extract posterior samples + posterior_manual <- as.matrix(fit_manual$mcmc[[1]]) + posterior_auto <- as.matrix(fit_auto$mcmc[[1]]) + + # The raw posterior samples should be very similar (both are on scaled space) + # since both models were fit with the same seed and same scaled data + + # Compare means of main effects + mean_manual_x1 <- mean(posterior_manual[, "mu_x_cont1"]) + mean_auto_x1 <- mean(posterior_auto[, "mu_x_cont1"]) + + mean_manual_x2 <- mean(posterior_manual[, "mu_x_cont2"]) + mean_auto_x2 <- mean(posterior_auto[, "mu_x_cont2"]) + + mean_manual_interaction <- mean(posterior_manual[, "mu_x_cont1__xXx__x_cont2"]) + mean_auto_interaction <- mean(posterior_auto[, "mu_x_cont1__xXx__x_cont2"]) + + # These should be very close since both use scaled data + expect_equal(mean_manual_x1, mean_auto_x1) + expect_equal(mean_manual_x2, mean_auto_x2) + expect_equal(mean_manual_interaction, mean_auto_interaction) + + # Compare standard deviations + sd_manual_x1 <- sd(posterior_manual[, "mu_x_cont1"]) + sd_auto_x1 <- sd(posterior_auto[, "mu_x_cont1"]) + + sd_manual_x2 <- sd(posterior_manual[, "mu_x_cont2"]) + sd_auto_x2 <- sd(posterior_auto[, "mu_x_cont2"]) + + sd_manual_interaction <- sd(posterior_manual[, "mu_x_cont1__xXx__x_cont2"]) + sd_auto_interaction <- sd(posterior_auto[, "mu_x_cont1__xXx__x_cont2"]) + + expect_equal(sd_manual_x1, sd_auto_x1) + expect_equal(sd_manual_x2, sd_auto_x2) + expect_equal(sd_manual_interaction, sd_auto_interaction) + + # Compare intercepts (these should also be similar) + mean_manual_int <- mean(posterior_manual[, "mu_intercept"]) + mean_auto_int <- mean(posterior_auto[, "mu_intercept"]) + + expect_equal(mean_manual_int, mean_auto_int, tolerance = 0.05) +}) + +test_that("Downstream functions work with scaled models", { + + skip_if_no_fits() + + # Load pre-fitted models + fit_manual <- readRDS(file.path(temp_fits_dir, "fit_formula_manual_scaled.RDS")) + fit_auto <- readRDS(file.path(temp_fits_dir, "fit_formula_auto_scaled.RDS")) + + expect_equal(JAGS_estimates_table(fit_manual), JAGS_estimates_table(fit_auto)) +}) + +test_that("Marginal likelihoods match for manual and automatic scaling", { + + skip_if_no_fits() + skip("no margliks") + # Load pre-fitted marginal likelihoods + marglik_manual <- readRDS(file.path(temp_fits_dir, "fit_formula_manual_scaled_marglik.RDS")) + marglik_auto <- readRDS(file.path(temp_fits_dir, "fit_formula_auto_scaled_marglik.RDS")) + + # The log marginal likelihoods should be very similar + # (both models use same scaled data internally) + expect_equal(marglik_manual$logml, marglik_auto$logml, tolerance = 0.1) +}) + +test_that("JAGS_evaluate_formula applies scaling correctly", { + + skip_if_no_fits() + + # Load pre-fitted models with scaling + fit_manual <- readRDS(file.path(temp_fits_dir, "fit_formula_manual_scaled.RDS")) + fit_auto <- readRDS(file.path(temp_fits_dir, "fit_formula_auto_scaled.RDS")) + + # Create new data with same scale as original (unscaled) + set.seed(3) + new_data <- data.frame( + x_cont1 = rnorm(10, mean = 1000, sd = 1000), + x_cont2 = rnorm(10, mean = 0.5, sd = 0.1) + ) + + # Get prior lists from fit attributes + prior_list_auto <- attr(fit_auto, "prior_list") + prior_list_manual <- attr(fit_manual, "prior_list") + + # For manual scaling, we need to manually scale the new data + manual_scale <- attr(fit_manual, "manual_scale") + new_data_manual <- new_data + new_data_manual$x_cont1 <- (new_data$x_cont1 - manual_scale$mu_x_cont1$mean) / manual_scale$mu_x_cont1$sd + new_data_manual$x_cont2 <- (new_data$x_cont2 - manual_scale$mu_x_cont2$mean) / manual_scale$mu_x_cont2$sd + + # For automatic scaling, JAGS_evaluate_formula should apply scaling automatically + # (using the formula_scale attribute from fit_auto) + + # Evaluate formula on new data + pred_manual <- JAGS_evaluate_formula( + fit = fit_manual, + formula = ~ x_cont1 * x_cont2, + parameter = "mu", + data = new_data_manual, + prior_list = prior_list_manual + ) + + pred_auto <- JAGS_evaluate_formula( + fit = fit_auto, + formula = ~ x_cont1 * x_cont2, + parameter = "mu", + data = new_data, # Note: passing unscaled data + prior_list = prior_list_auto + ) + + # The predictions should be very similar + # (both models use same scaled data internally, and seed) + expect_equal(pred_manual, pred_auto) + + # Also check that without scaling, predictions would be different + # (this verifies that scaling is actually being applied) + pred_auto_no_scale <- JAGS_evaluate_formula( + fit = fit_manual, # Use manual fit which doesn't have formula_scale attribute + formula = ~ x_cont1 * x_cont2, + parameter = "mu", + data = new_data, # Unscaled data + prior_list = prior_list_manual + ) + + # These should be very different from the correctly scaled predictions + expect_true(any(rowMeans(pred_manual) - rowMeans(pred_auto_no_scale) > 1)) +}) + +test_that("runjags_estimates_table with transform_scaled unscales coefficients", { + # TODO: something is wrong here with the intercept handling + skip_if_no_fits() + + # Load pre-fitted model with automatic scaling + fit_auto <- readRDS(file.path(temp_fits_dir, "fit_formula_auto_scaled.RDS")) + + # Get formula_scale attribute + formula_scale <- attr(fit_auto, "formula_scale") + expect_true(!is.null(formula_scale)) + + # Get estimates without unscaling + estimates_scaled <- JAGS_estimates_table(fit_auto, transform_scaled = FALSE) + + # Get estimates with unscaling + estimates_unscaled <- JAGS_estimates_table(fit_auto, transform_scaled = TRUE) + + # The scaled coefficient for x_cont1 should be divided by sd + # to get the unscaled coefficient (nested structure: formula_scale$mu$...) + sd_x_cont1 <- formula_scale$mu$mu_x_cont1$sd + sd_x_cont2 <- formula_scale$mu$mu_x_cont2$sd + mean_x_cont1 <- formula_scale$mu$mu_x_cont1$mean + mean_x_cont2 <- formula_scale$mu$mu_x_cont2$mean + + # Check that the interaction term is correctly unscaled (divided by product of SDs) + scaled_coef_int <- estimates_scaled["(mu) x_cont1:x_cont2", "Mean"] + unscaled_coef_int <- estimates_unscaled["(mu) x_cont1:x_cont2", "Mean"] + expect_equal(unscaled_coef_int, scaled_coef_int / (sd_x_cont1 * sd_x_cont2), tolerance = 1e-10) + + # The main effects are adjusted for interaction contributions + # beta_x1_orig = beta_x1_z/sd_x1 - beta_int_orig * mean_x2 + scaled_coef_x1 <- estimates_scaled["(mu) x_cont1", "Mean"] + expected_x1 <- scaled_coef_x1 / sd_x_cont1 - unscaled_coef_int * mean_x_cont2 + expect_equal(estimates_unscaled["(mu) x_cont1", "Mean"], expected_x1, tolerance = 1e-10) + + # beta_x2_orig = beta_x2_z/sd_x2 - beta_int_orig * mean_x1 + scaled_coef_x2 <- estimates_scaled["(mu) x_cont2", "Mean"] + expected_x2 <- scaled_coef_x2 / sd_x_cont2 - unscaled_coef_int * mean_x_cont1 + expect_equal(estimates_unscaled["(mu) x_cont2", "Mean"], expected_x2, tolerance = 1e-10) + + # The intercept should be adjusted + # alpha_orig = alpha_z - beta_x1_orig*mean_x1 - beta_x2_orig*mean_x2 - beta_int_orig*mean_x1*mean_x2 + scaled_intercept <- estimates_scaled["(mu) intercept", "Mean"] + expected_intercept <- scaled_intercept - expected_x1 * mean_x_cont1 - expected_x2 * mean_x_cont2 - + unscaled_coef_int * mean_x_cont1 * mean_x_cont2 + expect_equal(estimates_unscaled["(mu) intercept", "Mean"], expected_intercept, tolerance = 1e-10) +}) + +test_that("runjags_estimates_table transform_scaled with return_samples works", { + + skip_if_no_fits() + + fit_auto <- readRDS(file.path(temp_fits_dir, "fit_formula_auto_scaled.RDS")) + formula_scale <- attr(fit_auto, "formula_scale") + + # Get samples without unscaling + samples_scaled <- JAGS_estimates_table(fit_auto, transform_scaled = FALSE, return_samples = TRUE) + + # Get samples with unscaling + samples_unscaled <- JAGS_estimates_table(fit_auto, transform_scaled = TRUE, return_samples = TRUE) + + # For models with interactions, the transformation is more complex (nested structure) + sd_x_cont1 <- formula_scale$mu$mu_x_cont1$sd + sd_x_cont2 <- formula_scale$mu$mu_x_cont2$sd + mean_x_cont1 <- formula_scale$mu$mu_x_cont1$mean + mean_x_cont2 <- formula_scale$mu$mu_x_cont2$mean + + # First, compute the unscaled interaction coefficient + unscaled_int <- samples_scaled[, "(mu) x_cont1:x_cont2"] / (sd_x_cont1 * sd_x_cont2) + + # Check that x_cont1 samples are correctly unscaled (with interaction adjustment) + # beta_x1_orig = beta_x1_z/sd_x1 - beta_int_orig * mean_x2 + expected_x1 <- samples_scaled[, "(mu) x_cont1"] / sd_x_cont1 - unscaled_int * mean_x_cont2 + expect_equal( + samples_unscaled[, "(mu) x_cont1"], + expected_x1, + tolerance = 1e-10 + ) +}) + +test_that("ensemble_estimates_table with transform_scaled unscales coefficients", { + + skip_if_no_fits() + skip_if_not_installed("bridgesampling") + + # Load pre-fitted models + fit_auto <- readRDS(file.path(temp_fits_dir, "fit_formula_auto_scaled.RDS")) + marglik_auto <- structure(list(logml = -20), class = "bridge") + + formula_scale <- attr(fit_auto, "formula_scale") + + # Create a simple model list for mix_posteriors + model_list <- list( + list( + fit = fit_auto, + marglik = marglik_auto, + prior_weights = 1 + ) + ) + + # Get mixed posteriors - include interaction term for proper unscaling + mixed_posteriors <- mix_posteriors( + model_list = model_list, + parameters = c("mu_intercept", "mu_x_cont1", "mu_x_cont2", "mu_x_cont1__xXx__x_cont2"), + is_null_list = list( + mu_intercept = 1, + mu_x_cont1 = 1, + mu_x_cont2 = 1, + "mu_x_cont1__xXx__x_cont2" = 1 + ), + seed = 1 + ) + + # Get estimates without unscaling + estimates_scaled <- ensemble_estimates_table( + samples = mixed_posteriors, + parameters = c("mu_intercept", "mu_x_cont1", "mu_x_cont2", "mu_x_cont1__xXx__x_cont2"), + transform_scaled = FALSE + ) + + # Get estimates with unscaling + estimates_unscaled <- ensemble_estimates_table( + samples = mixed_posteriors, + parameters = c("mu_intercept", "mu_x_cont1", "mu_x_cont2", "mu_x_cont1__xXx__x_cont2"), + transform_scaled = TRUE, + formula_scale = formula_scale + ) + + # For models with interactions, the transformation is more complex (nested structure) + sd_x_cont1 <- formula_scale$mu$mu_x_cont1$sd + sd_x_cont2 <- formula_scale$mu$mu_x_cont2$sd + mean_x_cont1 <- formula_scale$mu$mu_x_cont1$mean + mean_x_cont2 <- formula_scale$mu$mu_x_cont2$mean + + # Check that the interaction term is correctly unscaled (divided by product of SDs) + scaled_coef_int <- estimates_scaled["(mu) x_cont1:x_cont2", "Mean"] + unscaled_coef_int <- estimates_unscaled["(mu) x_cont1:x_cont2", "Mean"] + expect_equal(unscaled_coef_int, scaled_coef_int / (sd_x_cont1 * sd_x_cont2), tolerance = 1e-10) + + # The main effects are adjusted for interaction contributions + # beta_x1_orig = beta_x1_z/sd_x1 - beta_int_orig * mean_x2 + scaled_coef_x1 <- estimates_scaled["mu_x_cont1", "Mean"] + expected_x1 <- scaled_coef_x1 / sd_x_cont1 - unscaled_coef_int * mean_x_cont2 + expect_equal(estimates_unscaled["mu_x_cont1", "Mean"], expected_x1, tolerance = 1e-10) + + # beta_x2_orig = beta_x2_z/sd_x2 - beta_int_orig * mean_x1 + scaled_coef_x2 <- estimates_scaled["mu_x_cont2", "Mean"] + expected_x2 <- scaled_coef_x2 / sd_x_cont2 - unscaled_coef_int * mean_x_cont1 + expect_equal(estimates_unscaled["mu_x_cont2", "Mean"], expected_x2, tolerance = 1e-10) +}) + +test_that("transform_scaled = FALSE is the default behavior", { + + skip_if_no_fits() + + fit_auto <- readRDS(file.path(temp_fits_dir, "fit_formula_auto_scaled.RDS")) + + # Default behavior should be no unscaling + estimates_default <- JAGS_estimates_table(fit_auto) + estimates_false <- JAGS_estimates_table(fit_auto, transform_scaled = FALSE) + + expect_equal(estimates_default, estimates_false) +}) + +test_that("transform_scaled has no effect when formula_scale is NULL", { + + skip_if_no_fits() + + # Load model without automatic scaling (manual scaling doesn't have formula_scale attr) + fit_manual <- readRDS(file.path(temp_fits_dir, "fit_formula_manual_scaled.RDS")) + + # transform_scaled = TRUE should have no effect when formula_scale is NULL + estimates_false <- JAGS_estimates_table(fit_manual, transform_scaled = FALSE) + estimates_true <- JAGS_estimates_table(fit_manual, transform_scaled = TRUE) + + expect_equal(estimates_false, estimates_true) +}) + + +# ============================================================================ # +# DUAL PARAMETER REGRESSION WITH LOG(INTERCEPT) TESTS +# ============================================================================ # + +test_that("Dual parameter model with log(intercept) has correct formula_scale structure", { + + skip_if_no_fits() + + # Load pre-fitted dual parameter regression model + fit_dual <- readRDS(file.path(temp_fits_dir, "fit_dual_param_regression.RDS")) + + # Check that formula_scale attribute exists + formula_scale <- attr(fit_dual, "formula_scale") + expect_true(!is.null(formula_scale)) + + + # Check that both parameters have scaling info + expect_true("mu" %in% names(formula_scale)) + expect_true("log_sigma" %in% names(formula_scale)) + + # Check nested structure + expect_true("mu_x_mu" %in% names(formula_scale$mu)) + expect_true("log_sigma_x_sigma" %in% names(formula_scale$log_sigma)) + + # Verify scale info structure for mu parameter + expect_equal(names(formula_scale$mu$mu_x_mu), c("mean", "sd")) + expect_true(is.numeric(formula_scale$mu$mu_x_mu$mean)) + expect_true(is.numeric(formula_scale$mu$mu_x_mu$sd)) + + # Verify scale info structure for log_sigma parameter + expect_equal(names(formula_scale$log_sigma$log_sigma_x_sigma), c("mean", "sd")) + expect_true(is.numeric(formula_scale$log_sigma$log_sigma_x_sigma$mean)) + expect_true(is.numeric(formula_scale$log_sigma$log_sigma_x_sigma$sd)) + + # Verify log_intercept attribute is stored correctly + # mu should NOT have log_intercept (or be FALSE) + expect_false(isTRUE(attr(formula_scale$mu, "log_intercept"))) + # log_sigma SHOULD have log_intercept = TRUE + expect_true(isTRUE(attr(formula_scale$log_sigma, "log_intercept"))) + + # Verify the model has expected parameters + param_names <- colnames(fit_dual$mcmc[[1]]) + expect_true("mu_intercept" %in% param_names) + expect_true("mu_x_mu" %in% param_names) + expect_true("log_sigma_intercept" %in% param_names) + expect_true("log_sigma_x_sigma" %in% param_names) +}) + +test_that("transform_scale_samples works with dual parameter model", { + + skip_if_no_fits() + + # Load pre-fitted dual parameter regression model + fit_dual <- readRDS(file.path(temp_fits_dir, "fit_dual_param_regression.RDS")) + formula_scale <- attr(fit_dual, "formula_scale") + + # Extract posterior samples + posterior <- as.matrix(fit_dual$mcmc[[1]]) + + # Transform to original scale + posterior_transformed <- transform_scale_samples(posterior, formula_scale) + + # Get scale parameters (nested structure) + mu_scale <- formula_scale$mu$mu_x_mu + log_sigma_scale <- formula_scale$log_sigma$log_sigma_x_sigma + + # Check mu_x_mu coefficient is correctly unscaled (divided by sd) + expected_mu_x_mu <- posterior[, "mu_x_mu"] / mu_scale$sd + expect_equal(posterior_transformed[, "mu_x_mu"], expected_mu_x_mu, tolerance = 1e-10) + + # Check log_sigma_x_sigma coefficient is correctly unscaled (divided by sd) + expected_log_sigma_x_sigma <- posterior[, "log_sigma_x_sigma"] / log_sigma_scale$sd + expect_equal(posterior_transformed[, "log_sigma_x_sigma"], expected_log_sigma_x_sigma, tolerance = 1e-10) + + # Check mu intercept is adjusted: intercept_orig = intercept_z - beta_orig * mean + expected_mu_intercept <- posterior[, "mu_intercept"] - expected_mu_x_mu * mu_scale$mean + expect_equal(posterior_transformed[, "mu_intercept"], expected_mu_intercept, tolerance = 1e-10) + + # Check log_sigma intercept is adjusted with multiplicative transformation (due to log(intercept)): + # intercept_orig = intercept_z * exp(-beta_orig * mean) + expected_log_sigma_intercept <- posterior[, "log_sigma_intercept"] * exp(-expected_log_sigma_x_sigma * log_sigma_scale$mean) + expect_equal(posterior_transformed[, "log_sigma_intercept"], expected_log_sigma_intercept, tolerance = 1e-10) +}) + +test_that("JAGS_estimates_table with transform_scaled works for dual parameter model", { + + skip_if_no_fits() + + # Load pre-fitted dual parameter regression model + fit_dual <- readRDS(file.path(temp_fits_dir, "fit_dual_param_regression.RDS")) + formula_scale <- attr(fit_dual, "formula_scale") + + # Get estimates without unscaling + estimates_scaled <- JAGS_estimates_table(fit_dual, transform_scaled = FALSE) + + # Get estimates with unscaling + estimates_unscaled <- JAGS_estimates_table(fit_dual, transform_scaled = TRUE) + + # Get scale parameters (nested structure) + mu_sd <- formula_scale$mu$mu_x_mu$sd + mu_mean <- formula_scale$mu$mu_x_mu$mean + log_sigma_sd <- formula_scale$log_sigma$log_sigma_x_sigma$sd + log_sigma_mean <- formula_scale$log_sigma$log_sigma_x_sigma$mean + + # Check mu_x_mu coefficient is correctly unscaled + scaled_mu_coef <- estimates_scaled["(mu) x_mu", "Mean"] + unscaled_mu_coef <- estimates_unscaled["(mu) x_mu", "Mean"] + expect_equal(unscaled_mu_coef, scaled_mu_coef / mu_sd, tolerance = 1e-10) + + # Check log_sigma_x_sigma coefficient is correctly unscaled + scaled_log_sigma_coef <- estimates_scaled["(log_sigma) x_sigma", "Mean"] + unscaled_log_sigma_coef <- estimates_unscaled["(log_sigma) x_sigma", "Mean"] + expect_equal(unscaled_log_sigma_coef, scaled_log_sigma_coef / log_sigma_sd, tolerance = 1e-10) + + # Check mu intercept is correctly adjusted + scaled_mu_int <- estimates_scaled["(mu) intercept", "Mean"] + expected_mu_int <- scaled_mu_int - unscaled_mu_coef * mu_mean + expect_equal(estimates_unscaled["(mu) intercept", "Mean"], expected_mu_int, tolerance = 1e-10) + + # Check log_sigma intercept is correctly adjusted with multiplicative transformation + # Due to log(intercept): intercept_orig = intercept_z * exp(-beta_orig * mean) + # For means, we can't use the simple relationship because E[X * exp(Y)] != E[X] * exp(E[Y]) + # Instead, verify that the unscaled intercept is close to the true value (0.5) + # and that it differs from the scaled intercept (which would be biased) + # Note: with transform_scaled=TRUE, the intercept is renamed to exp(intercept) + unscaled_log_sigma_int <- estimates_unscaled["(log_sigma) exp(intercept)", "Mean"] + + # The unscaled intercept should be reasonably close to the true value of 0.5 + expect_true(abs(unscaled_log_sigma_int - 0.5) < 0.15) + + # The scaled intercept should NOT be close to 0.5 (it's on the wrong scale) + scaled_log_sigma_int <- estimates_scaled["(log_sigma) intercept", "Mean"] + expect_true(abs(scaled_log_sigma_int - 0.5) > abs(unscaled_log_sigma_int - 0.5)) +}) + +test_that("JAGS_evaluate_formula applies scaling correctly for dual parameter model", { + + skip_if_no_fits() + + # Load pre-fitted dual parameter regression model + fit_dual <- readRDS(file.path(temp_fits_dir, "fit_dual_param_regression.RDS")) + formula_scale <- attr(fit_dual, "formula_scale") + prior_list <- attr(fit_dual, "prior_list") + + # Create new data (on original unscaled scale) + set.seed(123) + new_data_mu <- data.frame(x_mu = rnorm(5, mean = 5, sd = 2)) + new_data_sigma <- data.frame(x_sigma = rnorm(5, mean = 3, sd = 1.5)) + + # Evaluate mu formula (standard intercept) + pred_mu <- JAGS_evaluate_formula( + fit = fit_dual, + formula = ~ x_mu, + parameter = "mu", + data = new_data_mu, + prior_list = prior_list + ) + + # Evaluate log_sigma formula (log intercept) + formula_log_sigma <- ~ x_sigma + attr(formula_log_sigma, "log(intercept)") <- TRUE + + pred_log_sigma <- JAGS_evaluate_formula( + fit = fit_dual, + formula = formula_log_sigma, + parameter = "log_sigma", + data = new_data_sigma, + prior_list = prior_list + ) + + # Basic sanity checks + expect_equal(nrow(pred_mu), 5) + expect_equal(nrow(pred_log_sigma), 5) + + # The predictions should be matrices with n_samples columns + expect_true(ncol(pred_mu) > 1) + expect_true(ncol(pred_log_sigma) > 1) + + # Verify manually: predictions should match manual calculation + posterior <- as.matrix(coda::as.mcmc.list(fit_dual)) + mu_scale <- formula_scale$mu$mu_x_mu + log_sigma_scale <- formula_scale$log_sigma$log_sigma_x_sigma + + # Scale the new data as the function should do internally + x_mu_scaled <- (new_data_mu$x_mu - mu_scale$mean) / mu_scale$sd + x_sigma_scaled <- (new_data_sigma$x_sigma - log_sigma_scale$mean) / log_sigma_scale$sd + + # For first observation in new_data_mu + # mu[i] = intercept + x_mu * x_mu_scaled[i] + expected_mu_1 <- posterior[, "mu_intercept"] + posterior[, "mu_x_mu"] * x_mu_scaled[1] + expect_equal(pred_mu[1, ], expected_mu_1, tolerance = 1e-10) + + # For first observation in new_data_sigma (with log intercept) + # log_sigma[i] = log(intercept) + x_sigma * x_sigma_scaled[i] + expected_log_sigma_1 <- log(posterior[, "log_sigma_intercept"]) + posterior[, "log_sigma_x_sigma"] * x_sigma_scaled[1] + expect_equal(pred_log_sigma[1, ], expected_log_sigma_1, tolerance = 1e-10) +}) + + +# ============================================================================ # +# LM-BASED VALIDATION TESTS +# ============================================================================ # +# +# These tests validate the unscaling transformation by comparing against lm(): +# 1. Fit lm() with scaled predictors -> extract coefficients +# 2. Transform coefficients using transform_scale_samples() +# 3. Compare against lm() with unscaled predictors +# +# This approach validates both the implementation AND the derivation. +# ============================================================================ # + +# Helper: Create formula_scale from data frame and variable names +# Creates nested structure matching JAGS_fit output: list(mu = list(mu_x1 = list(mean, sd))) +.make_formula_scale <- function(df, var_names, prefix = "mu") { + param_scale <- list() + for (var in var_names) { + param_name <- paste0(prefix, "_", var) + param_scale[[param_name]] <- list( + mean = mean(df[[var]]), + sd = sd(df[[var]]) + ) + } + # Return nested structure keyed by parameter name + result <- list() + result[[prefix]] <- param_scale + result +} + +# Helper: Convert lm coefficients to posterior matrix format (repeated rows) +# Uses the same naming convention as JAGS (__xXx__ for interactions) +.lm_coefs_to_posterior <- function(coefs, prefix = "mu", n_rep = 10) { + # Convert names: "(Intercept)" -> "mu_intercept", "x1:x2" -> "mu_x1__xXx__x2" + new_names <- names(coefs) + new_names <- gsub("\\(Intercept\\)", "intercept", new_names) + new_names <- gsub(":", "__xXx__", new_names) + new_names <- paste0(prefix, "_", new_names) + + # Remove scale() wrapper from names if present + new_names <- gsub("scale\\(([^)]+)\\)", "\\1", new_names) + + posterior <- matrix(rep(coefs, each = n_rep), nrow = n_rep, ncol = length(coefs)) + colnames(posterior) <- new_names + posterior +} + +# Helper to reorder lm coefficients to match posterior column order +.reorder_lm_coefs <- function(coef_unscaled, posterior_transformed) { + # Build mapping from posterior names to lm names + posterior_names <- colnames(posterior_transformed) + lm_names <- sapply(posterior_names, function(nm) { + # Remove mu_ prefix + stripped <- sub("^mu_", "", nm) + if (stripped == "intercept") return("(Intercept)") + # Replace __xXx__ with : + gsub("__xXx__", ":", stripped) + }) + coef_unscaled[lm_names] +} + + +test_that("lm validation: simple standardization (one predictor)", { + + set.seed(42) + df <- data.frame( + x1 = rnorm(500, mean = 10, sd = 3), + y = rnorm(500) + ) + df$y <- 5 + 2 * scale(df$x1) + rnorm(500, 0, 0.5) + + # Fit with scaled predictor + fit_scaled <- lm(y ~ scale(x1), data = df) + coef_scaled <- coef(fit_scaled) + + # Fit with unscaled predictor (ground truth) + fit_unscaled <- lm(y ~ x1, data = df) + coef_unscaled <- coef(fit_unscaled) + + # Transform scaled coefficients + posterior_scaled <- .lm_coefs_to_posterior(coef_scaled) + formula_scale <- .make_formula_scale(df, "x1") + posterior_transformed <- transform_scale_samples(posterior_scaled, formula_scale) + + # Compare + expect_equal( + unname(posterior_transformed[1, ]), + unname(.reorder_lm_coefs(coef_unscaled, posterior_transformed)), + tolerance = 1e-10 + ) +}) + + +test_that("lm validation: multiple predictors (no interaction)", { + + set.seed(43) + df <- data.frame( + x1 = rnorm(500, mean = 3, sd = 5), + x2 = rnorm(500, mean = -10, sd = 2) + ) + df$y <- 2 - 0.5 * scale(df$x1) + 1.5 * scale(df$x2) + rnorm(500, 0, 0.3) + + # Fit with scaled predictors + fit_scaled <- lm(y ~ scale(x1) + scale(x2), data = df) + coef_scaled <- coef(fit_scaled) + + # Fit with unscaled predictors (ground truth) + fit_unscaled <- lm(y ~ x1 + x2, data = df) + coef_unscaled <- coef(fit_unscaled) + + # Transform + posterior_scaled <- .lm_coefs_to_posterior(coef_scaled) + formula_scale <- .make_formula_scale(df, c("x1", "x2")) + posterior_transformed <- transform_scale_samples(posterior_scaled, formula_scale) + + # Compare + expect_equal( + unname(posterior_transformed[1, ]), + unname(.reorder_lm_coefs(coef_unscaled, posterior_transformed)), + tolerance = 1e-10 + ) +}) + + +test_that("lm validation: two-way interaction (both scaled)", { + + set.seed(44) + df <- data.frame( + x1 = rnorm(500, mean = 5, sd = 2), + x2 = rnorm(500, mean = -3, sd = 4) + ) + df$y <- 3 + 0.8 * scale(df$x1) - 0.5 * scale(df$x2) + + 0.3 * scale(df$x1) * scale(df$x2) + rnorm(500, 0, 0.5) + + # Fit with scaled predictors + fit_scaled <- lm(y ~ scale(x1) * scale(x2), data = df) + coef_scaled <- coef(fit_scaled) + + # Fit with unscaled predictors (ground truth) + fit_unscaled <- lm(y ~ x1 * x2, data = df) + coef_unscaled <- coef(fit_unscaled) + + # Transform + posterior_scaled <- .lm_coefs_to_posterior(coef_scaled) + formula_scale <- .make_formula_scale(df, c("x1", "x2")) + posterior_transformed <- transform_scale_samples(posterior_scaled, formula_scale) + + # Compare all coefficients + expect_equal( + unname(posterior_transformed[1, ]), + unname(.reorder_lm_coefs(coef_unscaled, posterior_transformed)), + tolerance = 1e-10 + ) +}) + + +test_that("lm validation: two-way interaction (partial scaling)", { + + set.seed(45) + df <- data.frame( + x1 = rnorm(500, mean = 8, sd = 3), + x2 = rnorm(500, mean = -2, sd = 5) + ) + # Only x1 is scaled + df$y <- 1 + 0.6 * scale(df$x1) - 0.4 * df$x2 + + 0.25 * scale(df$x1) * df$x2 + rnorm(500, 0, 0.4) + + # Fit with partial scaling (only x1 scaled) + fit_scaled <- lm(y ~ scale(x1) * x2, data = df) + coef_scaled <- coef(fit_scaled) + + # Fit with unscaled predictors (ground truth) + fit_unscaled <- lm(y ~ x1 * x2, data = df) + coef_unscaled <- coef(fit_unscaled) + + # Transform - only x1 is in formula_scale + posterior_scaled <- .lm_coefs_to_posterior(coef_scaled) + formula_scale <- .make_formula_scale(df, "x1") # Only x1 scaled + posterior_transformed <- transform_scale_samples(posterior_scaled, formula_scale) + + # Compare + expect_equal( + unname(posterior_transformed[1, ]), + unname(.reorder_lm_coefs(coef_unscaled, posterior_transformed)), + tolerance = 1e-10 + ) +}) + + +test_that("lm validation: three-way interaction (all scaled)", { + + set.seed(46) + df <- data.frame( + x1 = rnorm(500, mean = 3, sd = 2), + x2 = rnorm(500, mean = -5, sd = 3), + x3 = rnorm(500, mean = 10, sd = 4) + ) + df$y <- 2 + + 0.5 * scale(df$x1) - 0.3 * scale(df$x2) + 0.4 * scale(df$x3) + + 0.2 * scale(df$x1) * scale(df$x2) + + 0.15 * scale(df$x1) * scale(df$x3) + + 0.1 * scale(df$x2) * scale(df$x3) + + 0.08 * scale(df$x1) * scale(df$x2) * scale(df$x3) + + rnorm(500, 0, 0.3) + + # Fit with scaled predictors + fit_scaled <- lm(y ~ scale(x1) * scale(x2) * scale(x3), data = df) + coef_scaled <- coef(fit_scaled) + + # Fit with unscaled predictors (ground truth) + fit_unscaled <- lm(y ~ x1 * x2 * x3, data = df) + coef_unscaled <- coef(fit_unscaled) + + # Transform + posterior_scaled <- .lm_coefs_to_posterior(coef_scaled) + formula_scale <- .make_formula_scale(df, c("x1", "x2", "x3")) + posterior_transformed <- transform_scale_samples(posterior_scaled, formula_scale) + + # Compare all coefficients + expect_equal( + unname(posterior_transformed[1, ]), + unname(.reorder_lm_coefs(coef_unscaled, posterior_transformed)), + tolerance = 1e-10 + ) +}) + + +test_that("lm validation: three-way interaction (partial scaling)", { + + set.seed(47) + df <- data.frame( + x1 = rnorm(500, mean = 4, sd = 2), + x2 = rnorm(500, mean = -3, sd = 3), + x3 = rnorm(500, mean = 7, sd = 1) # This one not scaled + ) + # x1 and x2 scaled, x3 not scaled + df$y <- 1 + + 0.4 * scale(df$x1) - 0.2 * scale(df$x2) + 0.3 * df$x3 + + 0.15 * scale(df$x1) * scale(df$x2) + + 0.12 * scale(df$x1) * df$x3 + + 0.08 * scale(df$x2) * df$x3 + + 0.05 * scale(df$x1) * scale(df$x2) * df$x3 + + rnorm(500, 0, 0.2) + + # Fit with partial scaling + fit_scaled <- lm(y ~ scale(x1) * scale(x2) * x3, data = df) + coef_scaled <- coef(fit_scaled) + + # Fit with unscaled predictors (ground truth) + fit_unscaled <- lm(y ~ x1 * x2 * x3, data = df) + coef_unscaled <- coef(fit_unscaled) + + # Transform - only x1 and x2 are scaled + posterior_scaled <- .lm_coefs_to_posterior(coef_scaled) + formula_scale <- .make_formula_scale(df, c("x1", "x2")) + posterior_transformed <- transform_scale_samples(posterior_scaled, formula_scale) + + # Compare all coefficients + expect_equal( + unname(posterior_transformed[1, ]), + unname(.reorder_lm_coefs(coef_unscaled, posterior_transformed)), + tolerance = 1e-10 + ) +}) + + +test_that("lm validation: four-way interaction", { + + set.seed(48) + df <- data.frame( + x1 = rnorm(1000, mean = 2, sd = 1), + x2 = rnorm(1000, mean = -4, sd = 2), + x3 = rnorm(1000, mean = 6, sd = 3), + x4 = rnorm(1000, mean = -1, sd = 0.5) + ) + # Complex model with 4-way interaction + df$y <- 3 + + 0.3 * scale(df$x1) - 0.2 * scale(df$x2) + + 0.4 * scale(df$x3) - 0.1 * scale(df$x4) + + 0.05 * scale(df$x1) * scale(df$x2) * scale(df$x3) * scale(df$x4) + + rnorm(1000, 0, 0.5) + + # Fit with scaled predictors + fit_scaled <- lm(y ~ scale(x1) * scale(x2) * scale(x3) * scale(x4), data = df) + coef_scaled <- coef(fit_scaled) + + # Fit with unscaled predictors (ground truth) + fit_unscaled <- lm(y ~ x1 * x2 * x3 * x4, data = df) + coef_unscaled <- coef(fit_unscaled) + + # Transform + posterior_scaled <- .lm_coefs_to_posterior(coef_scaled) + formula_scale <- .make_formula_scale(df, c("x1", "x2", "x3", "x4")) + posterior_transformed <- transform_scale_samples(posterior_scaled, formula_scale) + + # Compare all coefficients + expect_equal( + unname(posterior_transformed[1, ]), + unname(.reorder_lm_coefs(coef_unscaled, posterior_transformed)), + tolerance = 1e-10 + ) +}) + + +test_that("lm validation: five-way interaction (warning test)", { + + set.seed(49) + df <- data.frame( + x1 = rnorm(2000, mean = 1, sd = 0.5), + x2 = rnorm(2000, mean = -2, sd = 1), + x3 = rnorm(2000, mean = 3, sd = 1.5), + x4 = rnorm(2000, mean = -1, sd = 0.3), + x5 = rnorm(2000, mean = 4, sd = 2) + ) + df$y <- 2 + + 0.2 * scale(df$x1) - 0.1 * scale(df$x2) + + 0.3 * scale(df$x3) - 0.15 * scale(df$x4) + 0.1 * scale(df$x5) + + 0.02 * scale(df$x1) * scale(df$x2) * scale(df$x3) * scale(df$x4) * scale(df$x5) + + rnorm(2000, 0, 0.5) + + # Fit with scaled predictors + fit_scaled <- lm(y ~ scale(x1) * scale(x2) * scale(x3) * scale(x4) * scale(x5), data = df) + coef_scaled <- coef(fit_scaled) + + # Fit with unscaled predictors (ground truth) + fit_unscaled <- lm(y ~ x1 * x2 * x3 * x4 * x5, data = df) + coef_unscaled <- coef(fit_unscaled) + + # Transform - expect warning about 5+ way interaction + posterior_scaled <- .lm_coefs_to_posterior(coef_scaled) + formula_scale <- .make_formula_scale(df, c("x1", "x2", "x3", "x4", "x5")) + + expect_warning( + posterior_transformed <- transform_scale_samples(posterior_scaled, formula_scale), + "5-way or higher interactions" + ) + + # Should still produce correct results despite warning + expect_equal( + unname(posterior_transformed[1, ]), + unname(.reorder_lm_coefs(coef_unscaled, posterior_transformed)), + tolerance = 1e-10 + ) +}) + + +test_that("lm validation: complex model from user example", { + + # This is the exact example pattern from the user's request + set.seed(1) + df_orig <- data.frame( + x1 = rnorm(1000, mean = 3, sd = 5), + x2 = rnorm(1000, mean = -10, sd = 80), + x3 = rnorm(1000, mean = -20, sd = 0.07), + x4 = rnorm(1000, mean = 50, sd = 30), + x5 = rnorm(1000, mean = 20, sd = 0.2) + ) + + # DGP with specific structure + df_orig$y <- with( + df_orig, + 5 - 0.1 * scale(x1) + 0.2 * scale(x2) + 0.3 * scale(x1) * scale(x2) - + 0.25 * scale(x3) * scale(x4) * scale(x5) + 0.40 * scale(x3) * scale(x4) + + rnorm(1000, 0, 1) + ) + + # Fit the model with scaled predictors (matching DGP) + fit_scaled <- lm(y ~ scale(x1) * scale(x2) + scale(x3) * scale(x4) * scale(x5), data = df_orig) + coef_scaled <- coef(fit_scaled) + + # Fit with unscaled predictors (ground truth) + fit_unscaled <- lm(y ~ x1 * x2 + x3 * x4 * x5, data = df_orig) + coef_unscaled <- coef(fit_unscaled) + + # Transform + posterior_scaled <- .lm_coefs_to_posterior(coef_scaled) + formula_scale <- .make_formula_scale(df_orig, c("x1", "x2", "x3", "x4", "x5")) + posterior_transformed <- transform_scale_samples(posterior_scaled, formula_scale) + + # Compare all coefficients + expect_equal( + unname(posterior_transformed[1, ]), + unname(.reorder_lm_coefs(coef_unscaled, posterior_transformed)), + tolerance = 1e-10 + ) +}) + + +test_that("lm validation: factor + scaled continuous interaction", { + + set.seed(50) + df <- data.frame( + x1 = rnorm(500, mean = 5, sd = 3), + f1 = factor(sample(letters[1:2], 500, TRUE)) + ) + df$y <- 2 + 0.5 * scale(df$x1) + + ifelse(df$f1 == "b", 0.3, 0) + + ifelse(df$f1 == "b", 0.2, 0) * scale(df$x1) + + rnorm(500, 0, 0.4) + + # Fit with scaled predictor + fit_scaled <- lm(y ~ scale(x1) * f1, data = df) + coef_scaled <- coef(fit_scaled) + + # Fit with unscaled predictor (ground truth) + fit_unscaled <- lm(y ~ x1 * f1, data = df) + coef_unscaled <- coef(fit_unscaled) + + # Transform - only x1 is scaled (f1 is factor, not scaled) + posterior_scaled <- .lm_coefs_to_posterior(coef_scaled) + formula_scale <- .make_formula_scale(df, "x1") + posterior_transformed <- transform_scale_samples(posterior_scaled, formula_scale) + + # Compare + expect_equal( + unname(posterior_transformed[1, ]), + unname(.reorder_lm_coefs(coef_unscaled, posterior_transformed)), + tolerance = 1e-10 + ) +}) + + +test_that("lm validation: factor + unscaled continuous interaction", { + + set.seed(51) + df <- data.frame( + x1 = rnorm(500, mean = 8, sd = 2), # Will NOT be scaled + x2 = rnorm(500, mean = -3, sd = 4), # Will be scaled + f1 = factor(sample(letters[1:2], 500, TRUE)) + ) + df$y <- 1 + 0.3 * df$x1 + 0.4 * scale(df$x2) + + ifelse(df$f1 == "b", 0.5, 0) + + ifelse(df$f1 == "b", 0.1, 0) * df$x1 + + ifelse(df$f1 == "b", 0.15, 0) * scale(df$x2) + + rnorm(500, 0, 0.3) + + # Fit with partial scaling (only x2 scaled) + fit_scaled <- lm(y ~ x1 * f1 + scale(x2) * f1, data = df) + coef_scaled <- coef(fit_scaled) + + # Fit with unscaled predictors (ground truth) + fit_unscaled <- lm(y ~ x1 * f1 + x2 * f1, data = df) + coef_unscaled <- coef(fit_unscaled) + + # Transform - only x2 is scaled + posterior_scaled <- .lm_coefs_to_posterior(coef_scaled) + formula_scale <- .make_formula_scale(df, "x2") + posterior_transformed <- transform_scale_samples(posterior_scaled, formula_scale) + + # Compare + expect_equal( + unname(posterior_transformed[1, ]), + unname(.reorder_lm_coefs(coef_unscaled, posterior_transformed)), + tolerance = 1e-10 + ) +}) + + +test_that("lm validation: multi-level factor with scaled continuous", { + + set.seed(52) + df <- data.frame( + x1 = rnorm(600, mean = 3, sd = 5), + f1 = factor(sample(letters[1:3], 600, TRUE)) + ) + df$y <- 2 + 0.6 * scale(df$x1) + + ifelse(df$f1 == "b", 0.4, ifelse(df$f1 == "c", -0.3, 0)) + + ifelse(df$f1 == "b", 0.2, ifelse(df$f1 == "c", 0.1, 0)) * scale(df$x1) + + rnorm(600, 0, 0.5) + + # Fit with scaled predictor + fit_scaled <- lm(y ~ scale(x1) * f1, data = df) + coef_scaled <- coef(fit_scaled) + + # Fit with unscaled predictor (ground truth) + fit_unscaled <- lm(y ~ x1 * f1, data = df) + coef_unscaled <- coef(fit_unscaled) + + # Transform + posterior_scaled <- .lm_coefs_to_posterior(coef_scaled) + formula_scale <- .make_formula_scale(df, "x1") + posterior_transformed <- transform_scale_samples(posterior_scaled, formula_scale) + + # Compare + expect_equal( + unname(posterior_transformed[1, ]), + unname(.reorder_lm_coefs(coef_unscaled, posterior_transformed)), + tolerance = 1e-10 + ) +}) + + +test_that("lm validation: two factors with scaled continuous interaction", { + + set.seed(53) + df <- data.frame( + x1 = rnorm(800, mean = 10, sd = 4), + f1 = factor(sample(letters[1:2], 800, TRUE)), + f2 = factor(sample(letters[1:3], 800, TRUE)) + ) + # Complex model with factor-factor and factor-continuous interactions + df$y <- 3 + 0.5 * scale(df$x1) + rnorm(800, 0, 0.6) + + # Fit with scaled predictor - full three-way interaction + fit_scaled <- lm(y ~ scale(x1) * f1 * f2, data = df) + coef_scaled <- coef(fit_scaled) + + # Fit with unscaled predictor (ground truth) + fit_unscaled <- lm(y ~ x1 * f1 * f2, data = df) + coef_unscaled <- coef(fit_unscaled) + + # Transform + posterior_scaled <- .lm_coefs_to_posterior(coef_scaled) + formula_scale <- .make_formula_scale(df, "x1") + posterior_transformed <- transform_scale_samples(posterior_scaled, formula_scale) + + # Compare + expect_equal( + unname(posterior_transformed[1, ]), + unname(.reorder_lm_coefs(coef_unscaled, posterior_transformed)), + tolerance = 1e-10 + ) +}) + + +test_that("lm validation: complex model with factors and mixed scaling", { + + # Comprehensive test with the user's data structure + set.seed(1) + df <- data.frame( + x1 = rnorm(1000, mean = 3, sd = 5), + x2 = rnorm(1000, mean = -10, sd = 80), + x3 = rnorm(1000, mean = -20, sd = 0.07), + x4 = rnorm(1000, mean = 50, sd = 30), + x5 = rnorm(1000, mean = 20, sd = 0.2), + f1 = factor(sample(letters[1:2], 1000, TRUE)), + f2 = factor(sample(letters[1:3], 1000, TRUE)) + ) + + # Model with scaled continuous, unscaled continuous, and factors + # x1, x2, x3 are scaled; x4, x5 are NOT scaled + df$y <- 5 + + 0.3 * scale(df$x1) - 0.2 * scale(df$x2) + 0.1 * scale(df$x3) + + 0.15 * df$x4 - 0.1 * df$x5 + + ifelse(df$f1 == "b", 0.4, 0) + + 0.2 * scale(df$x1) * ifelse(df$f1 == "b", 1, 0) + + 0.1 * df$x4 * ifelse(df$f1 == "b", 1, 0) + + rnorm(1000, 0, 1) + + # Fit with partial scaling + fit_scaled <- lm(y ~ scale(x1) * f1 + scale(x2) + scale(x3) + x4 * f1 + x5, data = df) + coef_scaled <- coef(fit_scaled) + + # Fit with unscaled predictors (ground truth) + fit_unscaled <- lm(y ~ x1 * f1 + x2 + x3 + x4 * f1 + x5, data = df) + coef_unscaled <- coef(fit_unscaled) + + # Transform - only x1, x2, x3 are scaled + posterior_scaled <- .lm_coefs_to_posterior(coef_scaled) + formula_scale <- .make_formula_scale(df, c("x1", "x2", "x3")) + posterior_transformed <- transform_scale_samples(posterior_scaled, formula_scale) + + # Compare + expect_equal( + unname(posterior_transformed[1, ]), + unname(.reorder_lm_coefs(coef_unscaled, posterior_transformed)), + tolerance = 1e-10 + ) +}) + + +test_that("lm validation: factor interactions with multiple scaled continuous", { + + set.seed(54) + df <- data.frame( + x1 = rnorm(800, mean = 5, sd = 3), + x2 = rnorm(800, mean = -2, sd = 6), + f1 = factor(sample(letters[1:2], 800, TRUE)) + ) + # Continuous-continuous and continuous-factor interactions + df$y <- 2 + + 0.4 * scale(df$x1) - 0.3 * scale(df$x2) + + 0.25 * scale(df$x1) * scale(df$x2) + + ifelse(df$f1 == "b", 0.5, 0) + + 0.15 * scale(df$x1) * ifelse(df$f1 == "b", 1, 0) + + 0.1 * scale(df$x2) * ifelse(df$f1 == "b", 1, 0) + + rnorm(800, 0, 0.5) + + # Fit with scaled predictors - three-way interaction x1 * x2 * f1 + fit_scaled <- lm(y ~ scale(x1) * scale(x2) * f1, data = df) + coef_scaled <- coef(fit_scaled) + + # Fit with unscaled predictors (ground truth) + fit_unscaled <- lm(y ~ x1 * x2 * f1, data = df) + coef_unscaled <- coef(fit_unscaled) + + # Transform + posterior_scaled <- .lm_coefs_to_posterior(coef_scaled) + formula_scale <- .make_formula_scale(df, c("x1", "x2")) + posterior_transformed <- transform_scale_samples(posterior_scaled, formula_scale) + + # Compare + expect_equal( + unname(posterior_transformed[1, ]), + unname(.reorder_lm_coefs(coef_unscaled, posterior_transformed)), + tolerance = 1e-10 + ) +}) + diff --git a/tests/testthat/test-JAGS-formula.R b/tests/testthat/test-JAGS-formula.R index f399ecc7..25119b5d 100644 --- a/tests/testthat/test-JAGS-formula.R +++ b/tests/testthat/test-JAGS-formula.R @@ -1,546 +1,28 @@ -context("JAGS formula") - -test_that("JAGS formula works", { - - # check the posterior distributions with weak priors against a maximum likelihood estimates with ML - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes - skip_on_cran() - - set.seed(1) - df_all <- data.frame( - x_cont1 = rnorm(60), - x_cont2 = rnorm(60), - x_bin = rbinom(60, 1, .5), - x_fac2o = factor(rep(c("A", "B"), 30), levels = c("A", "B")), - x_fac2t = factor(rep(c("A", "B"), 30), levels = c("A", "B")), - x_fac3o = factor(rep(c("A", "B", "C"), 20), levels = c("A", "B", "C")), - x_fac3t = factor(rep(c("A", "B", "C"), 20), levels = c("A", "B", "C")), - x_fac3i = factor(rep(c("A", "B", "C"), 20), levels = c("A", "B", "C")), - x_fac3md= factor(rep(c("A", "B", "C"), 20), levels = c("A", "B", "C")) - ) - df_all$y <- rnorm(60, 0.1, 0.5) + 0.30 * df_all$x_cont1 - 0.15 * df_all$x_cont1 * df_all$x_cont2 + 0.2 * df_all$x_bin + - ifelse(df_all$x_fac3t == "A", 0.2, ifelse(df_all$x_fac3t == "B", -0.2, 0)) + - ifelse(df_all$x_fac3o == "A", 0.2, ifelse(df_all$x_fac3o == "B", -0.2, 0)) - prior_list_all <- list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior("normal", list(0, 1)), - "x_cont2" = prior("normal", list(0, 1)), - "x_cont1:x_cont2" = prior("normal", list(0, 1)), - "x_fac2o" = prior_factor("mcauchy", contrast = "orthonormal", list(0, 1)), - "x_fac2t" = prior_factor("normal", contrast = "treatment", list(0, 1)), - "x_fac3o" = prior_factor("mnormal", contrast = "orthonormal", list(0, 1)), - "x_fac3t" = prior_factor("uniform", contrast = "treatment", list(-2, 2)), - "x_fac3i" = prior_factor("normal", contrast = "independent", list(0, 1)), - "x_fac3md" = prior_factor("mnormal", contrast = "meandif", list(0, 1)), - "x_fac2t:x_fac3o" = prior_factor("mnormal", contrast = "orthonormal", list(0, 2)), - "x_fac2o:x_fac3t" = prior_factor("normal", contrast = "treatment", list(0, 2)), - "x_cont1:x_fac3o" = prior_factor("mnormal", contrast = "orthonormal", list(0, 2)), - "x_cont1:x_fac3t" = prior_factor("normal", contrast = "treatment", list(0, 2)) - ) - prior_list2 <- list( - "sigma" = prior("cauchy", list(0, 1), list(0, 1)) - ) - model_syntax <- paste0( - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n" - ) - - - # simple linear regression ---- - formula_1 <- JAGS_formula(~ x_cont1, parameter = "mu", data = df_all[,"x_cont1", drop = FALSE], prior_list = prior_list_all[c("intercept", "x_cont1")]) - prior_list_1 <- c(formula_1$prior_list, prior_list2) - model_syntax_1 <- JAGS_add_priors(paste0("model{", formula_1$formula_syntax, model_syntax, "}"), prior_list_1) - data_1 <- c(formula_1$data, N = nrow(df_all), y = list(df_all$y)) - - model_1 <- rjags::jags.model(file = textConnection(model_syntax_1), inits = JAGS_get_inits(prior_list_1, chains = 2, seed = 1), data = data_1, n.chains = 2, quiet = TRUE) - samples_1 <- rjags::coda.samples(model = model_1, variable.names = JAGS_to_monitor(prior_list_1), n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples_1 <- do.call(rbind, samples_1) - - lm_1 <- stats::lm(y ~ x_cont1, data = df_all) - - vdiffr::expect_doppelganger("JAGS-formula-lm-1", function(){ - - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfcol = c(1, 3)) - - hist(samples_1[,"mu_intercept"], freq = FALSE, main = "Intercept") - curve(dnorm(x, mean = coef(lm_1)["(Intercept)"], sd = summary(lm_1)$coefficients["(Intercept)", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_1[,"mu_x_cont1"], freq = FALSE, main = "x_cont1") - curve(dnorm(x, mean = coef(lm_1)["x_cont1"], sd = summary(lm_1)$coefficients["x_cont1", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_1[,"sigma"], freq = FALSE, main = "sigma") - abline(v = sigma(lm_1), lwd = 3) - }) - - - # linear regression with two continuous predictors and their interaction ---- - formula_2 <- JAGS_formula(~ x_cont1 * x_cont2, parameter = "mu", data = df_all[,c("x_cont1", "x_cont2")], prior_list = prior_list_all[c("intercept", "x_cont1", "x_cont2", "x_cont1:x_cont2")]) - prior_list_2 <- c(formula_2$prior_list, prior_list2) - model_syntax_2 <- JAGS_add_priors(paste0("model{", formula_2$formula_syntax, model_syntax, "}"), prior_list_2) - data_2 <- c(formula_2$data, N = nrow(df_all), y = list(df_all$y)) - - model_2 <- rjags::jags.model(file = textConnection(model_syntax_2), inits = JAGS_get_inits(prior_list_2, chains = 2, seed = 1), data = data_2, n.chains = 2, quiet = TRUE) - samples_2 <- rjags::coda.samples(model = model_2, variable.names = JAGS_to_monitor(prior_list_2), n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples_2 <- do.call(rbind, samples_2) - - lm_2 <- stats::lm(y ~ x_cont1 * x_cont2, data = df_all) - - vdiffr::expect_doppelganger("JAGS-formula-lm-2", function(){ - - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfcol = c(1, 3)) - - hist(samples_2[,"mu_x_cont1"], freq = FALSE, main = "x_cont1") - curve(dnorm(x, mean = coef(lm_2)["x_cont1"], sd = summary(lm_2)$coefficients["x_cont1", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_2[,"mu_x_cont2"], freq = FALSE, main = "x_cont2") - curve(dnorm(x, mean = coef(lm_2)["x_cont2"], sd = summary(lm_2)$coefficients["x_cont2", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_2[,"mu_x_cont1__xXx__x_cont2"], freq = FALSE, main = "x_cont1:x_cont2") - curve(dnorm(x, mean = coef(lm_2)["x_cont1:x_cont2"], sd = summary(lm_2)$coefficients["x_cont1:x_cont2", "Std. Error"]), add = TRUE, lwd = 2) - }) - - - # linear regression with a treatment factor (2 levels) ---- - formula_3 <- JAGS_formula(~ x_fac2t, parameter = "mu", data = df_all[,"x_fac2t",drop = FALSE], prior_list = prior_list_all[c("intercept", "x_fac2t")]) - prior_list_3 <- c(formula_3$prior_list, prior_list2) - model_syntax_3 <- JAGS_add_priors(paste0("model{", formula_3$formula_syntax, model_syntax, "}"), prior_list_3) - data_3 <- c(formula_3$data, N = nrow(df_all), y = list(df_all$y)) - - model_3 <- rjags::jags.model(file = textConnection(model_syntax_3), inits = JAGS_get_inits(prior_list_3, chains = 2, seed = 1), data = data_3, n.chains = 2, quiet = TRUE) - samples_3 <- rjags::coda.samples(model = model_3, variable.names = JAGS_to_monitor(prior_list_3), n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples_3 <- do.call(rbind, samples_3) - - lm_3 <- stats::lm(y ~ x_fac2t, data = df_all) - - vdiffr::expect_doppelganger("JAGS-formula-lm-3", function(){ - - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfcol = c(1, 3)) - - hist(samples_3[,"mu_intercept"], freq = FALSE, main = "Intercept") - curve(dnorm(x, mean = coef(lm_3)["(Intercept)"], sd = summary(lm_3)$coefficients["(Intercept)", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_3[,"mu_x_fac2t"], freq = FALSE, main = "x_fac2t") - curve(dnorm(x, mean = coef(lm_3)["x_fac2tB"], sd = summary(lm_3)$coefficients["x_fac2tB", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_3[,"sigma"], freq = FALSE, main = "sigma") - abline(v = sigma(lm_3), lwd = 3) - }) - - - # linear regression with an orthonormal factor (2 levels) ---- - formula_4 <- JAGS_formula(~ x_fac2o, parameter = "mu", data = df_all[,"x_fac2o",drop = FALSE], prior_list = prior_list_all[c("intercept", "x_fac2o")]) - prior_list_4 <- c(formula_4$prior_list, prior_list2) - model_syntax_4 <- JAGS_add_priors(paste0("model{", formula_4$formula_syntax, model_syntax, "}"), prior_list_4) - data_4 <- c(formula_4$data, N = nrow(df_all), y = list(df_all$y)) - - model_4 <- rjags::jags.model(file = textConnection(model_syntax_4), inits = JAGS_get_inits(prior_list_4, chains = 2, seed = 1), data = data_4, n.chains = 2, quiet = TRUE) - samples_4 <- rjags::coda.samples(model = model_4, variable.names = JAGS_to_monitor(prior_list_4), n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples_4 <- do.call(rbind, samples_4) - - df_4 <- df_all - contrasts(df_4$x_fac2o) <- contr.orthonormal(levels(df_4$x_fac2o)) - lm_4 <- stats::lm(y ~ x_fac2o, data = df_4) - - vdiffr::expect_doppelganger("JAGS-formula-lm-4", function(){ - - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfcol = c(1, 3)) - - hist(samples_4[,"mu_intercept"], freq = FALSE, main = "Intercept") - curve(dnorm(x, mean = coef(lm_4)["(Intercept)"], sd = summary(lm_4)$coefficients["(Intercept)", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_4[,"mu_x_fac2o"], freq = FALSE, main = "x_fac2o") - curve(dnorm(x, mean = coef(lm_4)["x_fac2o1"], sd = summary(lm_4)$coefficients["x_fac2o1", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_4[,"sigma"], freq = FALSE, main = "sigma") - abline(v = sigma(lm_4), lwd = 3) - }) - - - # linear regression with a treatment factor (3 levels) ---- - formula_5 <- JAGS_formula(~ x_fac3t, parameter = "mu", data = df_all[,"x_fac3t",drop = FALSE], prior_list = prior_list_all[c("intercept", "x_fac3t")]) - prior_list_5 <- c(formula_5$prior_list, prior_list2) - model_syntax_5 <- JAGS_add_priors(paste0("model{", formula_5$formula_syntax, model_syntax, "}"), prior_list_5) - data_5 <- c(formula_5$data, N = nrow(df_all), y = list(df_all$y)) - - model_5 <- rjags::jags.model(file = textConnection(model_syntax_5), inits = JAGS_get_inits(prior_list_5, chains = 2, seed = 1), data = data_5, n.chains = 2, quiet = TRUE) - samples_5 <- rjags::coda.samples(model = model_5, variable.names = JAGS_to_monitor(prior_list_5), n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples_5 <- do.call(rbind, samples_5) - - lm_5 <- stats::lm(y ~ x_fac3t, data = df_all) - - vdiffr::expect_doppelganger("JAGS-formula-lm-5", function(){ - - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfcol = c(1, 3)) - - hist(samples_5[,"mu_intercept"], freq = FALSE, main = "Intercept") - curve(dnorm(x, mean = coef(lm_5)["(Intercept)"], sd = summary(lm_5)$coefficients["(Intercept)", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_5[,"mu_x_fac3t[1]"], freq = FALSE, main = "x_fac3t[1]") - curve(dnorm(x, mean = coef(lm_5)["x_fac3tB"], sd = summary(lm_5)$coefficients["x_fac3tB", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_5[,"mu_x_fac3t[2]"], freq = FALSE, main = "x_fac3t[2]") - curve(dnorm(x, mean = coef(lm_5)["x_fac3tC"], sd = summary(lm_5)$coefficients["x_fac3tC", "Std. Error"]), add = TRUE, lwd = 2) - }) - - - # linear regression with an orthonormal factor (3 levels) ---- - formula_6 <- JAGS_formula(~ x_fac3o, parameter = "mu", data = df_all[,"x_fac3o",drop = FALSE], prior_list = prior_list_all[c("intercept", "x_fac3o")]) - prior_list_6 <- c(formula_6$prior_list, prior_list2) - model_syntax_6 <- JAGS_add_priors(paste0("model{", formula_6$formula_syntax, model_syntax, "}"), prior_list_6) - data_6 <- c(formula_6$data, N = nrow(df_all), y = list(df_all$y)) - - model_6 <- rjags::jags.model(file = textConnection(model_syntax_6), inits = JAGS_get_inits(prior_list_6, chains = 2, seed = 1), data = data_6, n.chains = 2, quiet = TRUE) - samples_6 <- rjags::coda.samples(model = model_6, variable.names = JAGS_to_monitor(prior_list_6), n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples_6 <- do.call(rbind, samples_6) - - df_6 <- df_all - contrasts(df_6$x_fac3o) <- contr.orthonormal(levels(df_6$x_fac3o)) - lm_6 <- stats::lm(y ~ x_fac3o, data = df_6) - - vdiffr::expect_doppelganger("JAGS-formula-lm-6", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfcol = c(1, 3)) - - hist(samples_6[,"mu_intercept"], freq = FALSE, main = "Intercept") - curve(dnorm(x, mean = coef(lm_6)["(Intercept)"], sd = summary(lm_6)$coefficients["(Intercept)", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_6[,"mu_x_fac3o[1]"], freq = FALSE, main = "x_fac3o") - curve(dnorm(x, mean = coef(lm_6)["x_fac3o1"], sd = summary(lm_6)$coefficients["x_fac3o1", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_6[,"mu_x_fac3o[2]"], freq = FALSE, main = "x_fac3o") - curve(dnorm(x, mean = coef(lm_6)["x_fac3o2"], sd = summary(lm_6)$coefficients["x_fac3o2", "Std. Error"]), add = TRUE, lwd = 2) - }) - - - # linear regression with an orthonormal interaction between factors ---- - formula_7 <- JAGS_formula(~ x_fac2t * x_fac3o, parameter = "mu", data = df_all[,c("x_fac2t", "x_fac3o")], prior_list = prior_list_all[c("intercept", "x_fac2t", "x_fac3o", "x_fac2t:x_fac3o")]) - prior_list_7 <- c(formula_7$prior_list, prior_list2) - model_syntax_7 <- JAGS_add_priors(paste0("model{", formula_7$formula_syntax, model_syntax, "}"), prior_list_7) - data_7 <- c(formula_7$data, N = nrow(df_all), y = list(df_all$y)) - - model_7 <- rjags::jags.model(file = textConnection(model_syntax_7), inits = JAGS_get_inits(prior_list_7, chains = 2, seed = 1), data = data_7, n.chains = 2, quiet = TRUE) - samples_7 <- rjags::coda.samples(model = model_7, variable.names = JAGS_to_monitor(prior_list_7), n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples_7 <- do.call(rbind, samples_7) - - df_7 <- df_all - contrasts(df_7$x_fac3o) <- contr.orthonormal(levels(df_7$x_fac3o)) - lm_7 <- stats::lm(y ~ x_fac2t * x_fac3o, data = df_7) - - vdiffr::expect_doppelganger("JAGS-formula-lm-7", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfrow = c(2, 3)) - - hist(samples_7[,"mu_intercept"], freq = FALSE, main = "Intercept") - curve(dnorm(x, mean = coef(lm_7)["(Intercept)"], sd = summary(lm_7)$coefficients["(Intercept)", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_7[,"mu_x_fac3o[1]"], freq = FALSE, main = "x_fac3o") - curve(dnorm(x, mean = coef(lm_7)["x_fac3o1"], sd = summary(lm_7)$coefficients["x_fac3o1", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_7[,"mu_x_fac3o[2]"], freq = FALSE, main = "x_fac3o") - curve(dnorm(x, mean = coef(lm_7)["x_fac3o2"], sd = summary(lm_7)$coefficients["x_fac3o2", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_7[,"mu_x_fac2t"], freq = FALSE, main = "x_fac2t") - curve(dnorm(x, mean = coef(lm_7)["x_fac2tB"], sd = summary(lm_7)$coefficients["x_fac2tB", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_7[,"mu_x_fac2t__xXx__x_fac3o[1]"], freq = FALSE, main = "x_fac2t:x_fac3o") - curve(dnorm(x, mean = coef(lm_7)["x_fac2tB:x_fac3o1"], sd = summary(lm_7)$coefficients["x_fac2tB:x_fac3o1", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_7[,"mu_x_fac2t__xXx__x_fac3o[2]"], freq = FALSE, main = "x_fac2t:x_fac3o") - curve(dnorm(x, mean = coef(lm_7)["x_fac2tB:x_fac3o2"], sd = summary(lm_7)$coefficients["x_fac2tB:x_fac3o2", "Std. Error"]), add = TRUE, lwd = 2) - - }) - - - # linear regression with a treatment interaction between factors ---- - formula_8 <- JAGS_formula(~ x_fac2o * x_fac3t, parameter = "mu", data = df_all[,c("x_fac2o", "x_fac3t")], prior_list = prior_list_all[c("intercept", "x_fac2o", "x_fac3t", "x_fac2o:x_fac3t")]) - prior_list_8 <- c(formula_8$prior_list, prior_list2) - model_syntax_8 <- JAGS_add_priors(paste0("model{", formula_8$formula_syntax, model_syntax, "}"), prior_list_8) - data_8 <- c(formula_8$data, N = nrow(df_all), y = list(df_all$y)) - - model_8 <- rjags::jags.model(file = textConnection(model_syntax_8), inits = JAGS_get_inits(prior_list_8, chains = 2, seed = 1), data = data_8, n.chains = 2, quiet = TRUE) - samples_8 <- rjags::coda.samples(model = model_8, variable.names = JAGS_to_monitor(prior_list_8), n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples_8 <- do.call(rbind, samples_8) - - df_8 <- df_all - contrasts(df_8$x_fac2o) <- contr.orthonormal(levels(df_8$x_fac2o)) - lm_8 <- stats::lm(y ~ x_fac2o * x_fac3t, data = df_8) - - vdiffr::expect_doppelganger("JAGS-formula-lm-8", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfrow = c(2, 3)) - - hist(samples_8[,"mu_intercept"], freq = FALSE, main = "Intercept") - curve(dnorm(x, mean = coef(lm_8)["(Intercept)"], sd = summary(lm_8)$coefficients["(Intercept)", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_8[,"mu_x_fac3t[1]"], freq = FALSE, main = "x_fac3t") - curve(dnorm(x, mean = coef(lm_8)["x_fac3tB"], sd = summary(lm_8)$coefficients["x_fac3tB", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_8[,"mu_x_fac3t[2]"], freq = FALSE, main = "x_fac3t") - curve(dnorm(x, mean = coef(lm_8)["x_fac3tC"], sd = summary(lm_8)$coefficients["x_fac3tC", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_8[,"mu_x_fac2o"], freq = FALSE, main = "x_fac2o") - curve(dnorm(x, mean = coef(lm_8)["x_fac2o1"], sd = summary(lm_8)$coefficients["x_fac2o1", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_8[,"mu_x_fac2o__xXx__x_fac3t[1]"], freq = FALSE, main = "x_fac2o:fac3t") - curve(dnorm(x, mean = coef(lm_8)["x_fac2o1:x_fac3tB"], sd = summary(lm_8)$coefficients["x_fac2o1:x_fac3tB", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_8[,"mu_x_fac2o__xXx__x_fac3t[2]"], freq = FALSE, main = "x_fac2o:fac3t") - curve(dnorm(x, mean = coef(lm_8)["x_fac2o1:x_fac3tC"], sd = summary(lm_8)$coefficients["x_fac2o1:x_fac3tC", "Std. Error"]), add = TRUE, lwd = 2) - - }) - # linear regression with an interaction between continuous variable and orthonormal factor ---- - formula_9 <- JAGS_formula(~ x_cont1 * x_fac3o , parameter = "mu", data = df_all[,c("x_cont1", "x_fac3o")], prior_list = prior_list_all[c("intercept", "x_cont1", "x_fac3o", "x_cont1:x_fac3o")]) - prior_list_9 <- c(formula_9$prior_list, prior_list2) - model_syntax_9 <- JAGS_add_priors(paste0("model{", formula_9$formula_syntax, model_syntax, "}"), prior_list_9) - data_9 <- c(formula_9$data, N = nrow(df_all), y = list(df_all$y)) - - model_9 <- rjags::jags.model(file = textConnection(model_syntax_9), inits = JAGS_get_inits(prior_list_9, chains = 2, seed = 1), data = data_9, n.chains = 2, quiet = TRUE) - samples_9 <- rjags::coda.samples(model = model_9, variable.names = JAGS_to_monitor(prior_list_9), n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples_9 <- do.call(rbind, samples_9) - - df_9 <- df_all - contrasts(df_9$x_fac3o) <- contr.orthonormal(levels(df_9$x_fac3o)) - lm_9 <- stats::lm(y ~ x_cont1 * x_fac3o, data = df_9) - - vdiffr::expect_doppelganger("JAGS-formula-lm-9", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfrow = c(2, 3)) - - hist(samples_9[,"mu_intercept"], freq = FALSE, main = "Intercept") - curve(dnorm(x, mean = coef(lm_9)["(Intercept)"], sd = summary(lm_9)$coefficients["(Intercept)", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_9[,"mu_x_fac3o[1]"], freq = FALSE, main = "x_fac3o") - curve(dnorm(x, mean = coef(lm_9)["x_fac3o1"], sd = summary(lm_9)$coefficients["x_fac3o1", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_9[,"mu_x_fac3o[2]"], freq = FALSE, main = "x_fac3o") - curve(dnorm(x, mean = coef(lm_9)["x_fac3o2"], sd = summary(lm_9)$coefficients["x_fac3o2", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_9[,"mu_x_cont1"], freq = FALSE, main = "x_cont1") - curve(dnorm(x, mean = coef(lm_9)["x_cont1"], sd = summary(lm_9)$coefficients["x_cont1", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_9[,"mu_x_cont1__xXx__x_fac3o[1]"], freq = FALSE, main = "x_cont1:x_fac3o") - curve(dnorm(x, mean = coef(lm_9)["x_cont1:x_fac3o1"], sd = summary(lm_9)$coefficients["x_cont1:x_fac3o1", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_9[,"mu_x_cont1__xXx__x_fac3o[2]"], freq = FALSE, main = "x_cont1:x_fac3o") - curve(dnorm(x, mean = coef(lm_9)["x_cont1:x_fac3o2"], sd = summary(lm_9)$coefficients["x_cont1:x_fac3o2", "Std. Error"]), add = TRUE, lwd = 2) - - }) - - - # linear regression with an interaction between continuous variable and orthonormal factor ---- - formula_10 <- JAGS_formula(~ x_cont1 * x_fac3t , parameter = "mu", data = df_all[,c("x_cont1", "x_fac3t")], prior_list = prior_list_all[c("intercept", "x_cont1", "x_fac3t", "x_cont1:x_fac3t")]) - prior_list_10 <- c(formula_10$prior_list, prior_list2) - model_syntax_10 <- JAGS_add_priors(paste0("model{", formula_10$formula_syntax, model_syntax, "}"), prior_list_10) - data_10 <- c(formula_10$data, N = nrow(df_all), y = list(df_all$y)) - - model_10 <- rjags::jags.model(file = textConnection(model_syntax_10), inits = JAGS_get_inits(prior_list_10, chains = 2, seed = 1), data = data_10, n.chains = 2, quiet = TRUE) - samples_10 <- rjags::coda.samples(model = model_10, variable.names = JAGS_to_monitor(prior_list_10), n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples_10 <- do.call(rbind, samples_10) - - lm_10 <- stats::lm(y ~ x_cont1 * x_fac3t, data = df_all) - - vdiffr::expect_doppelganger("JAGS-formula-lm-10", function(){ - - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfrow = c(2, 3)) - - hist(samples_10[,"mu_intercept"], freq = FALSE, main = "Intercept") - curve(dnorm(x, mean = coef(lm_10)["(Intercept)"], sd = summary(lm_10)$coefficients["(Intercept)", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_10[,"mu_x_fac3t[1]"], freq = FALSE, main = "x_fac3t") - curve(dnorm(x, mean = coef(lm_10)["x_fac3tB"], sd = summary(lm_10)$coefficients["x_fac3tB", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_10[,"mu_x_fac3t[2]"], freq = FALSE, main = "x_fac3t") - curve(dnorm(x, mean = coef(lm_10)["x_fac3tC"], sd = summary(lm_10)$coefficients["x_fac3tC", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_10[,"mu_x_cont1"], freq = FALSE, main = "x_cont1") - curve(dnorm(x, mean = coef(lm_10)["x_cont1"], sd = summary(lm_10)$coefficients["x_cont1", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_10[,"mu_x_cont1__xXx__x_fac3t[1]"], freq = FALSE, main = "x_cont1:x_fac3t") - curve(dnorm(x, mean = coef(lm_10)["x_cont1:x_fac3tB"], sd = summary(lm_10)$coefficients["x_cont1:x_fac3tB", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_10[,"mu_x_cont1__xXx__x_fac3t[2]"], freq = FALSE, main = "x_cont1:x_fac3t") - curve(dnorm(x, mean = coef(lm_10)["x_cont1:x_fac3tC"], sd = summary(lm_10)$coefficients["x_cont1:x_fac3tC", "Std. Error"]), add = TRUE, lwd = 2) - - }) - - - # scaling formula parameters by another parameter works ---- - prior_list_1s <- prior_list_all[c("intercept", "x_cont1")] - attr(prior_list_1s$x_cont1, "multiply_by") <- "sigma" - formula_1s <- JAGS_formula(~ x_cont1, parameter = "mu", data = df_all[,"x_cont1", drop = FALSE], prior_list = prior_list_1s) - prior_list_1s <- c(formula_1s$prior_list, prior_list2) - model_syntax_1s<- JAGS_add_priors(paste0("model{", formula_1s$formula_syntax, model_syntax, "}"), prior_list_1s) - data_1 <- c(formula_1$data, N = nrow(df_all), y = list(df_all$y)) - - model_1s <- rjags::jags.model(file = textConnection(model_syntax_1s), inits = JAGS_get_inits(prior_list_1s, chains = 2, seed = 1), data = data_1, n.chains = 2, quiet = TRUE) - samples_1s <- rjags::coda.samples(model = model_1s, variable.names = JAGS_to_monitor(prior_list_1s), n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples_1s <- do.call(rbind, samples_1s) - - expect_equal(formula_1s$formula_syntax, "for(i in 1:N_mu){\n mu[i] = mu_intercept + sigma * mu_x_cont1 * mu_data_x_cont1[i]\n}\n") - - lm_1s <- stats::lm(y ~ I(sd(y) * x_cont1), data = df_all) - - vdiffr::expect_doppelganger("JAGS-formula-lm-1s", function(){ - - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfcol = c(1, 3)) - - hist(samples_1s[,"mu_intercept"], freq = FALSE, main = "Intercept") - curve(dnorm(x, mean = coef(lm_1s)["(Intercept)"], sd = summary(lm_1s)$coefficients["(Intercept)", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_1s[,"mu_x_cont1"], freq = FALSE, main = "I(sd(y) * x_cont1)") - curve(dnorm(x, mean = coef(lm_1s)["I(sd(y) * x_cont1)"], sd = summary(lm_1s)$coefficients["I(sd(y) * x_cont1)", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_1s[,"sigma"], freq = FALSE, main = "sigma") - abline(v = sigma(lm_1s), lwd = 3) - }) - - - # input checks work - expect_error(JAGS_formula(~ x_cont1 , parameter = "mu", data = df_all[,c("x_cont1"), drop = FALSE], prior_list = prior_list_all[c("x_cont1")]), - "The 'intercept' objects are missing in the 'prior_list' argument.") - expect_error(JAGS_formula(~ x_cont1 , parameter = "mu", data = df_all[,c("x_cont1"), drop = FALSE], prior_list = prior_list_all[c("intercept")]), - "The 'x_cont1' objects are missing in the 'prior_list' argument.") - expect_error(JAGS_formula(~ x_fac2t , parameter = "mu", data = df_all[,c("x_cont1"), drop = FALSE], prior_list = prior_list_all[c("intercept", "x_fac2t")]), - "The 'x_fac2t' predictor variable is missing in the data set.") - expect_error(JAGS_formula(~ x_fac2t , parameter = "mu", data = as.matrix(df_all), prior_list = prior_list_all[c("intercept", "x_fac2t")]), - "'data' must be a data.frame") - expect_error(JAGS_formula(~ x_fac2t , parameter = "mu", data = df_all, prior_list = list( - "intercept" = prior("normal", list(0, 1)), - "x_fac2t" = prior("normal", list(0, 1)) - )), "Unsupported prior distribution defined for 'x_fac2t' factor variable") - expect_error(JAGS_formula(~ x_cont1 , parameter = "mu", data = df_all, prior_list = list( - "intercept" = prior("normal", list(0, 1)), - "x_cont1" = prior_factor("normal", list(0, 1), contrast = "treatment") - )), "Unsupported prior distribution defined for 'x_cont1' continuous variable.") - - # linear regression with an independent factor (3 levels) ---- - formula_11 <- JAGS_formula(~ x_fac3i - 1, parameter = "mu", data = df_all[,"x_fac3i",drop = FALSE], prior_list = prior_list_all[c("x_fac3i")]) - prior_list_11 <- c(formula_11$prior_list, prior_list2) - model_syntax_11 <- JAGS_add_priors(paste0("model{", formula_11$formula_syntax, model_syntax, "}"), prior_list_11) - data_11 <- c(formula_11$data, N = nrow(df_all), y = list(df_all$y)) - - model_11 <- rjags::jags.model(file = textConnection(model_syntax_11), inits = JAGS_get_inits(prior_list_11, chains = 2, seed = 1), data = data_11, n.chains = 2, quiet = TRUE) - samples_11 <- rjags::coda.samples(model = model_11, variable.names = JAGS_to_monitor(prior_list_11), n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples_11 <- do.call(rbind, samples_11) - - lm_11 <- stats::lm(y ~ x_fac3i - 1, data = df_all) - - vdiffr::expect_doppelganger("JAGS-formula-lm-11", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfcol = c(1, 3)) - - hist(samples_11[,"mu_x_fac3i[1]"], freq = FALSE, main = "x_fac3i[1]") - curve(dnorm(x, mean = coef(lm_11)["x_fac3iA"], sd = summary(lm_11)$coefficients["x_fac3iA", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_11[,"mu_x_fac3i[2]"], freq = FALSE, main = "x_fac3i[2]") - curve(dnorm(x, mean = coef(lm_11)["x_fac3iB"], sd = summary(lm_11)$coefficients["x_fac3iB", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_11[,"mu_x_fac3i[3]"], freq = FALSE, main = "x_fac3i[3]") - curve(dnorm(x, mean = coef(lm_11)["x_fac3iC"], sd = summary(lm_11)$coefficients["x_fac3iC", "Std. Error"]), add = TRUE, lwd = 2) - }) - - - # linear regression with a meandif factor (3 levels) ---- - formula_12 <- JAGS_formula(~ x_fac3md, parameter = "mu", data = df_all[,"x_fac3md",drop = FALSE], prior_list = prior_list_all[c("intercept", "x_fac3md")]) - prior_list_12 <- c(formula_12$prior_list, prior_list2) - model_syntax_12 <- JAGS_add_priors(paste0("model{", formula_12$formula_syntax, model_syntax, "}"), prior_list_12) - data_12 <- c(formula_12$data, N = nrow(df_all), y = list(df_all$y)) - - model_12 <- rjags::jags.model(file = textConnection(model_syntax_12), inits = JAGS_get_inits(prior_list_12, chains = 2, seed = 1), data = data_12, n.chains = 2, quiet = TRUE) - samples_12 <- rjags::coda.samples(model = model_12, variable.names = JAGS_to_monitor(prior_list_12), n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples_12 <- do.call(rbind, samples_12) - - df_12 <- df_all - contrasts(df_12$x_fac3md) <- contr.meandif(levels(df_12$x_fac3o)) - lm_12 <- stats::lm(y ~ x_fac3md, data = df_12) - - vdiffr::expect_doppelganger("JAGS-formula-lm-12", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfcol = c(1, 3)) - - hist(samples_12[,"mu_intercept"], freq = FALSE, main = "Intercept") - curve(dnorm(x, mean = coef(lm_12)["(Intercept)"], sd = summary(lm_12)$coefficients["(Intercept)", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_12[,"mu_x_fac3md[1]"], freq = FALSE, main = "x_fac3md") - curve(dnorm(x, mean = coef(lm_12)["x_fac3md1"], sd = summary(lm_12)$coefficients["x_fac3md1", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_12[,"mu_x_fac3md[2]"], freq = FALSE, main = "x_fac3md") - curve(dnorm(x, mean = coef(lm_12)["x_fac3md2"], sd = summary(lm_12)$coefficients["x_fac3md2", "Std. Error"]), add = TRUE, lwd = 2) - }) - - - # linear regression with a spike independent factor (3 levels) ---- - prior_list_13 <- list("x_fac3i" = prior_factor("spike", contrast = "independent", list(1.5))) - formula_13 <- JAGS_formula(~ x_fac3i - 1, parameter = "mu", data = df_all[,"x_fac3i",drop = FALSE], prior_list = prior_list_13) - prior_list_13 <- c(formula_13$prior_list, prior_list2) - model_syntax_13 <- JAGS_add_priors(paste0("model{", formula_13$formula_syntax, model_syntax, "}"), prior_list_13) - data_13 <- c(formula_13$data, N = nrow(df_all), y = list(df_all$y)) - - model_13 <- rjags::jags.model(file = textConnection(model_syntax_13), inits = JAGS_get_inits(prior_list_13, chains = 2, seed = 1), data = data_13, n.chains = 2, quiet = TRUE) - samples_13 <- rjags::coda.samples(model = model_13, variable.names = JAGS_to_monitor(prior_list_13), n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples_13 <- do.call(rbind, samples_13) - expect_equal(diag(3), contr.independent(1:3)) - - vdiffr::expect_doppelganger("JAGS-formula-lm-13", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfcol = c(1, 3)) - - hist(samples_13[,"mu_x_fac3i[1]"], freq = FALSE, main = "x_fac3i[1]") - hist(samples_13[,"mu_x_fac3i[2]"], freq = FALSE, main = "x_fac3i[2]") - hist(samples_13[,"mu_x_fac3i[3]"], freq = FALSE, main = "x_fac3i[3]") - }) - - - # linear regression with a meandif spike factor (3 levels) ---- - prior_list_14 <- list("intercept" = prior_list_all$intercept, "x_fac3md" = prior_factor("spike", contrast = "meandif", list(0))) - formula_14 <- JAGS_formula(~ x_fac3md, parameter = "mu", data = df_all[,"x_fac3md",drop = FALSE], prior_list = prior_list_14) - prior_list_14 <- c(formula_14$prior_list, prior_list2) - model_syntax_14 <- JAGS_add_priors(paste0("model{", formula_14$formula_syntax, model_syntax, "}"), prior_list_14) - data_14 <- c(formula_14$data, N = nrow(df_all), y = list(df_all$y)) - - model_14 <- rjags::jags.model(file = textConnection(model_syntax_14), inits = JAGS_get_inits(prior_list_14, chains = 2, seed = 1), data = data_14, n.chains = 2, quiet = TRUE) - samples_14 <- rjags::coda.samples(model = model_14, variable.names = JAGS_to_monitor(prior_list_14), n.iter = 5000, quiet = TRUE, progress.bar = "none") - samples_14 <- do.call(rbind, samples_14) - - df_14 <- df_all - contrasts(df_14$x_fac3md) <- contr.meandif(levels(df_14$x_fac3o)) - lm_14 <- stats::lm(y ~ 1, data = df_14) - - vdiffr::expect_doppelganger("JAGS-formula-lm-14", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfcol = c(1, 3)) - - hist(samples_14[,"mu_intercept"], freq = FALSE, main = "Intercept") - curve(dnorm(x, mean = coef(lm_14)["(Intercept)"], sd = summary(lm_14)$coefficients["(Intercept)", "Std. Error"]), add = TRUE, lwd = 2) - - hist(samples_14[,"mu_x_fac3md[1]"], freq = FALSE, main = "x_fac3md") - hist(samples_14[,"mu_x_fac3md[2]"], freq = FALSE, main = "x_fac3md") - }) - -}) +# ============================================================================ # +# TEST FILE: JAGS Formula Handling +# ============================================================================ # +# +# PURPOSE: +# Tests for JAGS formula parsing, parameter naming, and prediction functions +# in R/JAGS-formula.R. Includes JAGS_evaluate_formula and helper utilities. +# +# DEPENDENCIES: +# - rjags: Required for JAGS model evaluation +# - common-functions.R: Test helpers and pre-fitted model access +# +# SKIP CONDITIONS: +# - First section (parameter name tools): Can run on CRAN (pure R) +# - Second section (JAGS evaluation): skip_if_not_installed("rjags") +# - skip_on_os(): Multivariate sampling consistency (meandif priors) +# +# MODELS/FIXTURES: +# - Uses pre-fitted models from test-00-model-fits.R via temp_fits_dir +# +# TAGS: @evaluation, @JAGS, @formula +# ============================================================================ # + +# Load common test helpers +source(testthat::test_path("common-functions.R")) test_that("JAGS formula tools work", { @@ -569,13 +51,18 @@ test_that("JAGS formula tools work", { }) +# ============================================================================ # +# SECTION: Tests requiring JAGS (skip conditions per test) +# ============================================================================ # + test_that("JAGS evaluate formula works", { - # check the posterior distributions with weak priors against a maximum likelihood estimates with ML + # Test JAGS_evaluate_formula by comparing against lm() predictions using ML estimates. + # This test constructs samples manually (from ML estimates) - no pre-fitted JAGS model needed. skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes - skip_on_cran() + skip_if_not_installed("rjags") - # complex formula including scaling + # Setup: complex formula including scaling set.seed(1) df_all <- data.frame( x_cont1 = rnorm(60), @@ -597,19 +84,14 @@ test_that("JAGS evaluate formula works", { prior_list2 <- list( "sigma" = prior("cauchy", list(0, 1), list(0, 1)) ) - model_syntax <- paste0( - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n" - ) - formula <- JAGS_formula(~ x_fac2t + x_cont2 + x_cont1 * x_fac3o, parameter = "mu", data = df_all, prior_list = prior_list_all) - prior_list <- c(formula$prior_list, prior_list2) - model_syntax <- JAGS_add_priors(paste0("model{", formula$formula_syntax, model_syntax, "}"), prior_list) - data <- c(formula$data, N = nrow(df_all), y = list(df_all$y)) + # Use JAGS_formula to process formula and get prior_list with correct parameter names + formula_result <- JAGS_formula(~ x_fac2t + x_cont2 + x_cont1 * x_fac3o, parameter = "mu", data = df_all, prior_list = prior_list_all) + prior_list <- c(formula_result$prior_list, prior_list2) - model <- rjags::jags.model(file = textConnection(model_syntax), inits = JAGS_get_inits(prior_list, chains = 1, seed = 1), data = data, n.chains = 1, quiet = TRUE) - samples <- rjags::coda.samples(model = model, variable.names = JAGS_to_monitor(prior_list), n.iter = 10, quiet = TRUE, progress.bar = "none") + # Define expected column names for samples (must match what JAGS_formula produces) + col_names <- c("mu_intercept", "mu_x_cont1", "mu_x_cont1__xXx__x_fac3o[1]", "mu_x_cont1__xXx__x_fac3o[2]", + "mu_x_cont2", "mu_x_fac2t", "mu_x_fac3o[1]", "mu_x_fac3o[2]", "sigma") new_data <- data.frame( x_cont1 = c(0, 0, 1, 1), @@ -618,29 +100,30 @@ test_that("JAGS evaluate formula works", { x_fac3o = factor(c("A", "B", "C", "A"), levels = c("A", "B", "C")) ) - # test the results against the lm function (by passing the ML estimates) + # Test the results against the lm function (by passing the ML estimates) contrasts(df_all$x_fac3o) <- contr.orthonormal(levels(df_all$x_fac3o)) - fit_lm <- stats::lm(y~ x_fac2t + x_cont2 + x_cont1 * x_fac3o, data = df_all) + fit_lm <- stats::lm(y ~ x_fac2t + x_cont2 + x_cont1 * x_fac3o, data = df_all) + # Create mock samples from ML estimates samples_new <- c(coef(fit_lm), sigma = sigma(fit_lm))[c("(Intercept)","x_cont1","x_cont1:x_fac3o1","x_cont1:x_fac3o2","x_cont2","x_fac2tB","x_fac3o1","x_fac3o2","sigma")] samples_new <- matrix(samples_new, nrow = 1) - colnames(samples_new) <- colnames(samples[[1]]) + colnames(samples_new) <- col_names samples_new <- coda::as.mcmc.list(coda::as.mcmc(samples_new)) expect_equal(predict(fit_lm, newdata = new_data), JAGS_evaluate_formula(samples_new, ~ x_fac2t + x_cont2 + x_cont1 * x_fac3o, "mu", new_data, prior_list)[,1]) - # for a posterior samples matrix + # For a posterior samples matrix (multiple rows) samples_new <- c(coef(fit_lm), sigma = sigma(fit_lm))[c("(Intercept)","x_cont1","x_cont1:x_fac3o1","x_cont1:x_fac3o2","x_cont2","x_fac2tB","x_fac3o1","x_fac3o2","sigma")] samples_new <- matrix(samples_new, nrow = 5, ncol = length(samples_new), byrow = TRUE) - colnames(samples_new) <- colnames(samples[[1]]) + colnames(samples_new) <- col_names samples_new <- coda::as.mcmc.list(coda::as.mcmc(samples_new)) expect_equal(matrix(predict(fit_lm, newdata = new_data), nrow = 4, ncol = 5), unname(JAGS_evaluate_formula(samples_new, ~ x_fac2t + x_cont2 + x_cont1 * x_fac3o, "mu", new_data, prior_list))) - # check filling in missing or miss ordered factor levels + # Check filling in missing or miss-ordered factor levels samples_new <- c(coef(fit_lm), sigma = sigma(fit_lm))[c("(Intercept)","x_cont1","x_cont1:x_fac3o1","x_cont1:x_fac3o2","x_cont2","x_fac2tB","x_fac3o1","x_fac3o2","sigma")] samples_new <- matrix(samples_new, nrow = 1) - colnames(samples_new) <- colnames(samples[[1]]) + colnames(samples_new) <- col_names samples_new <- coda::as.mcmc.list(coda::as.mcmc(samples_new)) new_data2 <- new_data @@ -655,29 +138,22 @@ test_that("JAGS evaluate formula works", { new_data4$x_fac3o <- c("A", "B", "A", "B") expect_equal(predict(fit_lm, newdata = new_data3), JAGS_evaluate_formula(samples_new, ~ x_fac2t + x_cont2 + x_cont1 * x_fac3o, "mu", new_data4, prior_list)[,1]) - # check scaling works (by multiplying be zero) - prior_list2 <- prior_list - attr(prior_list2$mu_x_cont2, "multiply_by") <- 0 - attr(prior_list2$mu_x_fac2t, "multiply_by") <- 0 + # Check scaling works (by multiplying by zero) + prior_list_scaled <- prior_list + attr(prior_list_scaled$mu_x_cont2, "multiply_by") <- 0 + attr(prior_list_scaled$mu_x_fac2t, "multiply_by") <- 0 samples_new2 <- c(coef(fit_lm), sigma = sigma(fit_lm))[c("(Intercept)","x_cont1","x_cont1:x_fac3o1","x_cont1:x_fac3o2","x_cont2","x_fac2tB","x_fac3o1","x_fac3o2","sigma")] samples_new2 <- matrix(samples_new2, nrow = 1) - colnames(samples_new2) <- colnames(samples[[1]]) - samples_new2[,"mu_x_cont2"] <- 0 + colnames(samples_new2) <- col_names + samples_new2[,"mu_x_cont2"] <- 0 samples_new2[,"mu_x_fac2t"] <- 0 samples_new2 <- coda::as.mcmc.list(coda::as.mcmc(samples_new2)) - expect_equal(JAGS_evaluate_formula(samples_new, ~ x_fac2t + x_cont2 + x_cont1 * x_fac3o, "mu", new_data, prior_list2)[,1], + expect_equal(JAGS_evaluate_formula(samples_new, ~ x_fac2t + x_cont2 + x_cont1 * x_fac3o, "mu", new_data, prior_list_scaled)[,1], JAGS_evaluate_formula(samples_new2, ~ x_fac2t + x_cont2 + x_cont1 * x_fac3o, "mu", new_data, prior_list)[,1]) - # check scaling by another parameter works - prior_list2 <- prior_list - attr(prior_list2$mu_x_cont2, "multiply_by") <- "sigma" - - expect_equal(unname(unlist(JAGS_evaluate_formula(samples, ~ x_fac2t + x_cont2 + x_cont1 * x_fac3o, "mu", new_data, prior_list2)[,1])), - c(0.4436353, -0.0658681, 0.1870391, 0.8548012), tolerance = 1e-5) - - ### test input tests + ### Test input validation expect_error(JAGS_evaluate_formula(samples_new, ~ x_fac2t + x_cont2 + x_cont1 * x_fac3o, "mu", new_data[,1:3], prior_list), "The 'x_fac3o' predictor variable is missing in the data.") expect_error(JAGS_evaluate_formula(samples_new, ~ x_fac2t + x_cont2 + x_cont1 * x_fac3o, "mu", new_data, prior_list[-1]), @@ -694,83 +170,70 @@ test_that("JAGS evaluate formula works", { expect_error(JAGS_evaluate_formula(samples_new, ~ x_fac2t + x_cont2 + x_cont1 * x_fac3o, "mu", bad_data2, prior_list), "Levels specified in the 'x_fac2t' factor variable do not match the levels used for model specification.") +}) - # evaluate formula with spike prior distributions ---- - set.seed(1) - df_all <- data.frame( - x_fac2i = factor(rep(c("A", "B"), 30), levels = c("A", "B")), - x_fac3o = factor(sample(c("A", "B", "C"), 60, replace = TRUE), levels = c("A", "B", "C")), - x_fac3t = factor(sample(c("A", "B", "C"), 60, replace = TRUE), levels = c("A", "B", "C")), - x_fac3md = factor(sample(c("A", "B", "C"), 60, replace = TRUE), levels = c("A", "B", "C")) - ) - df_all$y <- rnorm(60, 0.1, 0.5) +test_that("JAGS evaluate formula works with spike priors", { - prior_list_all <- list( - "intercept" = prior("normal", list(0, 5)), - "x_fac2i" = prior_factor("spike", contrast = "independent", list(1)), - "x_fac3o" = prior_factor("spike", contrast = "orthonormal", list(0)), - "x_fac3t" = prior_factor("spike", contrast = "treatment", list(2)), - "x_fac3md" = prior_factor("spike", contrast = "meandif", list(0)) - ) - prior_list2 <- list( - "sigma" = prior("cauchy", list(0, 1), list(0, 1)) - ) - model_syntax <- paste0( - "model{", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) + # Test JAGS_evaluate_formula with spike prior distributions using pre-fitted model + skip_on_os(c("mac", "linux", "solaris")) + skip_on_cran() + skip_if_no_fits() + skip_if_not_installed("rjags") - fit1 <- JAGS_fit( - model_syntax = model_syntax, - formula_list = list(mu = ~ x_fac2i + x_fac3o + x_fac3t + x_fac3md), - data = list(y = df_all$y, N = nrow(df_all)), - prior_list = prior_list2, - formula_data_list = list(mu = df_all), - formula_prior_list = list(mu = prior_list_all)) + # Load pre-fitted model with spike factor priors (all 4 contrast types) + fit_spike <- readRDS(file.path(temp_fits_dir, "fit_spike_factors.RDS")) - new_data <- data.frame( + # New data for prediction + new_data <- data.frame( x_fac2i = factor(c("A", "B", "A"), levels = c("A", "B")), x_fac3o = factor(c("A", "A", "B"), levels = c("A", "B", "C")), x_fac3t = factor(c("A", "B", "C"), levels = c("A", "B", "C")), x_fac3md = factor(c("B", "B", "C"), levels = c("A", "B", "C")) ) - new_samples <- JAGS_evaluate_formula(fit1, ~ x_fac2i + x_fac3o + x_fac3t + x_fac3md, "mu", new_data, attr(fit1, "prior_list")) - new_samples <- apply(new_samples, 1, mean) - - intercept_estimate <- JAGS_estimates_table(fit1)["(mu) intercept", "Mean"] - - expect_equivalent(intercept_estimate + 1, new_samples[1]) - expect_equivalent(intercept_estimate + 1 + 2, new_samples[2]) - expect_equivalent(intercept_estimate + 1 + 2, new_samples[3]) - - - # dealing with spike and slab and mixture priors - prior_list_all2 <- list( - "intercept" = prior_spike_and_slab(prior("normal", list(0, 5))), - "x_fac2i" = prior_mixture(list( - prior("spike", list(1)), - prior_factor("mnormal", contrast = "orthonormal", list(0, 1)) - ), is_null = c(T, F)), - "x_fac3o" = prior_spike_and_slab(prior_factor("mnormal", contrast = "orthonormal", list(0, 1))), - "x_fac3t" = prior_mixture(list( - prior_factor("normal", contrast = "treatment", list(0, 1)), - prior("spike", list(0)) - ), is_null = c(T, F)) + + # Note: fit_spike_factors uses formula ~ x_fac2i + x_fac3o + x_fac3t + x_fac3md - 1 + # with spike priors: independent(1), orthonormal(0), treatment(2), meandif(0) + prior_list <- attr(fit_spike, "prior_list") + new_samples <- JAGS_evaluate_formula(fit_spike, ~ x_fac2i + x_fac3o + x_fac3t + x_fac3md - 1, "mu", new_data, prior_list) + new_samples_mean <- apply(new_samples, 1, mean) + + # Verify spike values are correctly applied: + # - x_fac2i independent spike(1): each level gets value 1 + # - x_fac3o orthonormal spike(0): contrast coefficients are 0 + # - x_fac3t treatment spike(2): non-reference levels get value 2 + # - x_fac3md meandif spike(0): differences from mean are 0 + # Row 1: A(1) + A(0) + A(ref=0) + B(0) = 1 + # Row 2: B(1) + A(0) + B(2) + B(0) = 3 + # Row 3: A(1) + B(0) + C(2) + C(0) = 3 + expect_equal(new_samples_mean[1], 1, tolerance = 0.01, ignore_attr = TRUE) + expect_equal(new_samples_mean[2], 3, tolerance = 0.01, ignore_attr = TRUE) + expect_equal(new_samples_mean[3], 3, tolerance = 0.01, ignore_attr = TRUE) +}) + +test_that("JAGS evaluate formula works with spike-and-slab and mixture priors", { + + # Test JAGS_evaluate_formula with spike-and-slab and mixture priors using pre-fitted model + skip_on_os(c("mac", "linux", "solaris")) + skip_on_cran() + skip_if_no_fits() + skip_if_not_installed("rjags") + + # Load pre-fitted joint complex model (mixture intercept, spike-and-slab continuous, spike-and-slab factor) + fit_joint <- readRDS(file.path(temp_fits_dir, "fit_joint_complex.RDS")) + + # New data for prediction + new_data <- data.frame( + x_cont1 = c(0, 1, -1), + x_fac3t = factor(c("A", "B", "C"), levels = c("A", "B", "C")) ) - fit2 <- JAGS_fit( - model_syntax = model_syntax, - formula_list = list(mu = ~ x_fac2i + x_fac3o + x_fac3t), - data = list(y = df_all$y, N = nrow(df_all)), - prior_list = prior_list2, - formula_data_list = list(mu = df_all), - formula_prior_list = list(mu = prior_list_all2), chains = 1, adapt = 100, burnin = 100, sample = 200) - new_samples <- JAGS_evaluate_formula(fit2, ~ x_fac2i + x_fac3o + x_fac3t, "mu", new_data, attr(fit2, "prior_list")) - expect_equivalent(dim(new_samples), c(3, 200)) + # fit_joint_complex uses formula ~ x_cont1 + x_fac3t + prior_list <- attr(fit_joint, "prior_list") + new_samples <- JAGS_evaluate_formula(fit_joint, ~ x_cont1 + x_fac3t, "mu", new_data, prior_list) + # Should return samples for 3 new data points x number of posterior samples + expect_equal(nrow(new_samples), 3) + expect_equal(ncol(new_samples), 1000) }) test_that("Expression handling functions work", { @@ -794,12 +257,12 @@ test_that("Expression handling functions work", { expect_equal(.extract_expressions(f5), list("x")) expect_equal(.extract_expressions(f6), list("x", "b")) - expect_equal(.remove_expressions(f1), formula(y ~ 1)) - expect_equal(.remove_expressions(f2), formula(y ~ z)) - expect_equal(.remove_expressions(f3), formula(y ~ 1)) - expect_equal(.remove_expressions(f4), formula(y ~ z)) - expect_equal(.remove_expressions(f5), formula(y ~ z)) - expect_equal(.remove_expressions(f6), formula(y ~ z)) + expect_equal(.remove_expressions(f1), formula(y ~ 1), ignore_formula_env = TRUE) + expect_equal(.remove_expressions(f2), formula(y ~ z), ignore_formula_env = TRUE) + expect_equal(.remove_expressions(f3), formula(y ~ 1), ignore_formula_env = TRUE) + expect_equal(.remove_expressions(f4), formula(y ~ z), ignore_formula_env = TRUE) + expect_equal(.remove_expressions(f5), formula(y ~ z), ignore_formula_env = TRUE) + expect_equal(.remove_expressions(f6), formula(y ~ z), ignore_formula_env = TRUE) }) test_that("Random effects handling functions work", { @@ -840,13 +303,13 @@ test_that("Random effects handling functions work", { expect_equal(.extract_random_effects(f6), t2) expect_equal(.extract_random_effects(f7), t3) - expect_equal(.remove_random_effects(f1), formula( ~ 1)) - expect_equal(.remove_random_effects(f2), formula( ~ x_cont1)) - expect_equal(.remove_random_effects(f3), formula( ~ 1)) - expect_equal(.remove_random_effects(f4), formula( ~ 1)) - expect_equal(.remove_random_effects(f5), formula( ~ x_cont1)) - expect_equal(.remove_random_effects(f6), formula( ~ x_cont1)) - expect_equal(.remove_random_effects(f7), formula( ~ x_cont1 + x_cont2)) + expect_equal(.remove_random_effects(f1), formula( ~ 1), ignore_formula_env = TRUE) + expect_equal(.remove_random_effects(f2), formula( ~ x_cont1), ignore_formula_env = TRUE) + expect_equal(.remove_random_effects(f3), formula( ~ 1), ignore_formula_env = TRUE) + expect_equal(.remove_random_effects(f4), formula( ~ 1), ignore_formula_env = TRUE) + expect_equal(.remove_random_effects(f5), formula( ~ x_cont1), ignore_formula_env = TRUE) + expect_equal(.remove_random_effects(f6), formula( ~ x_cont1), ignore_formula_env = TRUE) + expect_equal(.remove_random_effects(f7), formula( ~ x_cont1 + x_cont2), ignore_formula_env = TRUE) }) @@ -859,24 +322,245 @@ test_that("-1 (no intercept) formula handling works correctly", { x_fac3i = factor(rep(c("A", "B", "C"), 20), levels = c("A", "B", "C")), x_cont = rnorm(60) ) - + # Test 1: Basic -1 formula functionality prior_list_basic <- list( "x_fac3md" = prior_factor("mnormal", contrast = "meandif", list(0, 1)) ) - result_basic <- JAGS_formula(~ x_fac3md - 1, parameter = "mu", - data = df_test[, "x_fac3md", drop = FALSE], + result_basic <- JAGS_formula(~ x_fac3md - 1, parameter = "mu", + data = df_test[, "x_fac3md", drop = FALSE], prior_list = prior_list_basic) - + # The -1 should automatically add spike(0) intercept expect_true("mu_intercept" %in% names(result_basic$prior_list)) expect_true(is.prior.point(result_basic$prior_list$mu_intercept)) expect_equal(result_basic$prior_list$mu_intercept$parameters$location, 0) expect_true(grepl("mu_intercept", result_basic$formula_syntax)) - + # Test 2: Helper function test - expect_equal(.add_intercept_to_formula(~ x - 1), ~ x) - expect_equal(.add_intercept_to_formula(~ x + y - 1), ~ x + y) - expect_equal(.add_intercept_to_formula(~ - 1), ~ 1) + expect_equal(.add_intercept_to_formula(~ x - 1), ~ x, ignore_formula_env = TRUE) + expect_equal(.add_intercept_to_formula(~ x + y - 1), ~ x + y, ignore_formula_env = TRUE) + expect_equal(.add_intercept_to_formula(~ - 1), ~ 1, ignore_formula_env = TRUE) + + expect_equal(.add_intercept_to_formula(~ x + 0), ~ x, ignore_formula_env = TRUE) + expect_equal(.add_intercept_to_formula(~ x + y + 0), ~ x + y, ignore_formula_env = TRUE) + expect_equal(.add_intercept_to_formula(~ 0), ~ 1, ignore_formula_env = TRUE) + +}) + +test_that("log(intercept) attribute works for specifying log(int) + sum(beta_i * x_i) models", { + + # this is helpful for specifying models for e.g., standard deviation where the output must be positive, + # but we want the intercept to be specified on the original scale - we can take exp() of the whole formula output + + # setup test data + set.seed(1) + df_test <- data.frame( + x_fac3md = factor(rep(c("A", "B", "C"), 20), levels = c("A", "B", "C")), + x_fac3i = factor(rep(c("A", "B", "C"), 20), levels = c("A", "B", "C")), + x_cont = rnorm(60) + ) + + # Test 1: Basic -1 formula functionality + prior_list_basic <- list( + "intercept" = prior("normal", list(0, 1)), + "x_fac3md" = prior_factor("mnormal", contrast = "meandif", list(0, 1)) + ) + + # no log intercept + result_basic <- JAGS_formula(~ 1 + x_fac3md, parameter = "mu", + data = df_test[, "x_fac3md", drop = FALSE], + prior_list = prior_list_basic) + + # log intercept + formula <- ~ 1 + x_fac3md + attr(formula, "log(intercept)") <- TRUE + result_log <- JAGS_formula(formula, parameter = "mu", + data = df_test[, "x_fac3md", drop = FALSE], + prior_list = prior_list_basic) + + # generates normal intercept + expect_equal( + result_basic[["formula_syntax"]], + "for(i in 1:N_mu){\n mu[i] = mu_intercept + inprod(mu_x_fac3md, mu_data_x_fac3md[i,])\n}\n" + ) + + # generates log intercept + expect_equal( + result_log[["formula_syntax"]], + "for(i in 1:N_mu){\n mu[i] = log(mu_intercept) + inprod(mu_x_fac3md, mu_data_x_fac3md[i,])\n}\n" + ) + + # everything else should match + result_basic[["formula_syntax"]] <- NULL + result_log[["formula_syntax"]] <- NULL + result_basic[["formula"]] <- NULL + result_log[["formula"]] <- NULL + expect_equal(result_basic, result_log) +}) + +test_that("JAGS_evaluate_formula works with log(intercept) attribute", { + + # Test that JAGS_evaluate_formula correctly applies log() transformation to intercept + # when the formula has the log(intercept) attribute set + + skip_if_not_installed("coda") + + # Setup: simple data for testing + set.seed(1) + df_test <- data.frame( + x_cont = rnorm(10) + ) + + # Create prior list with gamma prior for intercept (must be positive for log) + prior_list <- list( + "intercept" = prior("gamma", list(2, 1)), + "x_cont" = prior("normal", list(0, 1)) + ) + + # Process formula to get prior_list with parameter names + formula_result <- JAGS_formula(~ x_cont, parameter = "mu", data = df_test, prior_list = prior_list) + prior_list_processed <- formula_result$prior_list + + + # Create mock samples: intercept = 2, x_cont = 0.5 + samples <- matrix(c(2, 0.5), nrow = 1) + colnames(samples) <- c("mu_intercept", "mu_x_cont") + samples <- coda::as.mcmc.list(coda::as.mcmc(samples)) + + # New data for prediction + new_data <- data.frame(x_cont = c(0, 1, -1)) + + # Test without log(intercept): result = intercept + x_cont * data + # For x_cont = 0: result = 2 + 0.5 * 0 = 2 + # For x_cont = 1: result = 2 + 0.5 * 1 = 2.5 + # For x_cont = -1: result = 2 + 0.5 * (-1) = 1.5 + formula_no_log <- ~ x_cont + result_no_log <- JAGS_evaluate_formula(samples, formula_no_log, "mu", new_data, prior_list_processed) + expect_equal(as.vector(result_no_log[,1]), c(2, 2.5, 1.5), tolerance = 1e-10) + + # Test with log(intercept): result = log(intercept) + x_cont * data + # For x_cont = 0: result = log(2) + 0.5 * 0 = log(2) + # For x_cont = 1: result = log(2) + 0.5 * 1 = log(2) + 0.5 + # For x_cont = -1: result = log(2) + 0.5 * (-1) = log(2) - 0.5 + formula_log <- ~ x_cont + attr(formula_log, "log(intercept)") <- TRUE + result_log <- JAGS_evaluate_formula(samples, formula_log, "mu", new_data, prior_list_processed) + expect_equal(as.vector(result_log[,1]), c(log(2), log(2) + 0.5, log(2) - 0.5), tolerance = 1e-10) +}) + +test_that("Default priors (__default_factor and __default_continuous) work correctly", { + + # setup test data + set.seed(1) + df_test <- data.frame( + x_cont1 = rnorm(60), + x_cont2 = rnorm(60), + x_fac3 = factor(rep(c("A", "B", "C"), 20), levels = c("A", "B", "C")), + x_fac2 = factor(rep(c("X", "Y"), 30), levels = c("X", "Y")) + ) + + # Test 1: Only __default_continuous - applies to intercept and continuous predictors + prior_list_cont_default <- list( + "__default_continuous" = prior("normal", list(0, 1)) + ) + result1 <- JAGS_formula(~ x_cont1 + x_cont2, parameter = "mu", + data = df_test, prior_list = prior_list_cont_default) + + # Check that intercept and both continuous predictors got the default prior + expect_true("mu_intercept" %in% names(result1$prior_list)) + expect_true("mu_x_cont1" %in% names(result1$prior_list)) + expect_true("mu_x_cont2" %in% names(result1$prior_list)) + expect_equal(result1$prior_list$mu_intercept$distribution, "normal") + expect_equal(result1$prior_list$mu_x_cont1$distribution, "normal") + expect_equal(result1$prior_list$mu_x_cont2$distribution, "normal") + + # Test 2: Only __default_factor - continuous predictors must still be specified + prior_list_fac_default <- list( + "intercept" = prior("normal", list(0, 5)), + "x_cont1" = prior("cauchy", list(0, 1)), + "__default_factor" = prior_factor("normal", list(0, 0.5), contrast = "treatment") + ) + result2 <- JAGS_formula(~ x_cont1 + x_fac3 + x_fac2, parameter = "mu", + data = df_test, prior_list = prior_list_fac_default) + + # Check that factors got the default prior + expect_true("mu_x_fac3" %in% names(result2$prior_list)) + expect_true("mu_x_fac2" %in% names(result2$prior_list)) + expect_equal(result2$prior_list$mu_x_fac3$distribution, "normal") + expect_equal(result2$prior_list$mu_x_fac2$distribution, "normal") + # Check that explicit priors are preserved + expect_equal(result2$prior_list$mu_intercept$distribution, "normal") + expect_equal(result2$prior_list$mu_intercept$parameters$mean, 0) + expect_equal(result2$prior_list$mu_intercept$parameters$sd, 5) + expect_equal(result2$prior_list$mu_x_cont1$distribution, "t") # cauchy is internally stored as t + + # Test 3: Both defaults - all terms get assigned correctly + prior_list_both_defaults <- list( + "__default_continuous" = prior("normal", list(0, 2)), + "__default_factor" = prior_factor("normal", list(0, 1), contrast = "treatment") + ) + result3 <- JAGS_formula(~ x_cont1 + x_fac3, parameter = "mu", + data = df_test, prior_list = prior_list_both_defaults) + + expect_equal(result3$prior_list$mu_intercept$distribution, "normal") + expect_equal(result3$prior_list$mu_intercept$parameters$sd, 2) # from continuous default + expect_equal(result3$prior_list$mu_x_cont1$parameters$sd, 2) # from continuous default + expect_equal(result3$prior_list$mu_x_fac3$parameters$sd, 1) # from factor default + + # Test 4: Explicit priors override defaults + prior_list_override <- list( + "intercept" = prior("cauchy", list(0, 10)), # explicit override + "__default_continuous" = prior("normal", list(0, 1)), + "__default_factor" = prior_factor("normal", list(0, 0.5), contrast = "treatment"), + "x_fac3" = prior_factor("mnormal", list(0, 2), contrast = "orthonormal") # explicit override + ) + result4 <- JAGS_formula(~ x_cont1 + x_fac3 + x_fac2, parameter = "mu", + data = df_test, prior_list = prior_list_override) + + # Explicit priors should be used + expect_equal(result4$prior_list$mu_intercept$distribution, "t") # cauchy is internally stored as t + expect_equal(result4$prior_list$mu_x_fac3$distribution, "mnormal") + expect_equal(result4$prior_list$mu_x_fac3$parameters$sd, 2) + # Default priors for non-specified terms + expect_equal(result4$prior_list$mu_x_cont1$distribution, "normal") + expect_equal(result4$prior_list$mu_x_fac2$distribution, "normal") + expect_equal(result4$prior_list$mu_x_fac2$parameters$sd, 0.5) + + # Test 5: Interactions use factor default when they involve factors + prior_list_interaction <- list( + "__default_continuous" = prior("normal", list(0, 1)), + "__default_factor" = prior_factor("mnormal", list(0, 0.5), contrast = "orthonormal") + ) + result5 <- JAGS_formula(~ x_cont1 * x_fac3, parameter = "mu", + data = df_test, prior_list = prior_list_interaction) + + # x_cont1:x_fac3 interaction involves a factor, so should get factor default + expect_true("mu_x_cont1__xXx__x_fac3" %in% names(result5$prior_list)) + expect_equal(result5$prior_list[["mu_x_cont1__xXx__x_fac3"]]$distribution, "mnormal") + + # Test 6: Error when term is missing and no appropriate default + prior_list_missing <- list( + "__default_continuous" = prior("normal", list(0, 1)) + # no __default_factor, and x_fac3 not specified + ) + expect_error( + JAGS_formula(~ x_cont1 + x_fac3, parameter = "mu", + data = df_test, prior_list = prior_list_missing), + "missing" + ) + + # Test 7: Reserved names cannot be used as variable names in data + df_bad <- data.frame( + `__default_factor` = rnorm(10), + x = rnorm(10), + check.names = FALSE + ) + expect_error( + JAGS_formula(~ x, parameter = "mu", data = df_bad, + prior_list = list("intercept" = prior("normal", list(0, 1)), + "x" = prior("normal", list(0, 1)))), + "__default_factor" + ) }) diff --git a/tests/testthat/test-marginal-distributions.R b/tests/testthat/test-JAGS-marginal-distributions.R similarity index 78% rename from tests/testthat/test-marginal-distributions.R rename to tests/testthat/test-JAGS-marginal-distributions.R index cac2232f..c958388f 100644 --- a/tests/testthat/test-marginal-distributions.R +++ b/tests/testthat/test-JAGS-marginal-distributions.R @@ -1,21 +1,48 @@ -context("Marginal distributions") -set.seed(1) +# ============================================================================ # +# TEST FILE: JAGS Marginal Distributions +# ============================================================================ # +# +# PURPOSE: +# Tests for marginal_posterior, ensemble_inference, mix_posteriors, +# and related functions. Uses pre-fitted models from test-00-model-fits.R. +# +# DEPENDENCIES: +# - rjags, bridgesampling: JAGS model fitting and marginal likelihood +# - common-functions.R: temp_fits_dir, skip_if_no_fits, test_reference_table +# +# SKIP CONDITIONS: +# - skip_if_no_fits(): Pre-fitted models required +# - skip_if_not_installed("rjags"), skip_if_not_installed("bridgesampling") +# - skip_on_os(): Multivariate sampling differs across OSes (meandif priors) +# +# MODELS/FIXTURES: +# - fit_marginal_0, fit_marginal_1 +# +# TAGS: @evaluation, @JAGS, @model-averaging, @marginal +# ============================================================================ # + +# Reference directory for table outputs +REFERENCE_DIR <<- testthat::test_path("..", "results", "JAGS-marginal-distributions") + +# Load common test helpers +source(testthat::test_path("common-functions.R")) + +# File-level skips: All tests in this file require pre-fitted models +skip_if_no_fits() +skip_if_not_installed("rjags") +skip_if_not_installed("bridgesampling") test_that("Marginal distribution prior and posterior functions work", { skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes - skip_on_cran() - ### complex formula including scaling ---- - set.seed(1) - df_all <- data.frame( - x_cont1 = rnorm(180), - x_fac2t = factor(rep(c("A", "B"), 90), levels = c("A", "B")), - x_fac3md = factor(rep(c("A", "B", "C"), 60), levels = c("A", "B", "C")) - ) - df_all$y <- rnorm(180, 0.1, 0.5) + 0.5 + 0.20 * df_all$x_cont1 + - ifelse(df_all$x_fac3md == "A", 0.15, ifelse(df_all$x_fac3md == "B", -0.15, 0)) + # Load pre-fitted marginal distribution models + fit0 <- readRDS(file.path(temp_fits_dir, "fit_marginal_0.RDS")) + fit1 <- readRDS(file.path(temp_fits_dir, "fit_marginal_1.RDS")) + marglik0 <- readRDS(file.path(temp_marglik_dir, "fit_marginal_0.RDS")) + marglik1 <- readRDS(file.path(temp_marglik_dir, "fit_marginal_1.RDS")) + # Define prior lists (needed for manual mixing validation and prior_samples) prior_list_0 <- list( "intercept" = prior("normal", list(0, 1)), "x_cont1" = prior("normal", list(0, 1)), @@ -35,46 +62,6 @@ test_that("Marginal distribution prior and posterior functions work", { ) attr(prior_list_0$x_cont1, "multiply_by") <- "sigma" attr(prior_list_1$x_cont1, "multiply_by") <- "sigma" - model_syntax <- paste0( - "model{", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) - log_posterior <- function(parameters, data){ - return(sum(stats::dnorm(data$y, mean = parameters[["mu"]], sd = parameters[["sigma"]], log = TRUE))) - } - model_formula <- list(mu = ~ x_cont1 + x_fac2t + x_cont1*x_fac3md) - - fit0 <- JAGS_fit( - model_syntax = model_syntax, data = list(y = df_all$y, N = nrow(df_all)), - prior_list = prior_list, - formula_list = model_formula, - formula_prior_list = list(mu = prior_list_0), - formula_data_list = list(mu = df_all)) - fit1 <- JAGS_fit( - model_syntax = model_syntax, data = list(y = df_all$y, N = nrow(df_all)), - prior_list = prior_list, - formula_list = model_formula, - formula_prior_list = list(mu = prior_list_1), - formula_data_list = list(mu = df_all)) - marglik0 <- JAGS_bridgesampling( - fit = fit0, - log_posterior = log_posterior, - data = list(y = df_all$y, N = nrow(df_all)), - prior_list = prior_list, - formula_list = model_formula, - formula_prior_list = list(mu = prior_list_0), - formula_data_list = list(mu = df_all)) - marglik1 <- JAGS_bridgesampling( - fit = fit1, - log_posterior = log_posterior, - data = list(y = df_all$y, N = nrow(df_all)), - prior_list = prior_list, - formula_list = model_formula, - formula_prior_list = list(mu = prior_list_1), - formula_data_list = list(mu = df_all)) # make the mixing equal marglik1$logml <- marglik0$logml @@ -581,29 +568,29 @@ test_that("Marginal distribution prior and posterior functions work", { prior_samples = FALSE)), "there are no prior samples for the posterior distribution") # simple restricted prior - expect_warning(Savage_Dickey_BF(marg_post_sigma)) + suppressWarnings(expect_warning(Savage_Dickey_BF(marg_post_sigma))) BF.marg_post_sigma <- suppressWarnings(Savage_Dickey_BF(marg_post_sigma)) - expect_equivalent(BF.marg_post_sigma, NaN) + expect_equal(BF.marg_post_sigma, NaN, ignore_attr = TRUE) expect_equal(attr(BF.marg_post_sigma, "warnings"), c("Prior samples do not span both sides of the null hypothesis. Check whether the prior distribution contain the null hypothesis in the first place. The Savage-Dickey density ratio is likely to be invalid.", "Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.")) # simple factor BF.marg_post_x_fac2t <- suppressWarnings(Savage_Dickey_BF(marg_post_simple_x_fac2t)) - expect_equivalent(BF.marg_post_x_fac2t, list("A" = 1, "B" = 1.660692), tolerance = 1e-3) + expect_equal(BF.marg_post_x_fac2t, list("A" = 1, "B" = 0.1793), tolerance = 1e-3, ignore_attr = TRUE) expect_equal(attr(BF.marg_post_x_fac2t[["A"]], "warnings"), c("There is a considerable cluster of posterior samples at the exact null hypothesis values. The Savage-Dickey density ratio is likely to be invalid.", "There is a considerable cluster of prior samples at the exact null hypothesis values. The Savage-Dickey density ratio is likely to be invalid.")) BF.marg_post_x_fac3md <- Savage_Dickey_BF(marg_post_x_fac3md, silent = TRUE) - expect_equivalent(BF.marg_post_x_fac3md, list("A" = Inf, "B" = Inf, "C" = Inf)) + expect_equal(BF.marg_post_x_fac3md, list("A" = Inf, "B" = Inf, "C" = Inf), ignore_attr = TRUE) BF2.marg_post_x_fac3md <- Savage_Dickey_BF(marg_post_x_fac3md, null_hypothesis = 0.5) - expect_equivalent(BF2.marg_post_x_fac3md, list("A" = 3.954431, "B" = 0.1405823, "C" = 0.1661251), tolerance = 1e-3) + expect_equal(BF2.marg_post_x_fac3md, list("A" = 4.5, "B" = 0.1316, "C" = 0.165), tolerance = 1e-3, ignore_attr = TRUE) BF2.marg_post_x_fac3md <- Savage_Dickey_BF(marg_post_x_fac3md, null_hypothesis = 0.5, normal_approximation = TRUE) - expect_equal(BF2.marg_post_x_fac3md, list("A" = 0.6342651, "B" = 0.1015235, "C" = 0.1267758), tolerance = 1e-3) + expect_equal(BF2.marg_post_x_fac3md, list("A" = 0.5918, "B" = 0.0996, "C" = 0.1266), tolerance = 1e-3) ### marginal_inference ---- out <- marginal_inference( @@ -684,44 +671,11 @@ test_that("Marginal distribution prior and posterior functions work", { # the previous BFs were based on model-averaged posteriors so they won't match # test summary table - expect_equal( - capture_output_lines(marginal_estimates_table(out$conditional, out$inference, parameters = c("mu_intercept", "mu_x_cont1", "mu_x_fac2t", "mu_x_fac3md", "mu_x_cont1__xXx__x_fac3md")), print = TRUE, width = 150), - c( " Mean Median 0.025 0.95 Inclusion BF" , - "(mu) intercept 0.616 0.616 0.518 0.691 Inf" , - "(mu) x_cont1[-1SD] 0.431 0.431 0.303 0.536 Inf" , - "(mu) x_cont1[0SD] 0.616 0.616 0.518 0.691 Inf" , - "(mu) x_cont1[1SD] 0.800 0.801 0.678 0.899 Inf" , - "(mu) x_fac2t[A] 0.613 0.614 0.503 0.700 Inf" , - "(mu) x_fac2t[B] 0.621 0.621 0.513 0.708 Inf" , - "(mu) x_fac3md[A] 0.770 0.772 0.618 0.893 Inf" , - "(mu) x_fac3md[B] 0.518 0.518 0.365 0.646 Inf" , - "(mu) x_fac3md[C] 0.550 0.551 0.405 0.674 Inf" , - "(mu) x_cont1:x_fac3md[-1SD, A] 0.556 0.556 0.344 0.734 Inf" , - "(mu) x_cont1:x_fac3md[0SD, A] 0.770 0.772 0.618 0.893 Inf" , - "(mu) x_cont1:x_fac3md[1SD, A] 0.984 0.985 0.791 1.140 Inf" , - "(mu) x_cont1:x_fac3md[-1SD, B] 0.372 0.372 0.159 0.556 10.816" , - "(mu) x_cont1:x_fac3md[0SD, B] 0.518 0.518 0.365 0.646 Inf" , - "(mu) x_cont1:x_fac3md[1SD, B] 0.665 0.664 0.464 0.830 Inf" , - "(mu) x_cont1:x_fac3md[-1SD, C] 0.373 0.373 0.171 0.541 69.939" , - "(mu) x_cont1:x_fac3md[0SD, C] 0.550 0.551 0.405 0.674 Inf" , - "(mu) x_cont1:x_fac3md[1SD, C] 0.727 0.727 0.524 0.904 Inf" , - "\033[0;31mmu_intercept: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" , - "\033[0;31mmu_x_cont1[-1SD]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" , - "\033[0;31mmu_x_cont1[0SD]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" , - "\033[0;31mmu_x_cont1[1SD]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" , - "\033[0;31mmu_x_fac2t[A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" , - "\033[0;31mmu_x_fac2t[B]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" , - "\033[0;31mmu_x_fac3md[A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" , - "\033[0;31mmu_x_fac3md[B]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" , - "\033[0;31mmu_x_fac3md[C]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" , - "\033[0;31mmu_x_cont1__xXx__x_fac3md[-1SD, A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m", - "\033[0;31mmu_x_cont1__xXx__x_fac3md[0SD, A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m", - "\033[0;31mmu_x_cont1__xXx__x_fac3md[1SD, A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m", - "\033[0;31mmu_x_cont1__xXx__x_fac3md[0SD, B]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m", - "\033[0;31mmu_x_cont1__xXx__x_fac3md[1SD, B]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m", - "\033[0;31mmu_x_cont1__xXx__x_fac3md[0SD, C]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m", - "\033[0;31mmu_x_cont1__xXx__x_fac3md[1SD, C]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" - )) + test_reference_table( + marginal_estimates_table(out$conditional, out$inference, parameters = c("mu_intercept", "mu_x_cont1", "mu_x_fac2t", "mu_x_fac3md", "mu_x_cont1__xXx__x_fac3md")), + "marginal_estimates_table_model_avg.txt", + info_msg = "marginal_estimates_table for model averaging" + ) # plots vdiffr::expect_doppelganger("plot_marginal-mu_x_fac2t-1", function(){plot_marginal(out$conditional, parameter = "mu_x_fac2t")}) @@ -822,17 +776,12 @@ test_that("Marginal distributions with spike and slab and mixture priors work", skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes skip_on_cran() + skip_if_not_installed("rjags") - ### complex formula including scaling ---- - set.seed(1) - df_all <- data.frame( - x_cont1 = rnorm(180), - x_fac2t = factor(rep(c("A", "B"), 90), levels = c("A", "B")), - x_fac3md = factor(rep(c("A", "B", "C"), 60), levels = c("A", "B", "C")) - ) - df_all$y <- rnorm(180, 0.1, 0.5) + 0.5 + 0.20 * df_all$x_cont1 + - ifelse(df_all$x_fac3md == "A", 0.15, ifelse(df_all$x_fac3md == "B", -0.15, 0)) + # Load pre-fitted spike-and-slab model + fit <- readRDS(file.path(temp_fits_dir, "fit_marginal_ss.RDS")) + # Define prior lists (needed for prior_samples validation in marginal_posterior) prior_pars <- list( "intercept" = prior("normal", list(0, 1)), "x_cont1" = prior_mixture(list( @@ -847,22 +796,6 @@ test_that("Marginal distributions with spike and slab and mixture priors work", "sigma" = prior("cauchy", list(0, 1), list(0, 5)) ) attr(prior_pars$x_cont1, "multiply_by") <- "sigma" - model_syntax <- paste0( - "model{", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) - - model_formula <- list(mu = ~ x_cont1 + x_fac2t + x_cont1*x_fac3md) - - fit <- JAGS_fit( - model_syntax = model_syntax, data = list(y = df_all$y, N = nrow(df_all)), - prior_list = prior_list, - formula_list = model_formula, - formula_prior_list = list(mu = prior_pars), - formula_data_list = list(mu = df_all)) mixed_posteriors <- as_mixed_posteriors( model = fit, @@ -1280,45 +1213,11 @@ test_that("Marginal distributions with spike and slab and mixture priors work", # the previous BFs were based on model-averaged posteriors so they won't match # test summary table (note that these differ from the first set of tests because of the different model settings) - expect_equal( - capture_output_lines(marginal_estimates_table(out$conditional, out$inference, parameters = c("mu_intercept", "mu_x_cont1", "mu_x_fac2t", "mu_x_fac3md", "mu_x_cont1__xXx__x_fac3md")), print = TRUE, width = 150), - c(" Mean Median 0.025 0.95 Inclusion BF" , - "(mu) intercept 0.617 0.617 0.542 0.681 Inf" , - "(mu) x_cont1[-1SD] 0.435 0.434 0.320 0.531 Inf" , - "(mu) x_cont1[0SD] 0.617 0.617 0.542 0.681 Inf" , - "(mu) x_cont1[1SD] 0.800 0.799 0.691 0.890 Inf" , - "(mu) x_fac2t[A] 0.617 0.617 0.542 0.681 Inf" , - "(mu) x_fac2t[B] 0.618 0.617 0.542 0.682 Inf" , - "(mu) x_fac3md[A] 0.778 0.778 0.651 0.886 Inf" , - "(mu) x_fac3md[B] 0.518 0.518 0.390 0.625 Inf" , - "(mu) x_fac3md[C] 0.554 0.554 0.427 0.662 Inf" , - "(mu) x_cont1:x_fac3md[-1SD, A] 0.590 0.592 0.407 0.729 Inf" , - "(mu) x_cont1:x_fac3md[0SD, A] 0.774 0.776 0.623 0.884 Inf" , - "(mu) x_cont1:x_fac3md[1SD, A] 0.958 0.959 0.802 1.084 Inf" , - "(mu) x_cont1:x_fac3md[-1SD, B] 0.342 0.341 0.182 0.483 158.472" , - "(mu) x_cont1:x_fac3md[0SD, B] 0.521 0.520 0.392 0.631 Inf" , - "(mu) x_cont1:x_fac3md[1SD, B] 0.700 0.699 0.549 0.827 Inf" , - "(mu) x_cont1:x_fac3md[-1SD, C] 0.375 0.374 0.226 0.501 Inf" , - "(mu) x_cont1:x_fac3md[0SD, C] 0.556 0.556 0.428 0.663 Inf" , - "(mu) x_cont1:x_fac3md[1SD, C] 0.737 0.738 0.579 0.871 Inf" , - "\033[0;31mmu_intercept: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" , - "\033[0;31mmu_x_cont1[-1SD]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" , - "\033[0;31mmu_x_cont1[0SD]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" , - "\033[0;31mmu_x_cont1[1SD]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" , - "\033[0;31mmu_x_fac2t[A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" , - "\033[0;31mmu_x_fac2t[B]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" , - "\033[0;31mmu_x_fac3md[A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" , - "\033[0;31mmu_x_fac3md[B]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" , - "\033[0;31mmu_x_fac3md[C]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" , - "\033[0;31mmu_x_cont1__xXx__x_fac3md[-1SD, A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m", - "\033[0;31mmu_x_cont1__xXx__x_fac3md[0SD, A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" , - "\033[0;31mmu_x_cont1__xXx__x_fac3md[1SD, A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" , - "\033[0;31mmu_x_cont1__xXx__x_fac3md[0SD, B]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" , - "\033[0;31mmu_x_cont1__xXx__x_fac3md[1SD, B]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" , - "\033[0;31mmu_x_cont1__xXx__x_fac3md[-1SD, C]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m", - "\033[0;31mmu_x_cont1__xXx__x_fac3md[0SD, C]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" , - "\033[0;31mmu_x_cont1__xXx__x_fac3md[1SD, C]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" - )) + test_reference_table( + marginal_estimates_table(out$conditional, out$inference, parameters = c("mu_intercept", "mu_x_cont1", "mu_x_fac2t", "mu_x_fac3md", "mu_x_cont1__xXx__x_fac3md")), + "marginal_estimates_table_spike_slab.txt", + info_msg = "marginal_estimates_table for spike-and-slab" + ) # plots vdiffr::expect_doppelganger("plot_marginal-ss-mu_x_fac2t-1", function(){plot_marginal(out$conditional, parameter = "mu_x_fac2t")}) @@ -1341,3 +1240,72 @@ test_that("Marginal distributions with spike and slab and mixture priors work", vdiffr::expect_doppelganger("plot_marginal-ss-int", plot_marginal(out$averaged, plot_type = "ggplot", parameter = "mu_intercept", prior = TRUE, dots_prior = list(lty = 2), xlim = c(-1, 1))) }) + + +test_that("Marginal distributions with one-sided weightfunction model work", { + + skip_on_os(c("mac", "linux", "solaris")) + skip_on_cran() + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + # Load pre-fitted one-sided weightfunction model + fit_wf <- readRDS(file.path(temp_fits_dir, "fit_wf_onesided.RDS")) + + mixed_posteriors <- as_mixed_posteriors( + model = fit_wf, + parameters = "omega" + ) + + # Not implemented for weightfunctions + # marginal_posterior(mixed_posteriors, parameter = "omega", prior_samples = TRUE) + temp_samples <- .as_mixed_priors.weightfunction(attr(fit_wf, "prior_list")[[1]], parameter = "omega") + + # Visual tests for weightfunction posteriors + vdiffr::expect_doppelganger("marginal-wf-onesided-hist", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) + + par(mfrow = c(1, 2)) + hist(mixed_posteriors$omega[,1], freq = FALSE, main = "omega[0,0.025]", breaks = 50, xlim = c(0, 1)) + lines(density(temp_samples[,1])) + hist(mixed_posteriors$omega[,2], freq = FALSE, main = "omega[0.025,1]", breaks = 50, xlim = c(0, 1)) + }) + +}) + + +test_that("Marginal distributions with independent factor model work", { + + skip_on_os(c("mac", "linux", "solaris")) + skip_on_cran() + skip_if_not_installed("rjags") + + # Load pre-fitted independent factor model + fit_ind <- readRDS(file.path(temp_fits_dir, "fit_factor_independent.RDS")) + + mixed_posteriors <- as_mixed_posteriors( + model = fit_ind, + parameters = "p1" + ) + marginal_posteriors <- marginal_posterior(mixed_posteriors, parameter = "p1", prior_samples = TRUE) + + # Visual tests for independent factor posteriors + vdiffr::expect_doppelganger("marginal-factor-independent-hist", function(){ + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) + + par(mfrow = c(1, 3)) + hist(mixed_posteriors$p1[,1], freq = FALSE, main = "p1[1] (level 1)", breaks = 50) + lines(density(marginal_posteriors[[1]])) + lines(density(attr(marginal_posteriors[[3]], "prior_samples")), lty = 2) + hist(mixed_posteriors$p1[,2], freq = FALSE, main = "p1[2] (level 2)", breaks = 50) + lines(density(marginal_posteriors[[2]])) + lines(density(attr(marginal_posteriors[[2]], "prior_samples")), lty = 2) + hist(mixed_posteriors$p1[,3], freq = FALSE, main = "p1[3] (level 3)", breaks = 50) + lines(density(marginal_posteriors[[3]])) + lines(density(attr(marginal_posteriors[[3]], "prior_samples")), lty = 2) + }) + +}) + diff --git a/tests/testthat/test-JAGS-marglik.R b/tests/testthat/test-JAGS-marglik.R index f47c0b0f..d68201d3 100644 --- a/tests/testthat/test-JAGS-marglik.R +++ b/tests/testthat/test-JAGS-marglik.R @@ -1,4 +1,34 @@ -context("JAGS marginal likelihood functions") +# ============================================================================ # +# TEST FILE: JAGS Marginal Likelihood Functions +# ============================================================================ # +# +# PURPOSE: +# Tests for JAGS marginal likelihood computation functions. +# Uses simple models where the log marginal likelihood is known to be 0 +# (for prior samples, the marginal likelihood for any proper prior is 1). +# +# DEPENDENCIES: +# - rjags: For JAGS model fitting +# - bridgesampling: For marginal likelihood computation +# +# SKIP CONDITIONS: +# - skip_if_not_installed("rjags") +# - Note: Creates fresh models, does not need pre-fitted models +# +# MODELS/FIXTURES: +# - Creates models with known analytical marginal likelihoods for validation +# +# TAGS: @evaluation, @JAGS, @marginal-likelihood +# ============================================================================ # + +# Load common test helpers +source(testthat::test_path("common-functions.R")) +skip_refit_if_cached("JAGS-marglik") + +# This file tests the JAGS marginal likelihood computation functions +# It uses simple models where the log marginal likelihood is known to be 0 +# (for prior samples, the marginal likelihood for any proper prior is 1, log(1) = 0) +# More complex consistency tests (e.g., including formulas etc part of `test-00-model-fits.R`) test_that("JAGS model functions work (simple)", { @@ -18,9 +48,7 @@ test_that("JAGS model functions work (simple)", { PEESE = prior_PEESE("gamma", list(1, 1)) #p12 = prior("bernoulli", list(0.75)) discrete priors are not supported with bridgesampling ) - log_posterior <- function(parameters, data){ - return(0) - } + log_posterior <- STANDARD_LOG_POSTERIOR for(i in seq_along(all_priors)){ @@ -49,9 +77,7 @@ test_that("JAGS model functions work (vector)", { p2 = prior("mcauchy", list(location = 0, scale = 1.5, K = 2)), p3 = prior("mt", list(location = 2, scale = 0.5, df = 5, K = 2)) ) - log_posterior <- function(parameters, data){ - return(0) - } + log_posterior <- STANDARD_LOG_POSTERIOR for(i in seq_along(all_priors)){ @@ -88,9 +114,7 @@ test_that("JAGS model functions work (factor)", { attr(all_priors[[4]], "levels") <- 1 attr(all_priors[[5]], "levels") <- 3 attr(all_priors[[6]], "levels") <- 3 - log_posterior <- function(parameters, data){ - return(0) - } + log_posterior <- STANDARD_LOG_POSTERIOR for(i in seq_along(all_priors)){ @@ -117,9 +141,7 @@ test_that("JAGS model functions work (spike and slab)", { p3 = prior_spike_and_slab(prior("invgamma", list(4, 5)), prior_inclusion = prior("point", list(.3))) ) - log_posterior <- function(parameters, data){ - return(0) - } + log_posterior <- STANDARD_LOG_POSTERIOR for(i in seq_along(all_priors)){ @@ -146,9 +168,7 @@ test_that("JAGS model functions work (weightfunctions)", { prior_weightfunction("one.sided", list(c(.05, 0.60), c(1, 1), c(1, 5))), prior_weightfunction("two.sided", list(c(.05), c(1, 1))) ) - log_posterior <- function(parameters, data){ - return(0) - } + log_posterior <- STANDARD_LOG_POSTERIOR for(i in seq_along(all_priors)){ @@ -189,9 +209,7 @@ test_that("JAGS model functions work (spikes)", { attr(all_priors$p4.5, "levels") <- 2 attr(all_priors$p5.5, "levels") <- 2 nuisance_prior <- list(sigma = prior("normal", list(0, 1))) - log_posterior <- function(parameters, data){ - return(0) - } + log_posterior <- STANDARD_LOG_POSTERIOR for(i in seq_along(all_priors)){ @@ -209,368 +227,283 @@ test_that("JAGS model functions work (spikes)", { }) -test_that("JAGS model functions work (complex scenario)", { +test_that("bridge sampling object function works",{ + + marglik0 <- bridgesampling_object() + marglik1 <- bridgesampling_object(1) + + expect_equal(marglik0$logml, -Inf) + expect_equal(marglik1$logml, 1) + expect_s3_class(marglik0, "bridge") + +}) + +test_that("JAGS marglik with formula works", { + + # Test marginal likelihood computation with formula interface + # Uses intercept-only formula with various priors + # When sampling from prior and computing marglik, the result should be ~0 (log(1)) skip_if_not_installed("rjags") - # tests different model estimation techniques and passing additional arguments + + # Simple data for the formula set.seed(1) - data <- list( - x = rnorm(50, 0, .5), - N = 50 - ) - priors1 <- list( - m = prior("normal", list(0, 1)), - s = prior("normal", list(0, 1), list(0, Inf)) - ) - priors2 <- list( - m = prior("normal", list(0, 1)), - s = prior("spike", list(1)) + df_test <- data.frame(x = rnorm(10)) + log_posterior <- STANDARD_LOG_POSTERIOR + + # Create formula prior list with intercept only + prior_list <- list( + "intercept" = prior("gamma", list(2, 2)), + "x" = prior("normal", list(0, 1)) ) - log_posterior <- function(parameters, data, return3){ - if(return3){ - return(3) - }else{ - return(sum(stats::dnorm(data$x, mean = parameters[["m"]], sd = parameters[["s"]], log = TRUE))) - } - } - model_syntax <- - "model{ - for(i in 1:N){ - x[i] ~ dnorm(m, pow(s, -2)) - } - }" + # Process formula to get JAGS syntax + formula_result <- JAGS_formula(~ 1 + x, parameter = "mu", data = df_test, prior_list = prior_list) + # Build JAGS model with formula priors + model_syntax <- JAGS_add_priors("model{}", formula_result$prior_list) + monitor <- JAGS_to_monitor(formula_result$prior_list) + inits <- JAGS_get_inits(formula_result$prior_list, chains = 2, seed = 1) - model1 <- rjags::jags.model( - file = textConnection(JAGS_add_priors(model_syntax, priors1)), - inits = JAGS_get_inits(priors1, chains = 2, seed = 1), - n.chains = 2, - data = data, - quiet = TRUE) - samples1 <- rjags::jags.samples( - model = model1, - variable.names = JAGS_to_monitor(priors1), - data = data, - n.iter = 5000, - quiet = TRUE, - progress.bar = "none") - marglik1 <- JAGS_bridgesampling( - samples1, - prior_list = priors1, - data = data, - log_posterior = log_posterior, - return3 = FALSE) - - runjags::runjags.options(silent.jags = TRUE, silent.runjags = TRUE) - fit2 <- runjags::run.jags( - model = JAGS_add_priors(model_syntax, priors2), - data = data, - inits = JAGS_get_inits(priors2, chains = 2, seed = 1), - monitor = JAGS_to_monitor(priors2), - n.chains = 2, - sample = 5000, - burnin = 1000, - adapt = 500, - summarise = FALSE + # Sample from prior using JAGS + set.seed(1) + model <- rjags::jags.model(file = textConnection(model_syntax), inits = inits, n.chains = 2, quiet = TRUE) + samples <- rjags::coda.samples(model = model, variable.names = monitor, n.iter = 5000, quiet = TRUE, progress.bar = "none") + + # Compute marginal likelihood using formula interface + marglik <- JAGS_bridgesampling( + fit = samples, + log_posterior = log_posterior, + data = list(), + prior_list = NULL, + formula_list = list(mu = ~ 1 + x), + formula_data_list = list(mu = df_test), + formula_prior_list = list(mu = prior_list) ) - marglik2 <- JAGS_bridgesampling( - fit2, - data = data, - prior_list = priors2, - log_posterior = log_posterior, - return3 = FALSE) - - marglik3 <- JAGS_bridgesampling( - fit2, - data = data, - prior_list = priors2, - log_posterior = log_posterior, - return3 = TRUE) - - - expect_equal(marglik1$logml, -31.944, tolerance = 1e-2) - expect_equal(marglik2$logml, -52.148, tolerance = 1e-2) - expect_equal(marglik3$logml, 1.489, tolerance = 1e-2) + + expect_equal(marglik$logml, 0, tolerance = 1e-3) }) -test_that("JAGS model functions work (formula)",{ +test_that("JAGS marglik with exp(intercept) formula works", { + + # Test marginal likelihood computation with formula interface + # Uses intercept-only formula with various priors + # When sampling from prior and computing marglik, the result should be ~0 (log(1)) + + skip_if_not_installed("rjags") + # Simple data for the formula set.seed(1) + df_test <- data.frame(x = rnorm(10)) + log_posterior <- STANDARD_LOG_POSTERIOR - data_formula <- data.frame( - x_cont1 = rnorm(300), - x_fac2t = factor(rep(c("A", "B"), 150), levels = c("A", "B")), - x_fac3t = factor(rep(c("A", "B", "C"), 100), levels = c("A", "B", "C")) - ) - data <- list( - y = rnorm(300, .4 * data_formula$x_cont1 + ifelse(data_formula$x_fac3t == "A", 0.0, ifelse(data_formula$x_fac3t == "B", -0.2, 0.4)), ifelse(data_formula$x_fac2t == "A", 0.5, 1)), - N = 300 + # Create formula prior list with intercept only + prior_list <- list( + "intercept" = prior("gamma", list(2, 2)), + "x" = prior("normal", list(0, 1)) ) - # create an empty model ---- - formula_list0 <- list( - mu = ~ x_cont1 + x_fac3t - ) - formula_data_list0 <- list( - mu = data_formula - ) - formula_prior_list0 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior("normal", list(0, 1)), - "x_fac3t" = prior_factor("normal", contrast = "treatment", list(0, 1)) - ) - ) - prior_list0 <- list( - sigma = prior("lognormal", list(0, 1)) - ) - model_syntax0 <- "model{}" + # Process formula to get JAGS syntax + formula <- ~ 1 + x + attr(formula, "log(intercept)") <- TRUE + formula_result <- JAGS_formula(formula, parameter = "mu", data = df_test, prior_list = prior_list) + expect_equal(formula_result$formula_syntax, "for(i in 1:N_mu){\n mu[i] = log(mu_intercept) + mu_x * mu_data_x[i]\n}\n") - fit0 <- JAGS_fit( - model_syntax = model_syntax0, data = list(), prior_list = prior_list0, - formula_list = formula_list0, formula_data_list = formula_data_list0, formula_prior_list = formula_prior_list0) + # Build JAGS model with formula priors + model_syntax <- JAGS_add_priors("model{}", formula_result$prior_list) + monitor <- JAGS_to_monitor(formula_result$prior_list) + inits <- JAGS_get_inits(formula_result$prior_list, chains = 2, seed = 1) - log_posterior0 <- function(parameters, data){ - return(0) - } + # Sample from prior using JAGS + set.seed(1) + model <- rjags::jags.model(file = textConnection(model_syntax), inits = inits, n.chains = 2, quiet = TRUE) + samples <- rjags::coda.samples(model = model, variable.names = monitor, n.iter = 5000, quiet = TRUE, progress.bar = "none") - marglik0 <- JAGS_bridgesampling( - fit = fit0, - log_posterior = log_posterior0, + # Compute marginal likelihood using formula interface + marglik <- JAGS_bridgesampling( + fit = samples, + log_posterior = log_posterior, data = list(), - prior_list = prior_list0, - formula_list = formula_list0, - formula_data_list = formula_data_list0, - formula_prior_list = formula_prior_list0 + prior_list = NULL, + formula_list = list(mu = formula), + formula_data_list = list(mu = df_test), + formula_prior_list = list(mu = prior_list) ) - expect_equal(marglik0$logml, 0, tolerance = 1e-3) + expect_equal(marglik$logml, 0, tolerance = 1e-3) +}) - # create model with mix of a formula and free parameters ---- - formula_list1 <- list( - mu = ~ x_cont1 + x_fac3t - ) - formula_data_list1 <- list( - mu = data_formula - ) - formula_prior_list1 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior("normal", list(0, 1)), - "x_fac3t" = prior_factor("normal", contrast = "treatment", list(0, 1)) - ) - ) - prior_list1 <- list( - sigma = prior("lognormal", list(0, 1)) +# Targeted tests for uncovered code paths in JAGS-marglik.R + +test_that("JAGS_bridgesampling_posterior input validation works", { + + posterior <- matrix(rnorm(30), nrow = 10, ncol = 3) + colnames(posterior) <- c("mu", "sigma", "x") + + # Input validation errors + + expect_error(JAGS_bridgesampling_posterior(data.frame(x = 1), prior_list = NULL), "'posterior' must be a matrix") + expect_error(JAGS_bridgesampling_posterior(posterior, prior_list = "x"), "'prior_list' must be a list.") + expect_error(JAGS_bridgesampling_posterior(posterior, prior_list = prior("normal", list(0, 1))), "'prior_list' must be a list of priors.") + expect_error(JAGS_bridgesampling_posterior(posterior, prior_list = list(x = 1)), "'prior_list' must be a list of priors.") + expect_error(JAGS_bridgesampling_posterior(posterior, prior_list = NULL, add_parameters = 1), "'add_parameters' must be a character") + expect_error(JAGS_bridgesampling_posterior(posterior, prior_list = NULL, add_parameters = "x", add_bounds = "x"), "'add_bounds' must be a list") + expect_error(JAGS_bridgesampling_posterior(posterior, prior_list = NULL, add_parameters = "x", add_bounds = list(a = 1)), "'add_bounds' must contain lower and upper bounds") + expect_error(JAGS_bridgesampling_posterior(posterior, prior_list = NULL, add_parameters = c("x", "y"), add_bounds = list(lb = 0, ub = 1)), "lb' and 'ub' must have the same lenght") + expect_error(JAGS_bridgesampling_posterior(posterior, prior_list = NULL, add_parameters = "x", add_bounds = list(lb = "a", ub = "b")), "lb' and 'ub' must be numeric") + + # Unsupported prior types + expect_error( + JAGS_bridgesampling_posterior(posterior, prior_list = list(p1 = prior_spike_and_slab(prior("normal", list(0, 1)), prior_inclusion = prior("beta", list(1, 1))))), + "spike and slab" ) - model_syntax1 <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" + expect_error( + JAGS_bridgesampling_posterior(posterior, prior_list = list(p1 = prior_mixture(list(prior("normal", list(0, 1)), prior("normal", list(1, 1))), is_null = c(TRUE, FALSE)))), + "prior mixture" ) - fit1 <- JAGS_fit( - model_syntax = model_syntax1, data = data, prior_list = prior_list1, - formula_list = formula_list1, formula_data_list = formula_data_list1, formula_prior_list = formula_prior_list1) + # Missing parameters + posterior_small <- matrix(rnorm(20), nrow = 10, ncol = 2) + colnames(posterior_small) <- c("a", "b") + expect_error(JAGS_bridgesampling_posterior(posterior_small, prior_list = list(x = prior("normal", list(0, 1)))), "'posterior' does not contain all") - log_posterior1 <- function(parameters, data){ - return(sum(stats::dnorm(data$y, mean = parameters[["mu"]], sd = parameters[["sigma"]], log = TRUE))) - } + # Successful case with add_parameters + result <- JAGS_bridgesampling_posterior(posterior, prior_list = list(mu = prior("normal", list(0, 1))), add_parameters = "x", add_bounds = list(lb = -Inf, ub = Inf)) + expect_true(is.matrix(result)) + expect_true("x" %in% colnames(result)) - marglik1 <- JAGS_bridgesampling( - fit = fit1, - log_posterior = log_posterior1, - data = data, - prior_list = prior_list1, - formula_list = formula_list1, - formula_data_list = formula_data_list1, - formula_prior_list = formula_prior_list1) - - # more of a consistency test - expect_equal(marglik1$logml, -370.87, tolerance = 1e-2) - - - # create model with mix of a formula and free scaled parameters ---- - prior_list1s <- prior_list1 - prior_list1s$scale3 <- prior("point", parameters = list(location = 1/3)) - formula_prior_list1s <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior("normal", list(0, 1/2)), - "x_fac3t" = prior_factor("normal", contrast = "treatment", list(0, 1*3)) - ) - ) - attr(formula_prior_list1s$mu$x_cont1, "multiply_by") <- 2 - attr(formula_prior_list1s$mu$x_fac3t, "multiply_by") <- "scale3" +}) - fit1s <- JAGS_fit( - model_syntax = model_syntax1, data = data, prior_list = prior_list1s, - formula_list = formula_list1, formula_data_list = formula_data_list1, formula_prior_list = formula_prior_list1s) +test_that("JAGS_marglik_priors input validation and edge cases work", { - log_posterior1s <- function(parameters, data){ - return(sum(stats::dnorm(data$y, mean = parameters[["mu"]], sd = parameters[["sigma"]], log = TRUE))) - } + # Empty prior_list returns empty list - marglik1s <- JAGS_bridgesampling( - fit = fit1s, - log_posterior = log_posterior1s, - data = data, - prior_list = prior_list1s, - formula_list = formula_list1, - formula_data_list = formula_data_list1, - formula_prior_list = formula_prior_list1s) + expect_equal(JAGS_marglik_priors(list(), prior_list = list()), list()) - # more of a consistency test - expect_equal(marglik1$logml, marglik1s$logml, tolerance = 1e-2) + # Input validation + expect_error(JAGS_marglik_priors(list(), prior_list = "x"), "'prior_list' must be a list.") + expect_error(JAGS_marglik_priors(list(), prior_list = prior("normal", list(0, 1))), "'prior_list' must be a list of priors.") + expect_error(JAGS_marglik_priors(list(), prior_list = list(x = 1)), "'prior_list' must be a list of priors.") +}) - # create model with two formulas ---- - formula_list2 <- list( - mu = ~ x_cont1 + x_fac3t, - sigma = ~ x_fac2t - ) +test_that("JAGS_marglik_parameters input validation and edge cases work", { + + # Test: empty prior_list returns empty list + result <- JAGS_marglik_parameters(list(), prior_list = list()) + expect_equal(result, list()) - formula_data_list2 <- list( - mu = data_formula, - sigma = data_formula + # Test: prior_list must be a list + expect_error( + JAGS_marglik_parameters(list(), prior_list = "not_a_list"), + "'prior_list' must be a list." ) - formula_prior_list2 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior("normal", list(0, 1)), - "x_fac3t" = prior_factor("normal", contrast = "treatment", list(0, 1)) - ), - sigma = list( - "intercept" = prior("normal", list(0, 1)), - "x_fac2t" = prior_factor("normal", contrast = "treatment", list(0, 1)) - ) + # Test: prior_list must be a list of priors (single prior passed) + expect_error( + JAGS_marglik_parameters(list(), prior_list = prior("normal", list(0, 1))), + "'prior_list' must be a list of priors." ) - model_syntax2 <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(exp(sigma[i]), 2))\n", - "}\n", - "}" + + # Test: prior_list must be a list of priors (non-prior elements) + expect_error( + JAGS_marglik_parameters(list(), prior_list = list(x = 1)), + "'prior_list' must be a list of priors." ) - fit2 <- JAGS_fit( - model_syntax = model_syntax2, data = data, prior_list = NULL, - formula_list = formula_list2, formula_data_list = formula_data_list2, formula_prior_list = formula_prior_list2) +}) - log_posterior2 <- function(parameters, data){ - return(sum(stats::dnorm(data$y, mean = parameters[["mu"]], sd = exp(parameters[["sigma"]]), log = TRUE))) - } +test_that(".fit_to_posterior handles different input types", { - marglik2 <- JAGS_bridgesampling( - fit = fit2, - log_posterior = log_posterior2, - data = data, - prior_list = NULL, - formula_list = formula_list2, - formula_data_list = formula_data_list2, - formula_prior_list = formula_prior_list2) + skip_if_not_installed("rjags") + skip_if_not_installed("coda") - # more of a consistency test - expect_equal(marglik2$logml, -351.43, tolerance = 1e-2) + prior_list <- list(mu = prior("normal", list(0, 1))) + model_syntax <- JAGS_add_priors("model{}", prior_list) + monitor <- JAGS_to_monitor(prior_list) + inits <- JAGS_get_inits(prior_list, chains = 2, seed = 1) + log_posterior <- STANDARD_LOG_POSTERIOR - # create a model with spike factor priors ---- - formula_list3 <- list( - mu = ~ x_cont1 + x_fac3t - ) - formula_list3c <- list( - mu = ~ x_cont1 - ) - formula_data_list3 <- list( - mu = data_formula - ) - formula_prior_list3a <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior("normal", list(0, 1)), - "x_fac3t" = prior_factor("spike", contrast = "treatment", list(0)) - ) - ) - formula_prior_list3b <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior("normal", list(0, 1)), - "x_fac3t" = prior_factor("spike", contrast = "meandif", list(0)) - ) - ) - formula_prior_list3c <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior("normal", list(0, 1)) - ) - ) - prior_list3 <- list( - sigma = prior("lognormal", list(0, 1)) - ) - model_syntax3 <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) + set.seed(1) + model <- rjags::jags.model(file = textConnection(model_syntax), inits = inits, n.chains = 2, quiet = TRUE) - fit3a <- JAGS_fit( - model_syntax = model_syntax3, data = data, prior_list = prior_list3, - formula_list = formula_list3, formula_data_list = formula_data_list3, formula_prior_list = formula_prior_list3a) - fit3b <- JAGS_fit( - model_syntax = model_syntax3, data = data, prior_list = prior_list3, - formula_list = formula_list3, formula_data_list = formula_data_list3, formula_prior_list = formula_prior_list3b) - fit3c <- JAGS_fit( - model_syntax = model_syntax3, data = data, prior_list = prior_list3, - formula_list = formula_list3c, formula_data_list = formula_data_list3, formula_prior_list = formula_prior_list3c) - - log_posterior3 <- function(parameters, data){ - return(sum(stats::dnorm(data$y, mean = parameters[["mu"]], sd = parameters[["sigma"]], log = TRUE))) - } + # mcmc.list (rjags::coda.samples) + samples_mcmc_list <- rjags::coda.samples(model = model, variable.names = monitor, n.iter = 100, quiet = TRUE, progress.bar = "none") + marglik <- JAGS_bridgesampling(samples_mcmc_list, prior_list = prior_list, data = list(), log_posterior = log_posterior) + expect_s3_class(marglik, "bridge") - marglik3a <- JAGS_bridgesampling( - fit = fit3a, - log_posterior = log_posterior3, - data = data, - prior_list = prior_list3, - formula_list = formula_list3, - formula_data_list = formula_data_list3, - formula_prior_list = formula_prior_list3a) - marglik3b <- JAGS_bridgesampling( - fit = fit3b, - log_posterior = log_posterior3, - data = data, - prior_list = prior_list3, - formula_list = formula_list3, - formula_data_list = formula_data_list3, - formula_prior_list = formula_prior_list3b) - marglik3c <- JAGS_bridgesampling( - fit = fit3c, - log_posterior = log_posterior3, - data = data, - prior_list = prior_list3, - formula_list = formula_list3c, - formula_data_list = formula_data_list3, - formula_prior_list = formula_prior_list3c) - - # more of a consistency test - expect_equal(marglik3a$logml, marglik3c$logml, tolerance = 1e-2) - expect_equal(marglik3b$logml, marglik3c$logml, tolerance = 1e-2) + # mcmc (coda::as.mcmc) + samples_mcmc <- coda::as.mcmc(samples_mcmc_list[[1]]) + marglik_mcmc <- JAGS_bridgesampling(samples_mcmc, prior_list = prior_list, data = list(), log_posterior = log_posterior) + expect_s3_class(marglik_mcmc, "bridge") + + # Error for unsupported input + expect_error(JAGS_bridgesampling("bad_input", prior_list = prior_list, data = list(), log_posterior = log_posterior), "not implemented") }) -test_that("bridge sampling object function works",{ +test_that(".fit_to_posterior handles jags.samples output", { - marglik0 <- bridgesampling_object() - marglik1 <- bridgesampling_object(1) + skip_if_not_installed("rjags") - expect_equal(marglik0$logml, -Inf) - expect_equal(marglik1$logml, 1) - expect_s3_class(marglik0, "bridge") + # Scalar parameter + prior_list <- list(mu = prior("normal", list(0, 1)), sigma = prior("gamma", list(1, 1))) + model_syntax <- JAGS_add_priors("model{}", prior_list) + monitor <- JAGS_to_monitor(prior_list) + inits <- JAGS_get_inits(prior_list, chains = 2, seed = 1) + log_posterior <- STANDARD_LOG_POSTERIOR + + set.seed(1) + model <- rjags::jags.model(file = textConnection(model_syntax), inits = inits, n.chains = 2, quiet = TRUE) + samples_jags <- rjags::jags.samples(model = model, variable.names = monitor, n.iter = 100, progress.bar = "none") + marglik_jags <- JAGS_bridgesampling(samples_jags, prior_list = prior_list, data = list(), log_posterior = log_posterior) + expect_s3_class(marglik_jags, "bridge") + +}) + +test_that(".fit_to_posterior handles vector parameters in jags.samples", { + + skip_if_not_installed("rjags") + + # Vector parameter (K > 1) + prior_list <- list(p = prior("mnormal", list(mean = 0, sd = 1, K = 3))) + model_syntax <- JAGS_add_priors("model{}", prior_list) + monitor <- JAGS_to_monitor(prior_list) + inits <- JAGS_get_inits(prior_list, chains = 2, seed = 1) + log_posterior <- STANDARD_LOG_POSTERIOR + + set.seed(1) + model <- rjags::jags.model(file = textConnection(model_syntax), inits = inits, n.chains = 2, quiet = TRUE) + samples_jags <- rjags::jags.samples(model = model, variable.names = monitor, n.iter = 100, progress.bar = "none") + marglik_jags <- JAGS_bridgesampling(samples_jags, prior_list = prior_list, data = list(), log_posterior = log_posterior) + expect_s3_class(marglik_jags, "bridge") + +}) + +test_that("JAGS_bridgesampling handles runjags output", { + + skip_if_not_installed("runjags") + skip_if_not_installed("rjags") + + prior_list <- list(mu = prior("normal", list(0, 1))) + model_syntax <- JAGS_add_priors("model{}", prior_list) + log_posterior <- STANDARD_LOG_POSTERIOR + + set.seed(1) + fit <- suppressWarnings(runjags::run.jags( + model = model_syntax, + monitor = "mu", + n.chains = 2, + adapt = 100, + burnin = 100, + sample = 500, + silent.jags = TRUE, + modules = "glm" + )) + + marglik <- JAGS_bridgesampling(fit, prior_list = prior_list, data = list(), log_posterior = log_posterior) + expect_s3_class(marglik, "bridge") + expect_equal(marglik$logml, 0, tolerance = 0.1) }) diff --git a/tests/testthat/test-JAGS-model-averaging.R b/tests/testthat/test-JAGS-model-averaging.R deleted file mode 100644 index 65f34436..00000000 --- a/tests/testthat/test-JAGS-model-averaging.R +++ /dev/null @@ -1,498 +0,0 @@ -context("JAGS model-averaging functions") - -test_that("JAGS model-averaging functions work (simple)",{ - - set.seed(1) - data <- list( - x = rnorm(20, 0, 1), - N = 20 - ) - priors_list0 <- list( - m = prior("spike", list(0)), - s = prior("normal", list(0, 1), list(0, Inf)) - ) - priors_list1 <- list( - m = prior("normal", list(0, .3)), - s = prior("normal", list(0, 1), list(0, Inf)) - ) - model_syntax <- - "model - { - for(i in 1:N){ - x[i] ~ dnorm(m, pow(s, -2)) - } - }" - log_posterior <- function(parameters, data){ - sum(stats::dnorm(data$x, parameters[["m"]], parameters[["s"]], log = TRUE)) - } - # fit the models - fit0 <- JAGS_fit(model_syntax, data, priors_list0, chains = 1, adapt = 100, burnin = 150, sample = 500, seed = 0) - fit1 <- JAGS_fit(model_syntax, data, priors_list1, chains = 1, adapt = 100, burnin = 150, sample = 500, seed = 1) - # get marginal likelihoods - marglik0 <- JAGS_bridgesampling(fit0, log_posterior = log_posterior, data = data, prior_list = priors_list0) - marglik1 <- JAGS_bridgesampling(fit1, log_posterior = log_posterior, data = data, prior_list = priors_list1) - - # make parameter inference - inference_m <- compute_inference(c(1, 1), c(marglik0$logml, marglik1$logml), c(T, F)) - inference_m_conditional <- compute_inference(c(1, 1), c(marglik0$logml, marglik1$logml), c(T, F), conditional = T) - - # manually mix posteriors - mixed_posterior <- BayesTools:::.mix_posteriors.simple(list(fit0, fit1), list(priors_list0[["m"]], priors_list1[["m"]]), "m", inference_m$post_prob) - mixed_posterior_conditional <- BayesTools:::.mix_posteriors.simple(list(fit0, fit1), list(priors_list0[["m"]], priors_list1[["m"]]), "m", inference_m_conditional$post_prob) - - expect_equal(mean(mixed_posterior == 0), inference_m$post_probs[1], tolerance = 1e-4) - expect_equal(mean(attr(mixed_posterior, "models_ind") == 1), inference_m$post_probs[1], tolerance = 1e-4) - - vdiffr::expect_doppelganger("JAGS-model-averaging-1", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(1, 2)) - hist(mixed_posterior, main = "model-averaged") - hist(mixed_posterior_conditional, main = "conditional") - }) - - # automatically mix posteriors - models <- list( - list(fit = fit0, marglik = marglik0, prior_weights = 1), - list(fit = fit1, marglik = marglik1, prior_weights = 1) - ) - inference <- ensemble_inference(model_list = models, parameters = c("m", "s"), is_null_list = list("m" = 1, "s" = 0), conditional = FALSE) - inference_conditional <- ensemble_inference(model_list = models, parameters = c("m", "s"), is_null_list = list("m" = 1, "s" = 0), conditional = TRUE) - - mixed_posteriors <- mix_posteriors(model_list = models, parameters = c("m", "s"), is_null_list = list("m" = 1, "s" = 0), seed = 1) - mixed_posteriors_conditional <- mix_posteriors(model_list = models, parameters = c("m", "s"), is_null_list = list("m" = 1, "s" = 0), conditional = TRUE) - - inference_s <- compute_inference(c(1, 1), c(marglik0$logml, marglik1$logml), c(F, F)) - inference_s_conditional <- compute_inference(c(1, 1), c(marglik0$logml, marglik1$logml), c(F, F), conditional = T) - - expect_equal(inference$m[c("prior_probs", "post_probs", "BF")], inference_m[c("prior_probs", "post_probs", "BF")]) - expect_equal(inference_conditional$m[c("prior_probs", "post_probs", "BF")], inference_m_conditional[c("prior_probs", "post_probs", "BF")]) - expect_equal(inference$s[c("prior_probs", "post_probs", "BF")], inference_s[c("prior_probs", "post_probs", "BF")]) - expect_equal(inference_conditional$s[c("prior_probs", "post_probs", "BF")], inference_s_conditional[c("prior_probs", "post_probs", "BF")]) - expect_equal(mean(mixed_posteriors$m == 0), inference_m$post_probs[1], tolerance = 1e-4) - expect_equal(mean(attr(mixed_posteriors$m, "models_ind") == 1), inference_m$post_probs[1], tolerance = 1e-4) - expect_equal(mean(attr(mixed_posteriors$s, "models_ind") == 1), inference_m$post_probs[1], tolerance = 1e-4) - expect_true(all(attr(mixed_posteriors_conditional$m, "models_ind") == 2)) - expect_equal(mean(attr(mixed_posteriors_conditional$s, "models_ind") == 1), inference_m$post_probs[1], tolerance = 1e-4) - vdiffr::expect_doppelganger("JAGS-model-averaging-2", function(){ - par(mfrow = c(2, 2)) - hist(mixed_posteriors$m, main = "model-averaged (m)") - hist(mixed_posteriors_conditional$m, main = "conditional (m)") - hist(mixed_posteriors$s, main = "model-averaged (s)") - hist(mixed_posteriors_conditional$s, main = "conditional = conditional (s)") - }) - - # dealing with missing unspecified null priors - models2 <- list( - list(fit = fit0, marglik = marglik0, prior_weights = 1), - list(fit = fit1, marglik = marglik1, prior_weights = 1) - ) - mixed_posteriors2 <- mix_posteriors(model_list = models2, parameters = c("m", "s"), is_null_list = list("m" = 1, "s" = 0), seed = 1) - expect_equal(mixed_posteriors, mixed_posteriors2) -}) - -# skip the rest as it takes too long -skip_on_cran() - -test_that("JAGS model-averaging functions work (weightfunctions)",{ - - set.seed(1) - data <- list( - x = rnorm(20, 0, 1), - N = 20 - ) - priors_list0 <- list( - m =prior("normal", list(0, 1)), - omega = prior_none() - ) - priors_list1 <- list( - m = prior("normal", list(0, .5)), - omega = prior_weightfunction("one.sided", list(c(0.05), c(1, 1))) - ) - priors_list2 <- list( - m = prior("normal", list(0, .3)), - omega = prior_weightfunction("one.sided", list(c(0.05, 0.50), c(1, 1, 1))) - ) - model_syntax <- - "model - { - for(i in 1:N){ - x[i] ~ dnorm(m, 1) - } - }" - log_posterior <- function(parameters, data){ - return(0) - } - # fit the models - fit0 <- JAGS_fit(model_syntax, data, priors_list0, chains = 1, adapt = 100, burnin = 150, sample = 500, seed = 0) - fit1 <- JAGS_fit(model_syntax, data, priors_list1, chains = 1, adapt = 100, burnin = 150, sample = 500, seed = 1) - fit2 <- JAGS_fit(model_syntax, data, priors_list2, chains = 1, adapt = 100, burnin = 150, sample = 500, seed = 1) - # get marginal likelihoods - marglik0 <- JAGS_bridgesampling(fit0, log_posterior = log_posterior, data = data, prior_list = priors_list0) - marglik1 <- JAGS_bridgesampling(fit1, log_posterior = log_posterior, data = data, prior_list = priors_list1) - marglik2 <- JAGS_bridgesampling(fit2, log_posterior = log_posterior, data = data, prior_list = priors_list2) - - # check coefficient mapping - expect_equal(weightfunctions_mapping(list(priors_list0$omega, priors_list1$omega, priors_list2$omega)), list(NULL, c(2, 1, 1), c(3, 2, 1))) - expect_equal(weightfunctions_mapping(list( - prior_weightfunction("two.sided", list(c(0.05), c(1, 1))), - prior_weightfunction("one.sided", list(c(0.05, 0.50), c(1, 1, 1))) - )), list( - c(2, 1, 1, 1, 2), - c(3, 3, 2, 1, 1)) - ) - expect_equal(weightfunctions_mapping(list( - prior_weightfunction("two.sided", list(c(0.05), c(1, 1))), - prior_weightfunction("one.sided", list(c(0.05, 0.50, .975), c(1, 1, 1), c(1, 1))) - )), list( - c(2, 1, 1, 1, 2), - c(4, 4, 3, 2, 1)) - ) - - models <- list( - list(fit = fit0, marglik = marglik0, prior_weights = 1), - list(fit = fit1, marglik = marglik1, prior_weights = 1), - list(fit = fit2, marglik = marglik2, prior_weights = 1) - ) - - # get models inference & mix posteriors - models <- models_inference(models) - inference <- ensemble_inference(model_list = models, parameters = c("m", "omega"), is_null_list = list("m" = 0, "omega" = 1), conditional = FALSE) - inference_conditional <- ensemble_inference(model_list = models, parameters = c("m", "omega"), is_null_list = list("m" = 0, "omega" = 1), conditional = TRUE) - mixed_posteriors <- mix_posteriors(model_list = models, parameters = c("m", "omega"), is_null_list = list("m" = 0, "omega" = 1), seed = 1) - mixed_posteriors_conditional <-mix_posteriors(model_list = models, parameters = c("m", "omega"), is_null_list = list("m" = 0, "omega" = 1), seed = 1, conditional = TRUE) - - # checking posteriors and inferences - expect_equal(names(models[[1]]$inference), c("m_number", "marglik", "prior_prob", "post_prob", "inclusion_BF")) - expect_equal(unname(unlist(models[[1]]$inference)), c(1.0000000, -1.1023042, 0.3333333, 0.1998118, 0.4994120), tolerance = 1e-4) - expect_equal(mean(mixed_posteriors$omega[,-1] == 1), inference$omega$post_probs[1], tolerance = 1e-4) - expect_true(all(mixed_posteriors$omega[1,] == 1)) - expect_true(all(colnames(mixed_posteriors$omega[1,]) == c("omega[0,0.05]", "omega[0.05,0.5]", "omega[0.5,1]"))) - expect_equal(mean(attr(mixed_posteriors$omega, "models_ind") == 2), inference$omega$post_probs[2], tolerance = 1e-4) - expect_equal(mean(attr(mixed_posteriors$omega, "models_ind") == 3), inference$omega$post_probs[3], tolerance = 1e-4) - expect_equal(mean(attr(mixed_posteriors_conditional$omega, "models_ind") == 2), inference_conditional$omega$post_probs[2], tolerance = 1e-4) - expect_equal(mean(attr(mixed_posteriors_conditional$omega, "models_ind") == 3), inference_conditional$omega$post_probs[3], tolerance = 1e-4) - vdiffr::expect_doppelganger("JAGS-model-averaging-weightfunctions-1", function(){ - par(mfrow = c(2, 3)) - sapply(1:3, function(i)hist(mixed_posteriors$omega[,i], main = "model-averaged (omega)", xlab = colnames(mixed_posteriors$omega)[i])) - sapply(1:3, function(i)hist(mixed_posteriors_conditional$omega[,i], main = "conditional (omega)", xlab = colnames(mixed_posteriors$omega)[i])) - }) - - - ### checking fixed weightfunctions - priors_list3 <- list( - m = prior("normal", list(0, .3)), - omega = prior_weightfunction("two.sided.fixed", list(0.20, c(.3, 1))) - ) - fit3 <- JAGS_fit(model_syntax, data, priors_list3, chains = 1, adapt = 100, burnin = 150, sample = 500, seed = 1) - marglik3 <- JAGS_bridgesampling(fit3, log_posterior = log_posterior, data = data, prior_list = priors_list3) - models <- list( - list(fit = fit0, marglik = marglik0, prior_weights = 1), - list(fit = fit1, marglik = marglik1, prior_weights = 1), - list(fit = fit2, marglik = marglik2, prior_weights = 1), - list(fit = fit3, marglik = marglik3, prior_weights = 1) - ) - - inference <- ensemble_inference(model_list = models, parameters = c("m", "omega"), is_null_list = list("m" = 0, "omega" = 1), conditional = FALSE) - mixed_posteriors <- mix_posteriors(model_list = models, parameters = c("m", "omega"), is_null_list = list("m" = 0, "omega" = 1), seed = 1) - mixed_posteriors_conditional <- mix_posteriors(model_list = models, parameters = c("m", "omega"), is_null_list = list("m" = 0, "omega" = 1), seed = 1, conditional = TRUE) - - expect_equal(mean(mixed_posteriors$omega[,1] == .3), inference$omega$post_probs[4], tolerance = 1e-4) - expect_equal(mean(mixed_posteriors$omega[,3] == 1), inference$omega$post_probs[4] + inference$omega$post_probs[1], tolerance = 1e-4) - vdiffr::expect_doppelganger("JAGS-model-averaging-weightfunctions-2", function(){ - par(mfrow = c(2, 5)) - sapply(1:5, function(i)hist(mixed_posteriors$omega[,i], main = "model-averaged (omega)", xlab = colnames(mixed_posteriors$omega)[i])) - sapply(1:5, function(i)hist(mixed_posteriors_conditional$omega[,i], main = "conditional (omega)", xlab = colnames(mixed_posteriors$omega)[i])) - }) -}) - -test_that("JAGS model-averaging functions work (formula + factors)",{ - - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes - skip_on_cran() - - set.seed(1) - - data_formula <- data.frame( - x_cont1 = rnorm(60), - x_fac2t = factor(rep(c("A", "B"), 30), levels = c("A", "B")), - x_fac3o = factor(rep(c("A", "B", "C"), 20), levels = c("A", "B", "C")), - x_fac3t = factor(rep(c("A", "B", "C"), 20), levels = c("A", "B", "C")) - ) - data <- list( - y = rnorm(60, .4 * data_formula$x_cont1 + ifelse(data_formula$x_fac3t == "A", 0.0, ifelse(data_formula$x_fac3t == "B", -0.2, 0.4)), 1), - N = 60 - ) - - # create model with mix of a formula and free parameters --- - formula_list0 <- list(mu = ~ x_fac2t) - formula_list1 <- list(mu = ~ x_cont1 + x_fac3t) - formula_list2 <- list(mu = ~ x_fac3o) - formula_list3 <- list(mu = ~ x_cont1 * x_fac3o) - - formula_prior_list0 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_fac2t" = prior_factor("normal", contrast = "treatment", list(0, 1)) - ) - ) - formula_prior_list1 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior("normal", list(0, 1)), - "x_fac3t" = prior_factor("normal", contrast = "treatment", list(0, 1)) - ) - ) - formula_prior_list2 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_fac3o" = prior_factor("mnormal", contrast = "orthonormal", list(0, 1)) - ) - ) - formula_prior_list3 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior("normal", list(0, 1)), - "x_fac3o" = prior_factor("mnormal", contrast = "orthonormal", list(0, 1)), - "x_cont1:x_fac3o" = prior_factor("mnormal", contrast = "orthonormal", list(0, 1)) - ) - ) - - prior_list <- list(sigma = prior("lognormal", list(0, 1))) - formula_data_list <- list(mu = data_formula) - - model_syntax <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) - - log_posterior <- function(parameters, data){ - sum(stats::dnorm(data$y, parameters[["mu"]], parameters[["sigma"]], log = TRUE)) - } - - fit0 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list0, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0, seed = 1) - fit1 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list1, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1, seed = 2) - fit2 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list2, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list2, seed = 3) - fit3 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list3, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list3, seed = 4) - - marglik0 <- JAGS_bridgesampling( - fit0, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list0, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0) - marglik1 <- JAGS_bridgesampling( - fit1, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list1, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1) - marglik2 <- JAGS_bridgesampling( - fit2, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list2, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list2) - marglik3 <- JAGS_bridgesampling( - fit3, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list3, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list3) - - - # mix posteriors - models <- list( - list(fit = fit0, marglik = marglik0, prior_weights = 1), - list(fit = fit1, marglik = marglik1, prior_weights = 1), - list(fit = fit2, marglik = marglik2, prior_weights = 1), - list(fit = fit3, marglik = marglik3, prior_weights = 1) - ) - - - inference <- ensemble_inference( - model_list = models, - parameters = c("mu_x_cont1", "mu_x_fac2t", "mu_x_fac3t", "mu_x_fac3o", "mu_x_cont1__xXx__x_fac3o"), - is_null_list = list( - "mu_x_cont1" = c(TRUE, FALSE, TRUE, FALSE), - "mu_x_fac2t" = c(FALSE, TRUE, TRUE, TRUE), - "mu_x_fac2t" = c(TRUE, FALSE, TRUE, TRUE), - "mu_x_fac2t" = c(TRUE, TRUE, FALSE, FALSE), - "mu_x_cont1:x_fac3o" = c(TRUE, TRUE, TRUE, FALSE) - ), - conditional = FALSE) - - mixed_posteriors <- mix_posteriors( - model_list = models, - parameters = c("mu_x_cont1", "mu_x_fac2t", "mu_x_fac3t", "mu_x_fac3o", "mu_x_cont1__xXx__x_fac3o"), - is_null_list = list( - "mu_x_cont1" = c(TRUE, FALSE, TRUE, FALSE), - "mu_x_fac2t" = c(FALSE, TRUE, TRUE, TRUE), - "mu_x_fac2t" = c(TRUE, FALSE, TRUE, TRUE), - "mu_x_fac2t" = c(TRUE, TRUE, FALSE, FALSE), - "mu_x_cont1:x_fac3o" = c(TRUE, TRUE, TRUE, FALSE) - ), - seed = 1, n_samples = 10000) - - mixed_posteriors_c <- mix_posteriors( - model_list = models, - parameters = c("mu_x_cont1", "mu_x_fac2t", "mu_x_fac3t", "mu_x_fac3o", "mu_x_cont1__xXx__x_fac3o"), - is_null_list = list( - "mu_x_cont1" = c(TRUE, FALSE, TRUE, FALSE), - "mu_x_fac2t" = c(FALSE, TRUE, TRUE, TRUE), - "mu_x_fac2t" = c(TRUE, FALSE, TRUE, TRUE), - "mu_x_fac2t" = c(TRUE, TRUE, FALSE, FALSE), - "mu_x_cont1:x_fac3o" = c(TRUE, TRUE, TRUE, FALSE) - ), - seed = 1, n_samples = 10000, conditional = TRUE) - - - expect_true(is.numeric(inference$mu_x_cont1$BF)) - expect_true(is.numeric(inference$mu_x_fac2t$BF)) - expect_true(is.numeric(inference$mu_x_fac3t$BF)) - expect_true(is.numeric(inference$mu_x_fac3o$BF)) - expect_true(is.numeric(inference$mu_x_cont1__xXx__x_fac3o$BF)) - - expect_equal(length(mixed_posteriors$mu_x_cont1), 10000) - expect_equal(length(mixed_posteriors$mu_x_fac2t), 10000) - expect_equal(dim(mixed_posteriors$mu_x_fac3t), c(10000, 2)) - expect_equal(dim(mixed_posteriors$mu_x_fac3o), c(10000, 2)) - expect_equal(dim(mixed_posteriors$mu_x_cont1__xXx__x_fac3o), c(10000, 2)) - - vdiffr::expect_doppelganger("JAGS-model-averaging-3", function(){ - par(mfrow = c(2, 3)) - hist(mixed_posteriors$mu_x_fac2t, main = "averaged x_fac2t") - hist(mixed_posteriors_c$mu_x_fac2t, main = "conditiona x_fac2t") - hist(mixed_posteriors_c$mu_x_fac3t[,1], main = "conditional mu_x_fac3t[1]") - hist(mixed_posteriors_c$mu_x_fac3t[,2], main = "conditional mu_x_fac3t[2]") - hist(mixed_posteriors_c$mu_x_cont1__xXx__x_fac3o[,1], main = "conditional mu_x_cont1__xXx__x_fac3o[1]") - hist(mixed_posteriors_c$mu_x_cont1__xXx__x_fac3o[,2], main = "conditional mu_x_cont1__xXx__x_fac3o[2]") - }) - -}) - -test_that("JAGS model-averaging functions work (formula + spike factors)",{ - - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes - skip_on_cran() - - set.seed(1) - - data_formula <- data.frame( - x_fac3md = factor(rep(c("A", "B", "C"), 20), levels = c("A", "B", "C")) - ) - data <- list( - y = rnorm(60, ifelse(data_formula$x_fac3md == "A", 0.0, ifelse(data_formula$x_fac3md == "B", -0.2, 0.4)), 1), - N = 60 - ) - - # create model with mix of a formula and free parameters --- - formula_list0a <- list(mu = ~ 1) - formula_list0b <- list(mu = ~ x_fac3md) - formula_list1 <- list(mu = ~ x_fac3md) - - - formula_prior_list0a <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)) - ) - ) - formula_prior_list0b <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_fac3md" = prior_factor("spike", contrast = "meandif", list(0)) - ) - ) - formula_prior_list1 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_fac3md" = prior_factor("mnormal", contrast = "meandif", list(0, 0.25)) - ) - ) - - prior_list <- list(sigma = prior("lognormal", list(0, 1))) - formula_data_list <- list(mu = data_formula) - - model_syntax <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) - - log_posterior <- function(parameters, data){ - sum(stats::dnorm(data$y, parameters[["mu"]], parameters[["sigma"]], log = TRUE)) - } - - fit0a <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list0a, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0a, seed = 1) - fit0b <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list0b, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0b, seed = 2) - fit1 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list1, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1, seed = 3) - - - marglik0a <- JAGS_bridgesampling( - fit0a, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list0a, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0a) - marglik0b <- JAGS_bridgesampling( - fit0b, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list0b, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0b) - marglik1 <- JAGS_bridgesampling( - fit1, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list1, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1) - - - # mix posteriors - modelsA <- list( - list(fit = fit0a, marglik = marglik0a, prior_weights = 1), - list(fit = fit1, marglik = marglik1, prior_weights = 1) - ) - modelsB <- list( - list(fit = fit0b, marglik = marglik0b, prior_weights = 1), - list(fit = fit1, marglik = marglik1, prior_weights = 1) - ) - - - inferenceA <- ensemble_inference( - model_list = modelsA, - parameters = c("mu_x_fac3md"), - is_null_list = list( - "mu_x_fac3md" = c(TRUE, FALSE) - ), - conditional = FALSE) - inferenceB <- ensemble_inference( - model_list = modelsB, - parameters = c("mu_x_fac3md"), - is_null_list = list( - "mu_x_fac3md" = c(TRUE, FALSE) - ), - conditional = FALSE) - - mixed_posteriorsA <- mix_posteriors( - model_list = modelsA, - parameters = c("mu_x_fac3md"), - is_null_list = list( - "mu_x_fac3md" = c(TRUE, FALSE) - ), - seed = 1, n_samples = 10000) - mixed_posteriorsB <- mix_posteriors( - model_list = modelsB, - parameters = c("mu_x_fac3md"), - is_null_list = list( - "mu_x_fac3md" = c(TRUE, FALSE) - ), - seed = 1, n_samples = 10000) - - - expect_equivalent(inferenceA, inferenceB, tolerance = 1e-2) - - common_attributes <- names(attributes(mixed_posteriorsB$mu_x_fac3md)) - common_attributes <- common_attributes[!common_attributes %in% c("sample_ind", "models_ind", "prior_list")] - - expect_equal(attributes(mixed_posteriorsA$mu_x_fac3md)[common_attributes], attributes(mixed_posteriorsB$mu_x_fac3md)[common_attributes]) - -}) diff --git a/tests/testthat/test-JAGS-posterior-extraction.R b/tests/testthat/test-JAGS-posterior-extraction.R new file mode 100644 index 00000000..c7258399 --- /dev/null +++ b/tests/testthat/test-JAGS-posterior-extraction.R @@ -0,0 +1,501 @@ +# ============================================================================ # +# TEST FILE: Posterior Density Extraction Functions +# ============================================================================ # +# +# PURPOSE: +# Tests for posterior extraction helper functions including +# .extract_posterior_samples and .remove_auxiliary_parameters. +# +# DEPENDENCIES: +# - rjags, runjags, coda: For JAGS model handling +# +# SKIP CONDITIONS: +# - skip_if_not_installed("rjags"), skip_if_not_installed("runjags") +# - Note: Creates mock objects, does not need pre-fitted models +# +# MODELS/FIXTURES: +# - Creates mock runjags objects for testing +# +# TAGS: @evaluation, @JAGS, @posterior-extraction +# ============================================================================ # + +# Tests for posterior extraction helper functions +test_that(".extract_posterior_samples extracts samples correctly", { + + skip_if_not_installed("rjags") + skip_if_not_installed("runjags") + + # Load runjags to ensure S3 methods are registered + library(runjags) + + # Create a proper runjags object structure for testing + # The runjags package has an as.mcmc method that handles mcmc.list objects + set.seed(123) + mcmc1 <- coda::mcmc(matrix(rnorm(100), ncol = 1, dimnames = list(NULL, "mu")), + start = 1, end = 100, thin = 1) + mcmc2 <- coda::mcmc(matrix(rnorm(100), ncol = 1, dimnames = list(NULL, "mu")), + start = 1, end = 100, thin = 1) + mcmc_list <- coda::mcmc.list(mcmc1, mcmc2) + + # Create a minimal runjags object + fit <- structure( + list(mcmc = mcmc_list), + class = c("runjags", "list") + ) + + # Test matrix extraction (as_list = FALSE) + # This calls coda::as.mcmc on the runjags object which returns an mcmc object + samples_matrix <- BayesTools:::.extract_posterior_samples(fit, as_list = FALSE) + # mcmc objects inherit from matrix + expect_true(inherits(samples_matrix, "mcmc")) + expect_equal(ncol(samples_matrix), 1) + expect_true("mu" %in% colnames(samples_matrix)) + expect_equal(nrow(samples_matrix), 200) # 100 samples x 2 chains merged + + # Test list extraction (as_list = TRUE) + samples_list <- BayesTools:::.extract_posterior_samples(fit, as_list = TRUE) + expect_true(inherits(samples_list, "mcmc.list")) + expect_equal(length(samples_list), 2) # 2 chains +}) + + +test_that(".remove_auxiliary_parameters removes invgamma support", { + skip_on_cran() + skip_if_not_installed("rjags") + + # Create mock samples with invgamma support parameter + model_samples <- matrix(rnorm(100), ncol = 2) + colnames(model_samples) <- c("sigma", "inv_sigma") + + prior_list <- list( + sigma = prior("invgamma", list(1, 1)) + ) + + result <- BayesTools:::.remove_auxiliary_parameters(model_samples, prior_list, NULL) + + expect_false("inv_sigma" %in% colnames(result$model_samples)) + expect_true("sigma" %in% colnames(result$model_samples)) +}) + + +test_that(".process_spike_and_slab handles conditional samples", { + skip_on_cran() + skip_if_not_installed("rjags") + + # Create mock samples with spike and slab + model_samples <- matrix(c( + rnorm(50, 0, 1), # mu values + rep(1, 50) # indicator (all in slab) + ), ncol = 2) + colnames(model_samples) <- c("mu", "mu_indicator") + + prior_list <- list( + mu = prior_spike_and_slab( + prior("normal", list(0, 1)), + prior_inclusion = prior("spike", list(0.5)) + ) + ) + + result <- BayesTools:::.process_spike_and_slab( + model_samples, prior_list, "mu", + conditional = TRUE, remove_inclusion = FALSE, warnings = NULL + ) + + expect_true("mu (inclusion)" %in% colnames(result$model_samples)) + expect_false("mu_indicator" %in% colnames(result$model_samples)) + expect_true(is.prior.simple(result$prior_list$mu)) +}) + + +test_that(".apply_parameter_transformations applies transformations", { + skip_on_cran() + skip_if_not_installed("rjags") + + # Create mock samples + model_samples <- matrix(rnorm(100, 0, 1), ncol = 1) + colnames(model_samples) <- "mu" + + prior_list <- list( + mu = prior("normal", list(0, 1)) + ) + + # Apply exp transformation + transformations <- list( + mu = list(fun = exp, arg = list()) + ) + + result <- BayesTools:::.apply_parameter_transformations( + model_samples, transformations, prior_list + ) + + expect_true(all(result[, "mu"] > 0)) # exp makes all values positive + expect_equal(ncol(result), 1) +}) + + +test_that(".rename_factor_levels renames treatment factors", { + skip_on_cran() + skip_if_not_installed("rjags") + + # Create mock samples with factor + model_samples <- matrix(rnorm(300), ncol = 3) + colnames(model_samples) <- c("group[1]", "group[2]", "group[3]") + + # Create a factor prior with levels attribute (as would be set by JAGS_formula) + prior_obj <- prior_factor("normal", list(0, 1), contrast = "treatment") + attr(prior_obj, "levels") <- 4 # 4 levels total (treatment has K-1 parameters for K levels) + attr(prior_obj, "level_names") <- c("A", "B", "C", "D") # Should be a vector, not a list + + prior_list <- list(group = prior_obj) + + result <- BayesTools:::.rename_factor_levels(model_samples, prior_list) + + expect_true("group[B]" %in% colnames(result)) + expect_true("group[C]" %in% colnames(result)) + expect_true("group[D]" %in% colnames(result)) + expect_false("group[1]" %in% colnames(result)) +}) + + +test_that(".transform_factor_contrasts transforms orthonormal to differences", { + skip_on_cran() + skip_if_not_installed("rjags") + + # Create mock samples with orthonormal contrasts + model_samples <- matrix(rnorm(300), ncol = 3) + colnames(model_samples) <- c("group[1]", "group[2]", "group[3]") + + # Create a factor prior with levels attribute (as would be set by JAGS_formula) + prior_obj <- prior_factor("mnormal", list(0, 1), contrast = "orthonormal") + attr(prior_obj, "levels") <- 4 # 4 levels total (orthonormal has K-1 parameters for K levels) + attr(prior_obj, "level_names") <- c("A", "B", "C", "D") # Should be a vector, not a list + + prior_list <- list(group = prior_obj) + + expect_message( + result <- BayesTools:::.transform_factor_contrasts( + model_samples, prior_list, transform_factors = TRUE + ), + "transformation was applied" + ) + + # Should have 4 columns after transformation (one per level) + expect_equal(ncol(result), 4) + expect_true(any(grepl("dif:", colnames(result)))) +}) + + +test_that(".filter_parameters removes spike at 0 priors", { + skip_on_cran() + skip_if_not_installed("rjags") + + prior_list <- list( + mu = prior("normal", list(0, 1)), + delta = prior("point", list(0)), # spike at 0 + tau = prior("normal", list(1, 1)) + ) + + # With remove_spike_0 = TRUE + result <- BayesTools:::.filter_parameters(prior_list, remove_spike_0 = TRUE) + expect_true("delta" %in% result) + expect_false("mu" %in% result) + expect_false("tau" %in% result) + + # With remove_spike_0 = FALSE + result <- BayesTools:::.filter_parameters(prior_list, remove_spike_0 = FALSE) + expect_equal(length(result), 0) +}) + + +test_that(".filter_parameters removes character specified parameters", { + skip_on_cran() + skip_if_not_installed("rjags") + + prior_list <- list( + mu = prior("normal", list(0, 1)), + delta = prior("normal", list(0, 1)), + tau = prior("normal", list(1, 1)) + ) + + result <- BayesTools:::.filter_parameters(prior_list, remove_parameters = c("mu", "tau"), remove_spike_0 = FALSE) + expect_true("mu" %in% result) + expect_true("tau" %in% result) + expect_false("delta" %in% result) +}) + + +test_that(".filter_parameters removes non-formula parameters when TRUE", { + skip_on_cran() + skip_if_not_installed("rjags") + + # Create priors with formula attributes + prior_formula <- prior("normal", list(0, 1)) + attr(prior_formula, "parameter") <- "y" + + prior_list <- list( + intercept = prior_formula, + sigma = prior("normal", list(1, 1)) # no formula attribute + ) + + result <- BayesTools:::.filter_parameters(prior_list, remove_parameters = TRUE, remove_spike_0 = FALSE) + expect_true("sigma" %in% result) + expect_false("intercept" %in% result) +}) + + +test_that(".filter_parameters removes formula-specific parameters", { + skip_on_cran() + skip_if_not_installed("rjags") + + # Create priors with different formula attributes + prior_y <- prior("normal", list(0, 1)) + attr(prior_y, "parameter") <- "y" + + prior_x <- prior("normal", list(0, 1)) + attr(prior_x, "parameter") <- "x" + + prior_list <- list( + intercept_y = prior_y, + slope_y = prior_y, + intercept_x = prior_x, + sigma = prior("normal", list(1, 1)) # no formula attribute + ) + + result <- BayesTools:::.filter_parameters(prior_list, remove_formulas = "y", remove_spike_0 = FALSE) + expect_true("intercept_y" %in% result) + expect_true("slope_y" %in% result) + expect_false("intercept_x" %in% result) + expect_false("sigma" %in% result) +}) + + +test_that(".filter_parameters keeps only specified parameters", { + skip_on_cran() + skip_if_not_installed("rjags") + + prior_list <- list( + mu = prior("normal", list(0, 1)), + delta = prior("normal", list(0, 1)), + tau = prior("normal", list(1, 1)) + ) + + result <- BayesTools:::.filter_parameters(prior_list, keep_parameters = "mu", remove_spike_0 = FALSE) + expect_false("mu" %in% result) + expect_true("delta" %in% result) + expect_true("tau" %in% result) +}) + + +test_that(".filter_parameters keeps only specified formulas", { + skip_on_cran() + skip_if_not_installed("rjags") + + # Create priors with different formula attributes + prior_y <- prior("normal", list(0, 1)) + attr(prior_y, "parameter") <- "y" + + prior_x <- prior("normal", list(0, 1)) + attr(prior_x, "parameter") <- "x" + + prior_list <- list( + intercept_y = prior_y, + slope_y = prior_y, + intercept_x = prior_x, + sigma = prior("normal", list(1, 1)) # no formula attribute + ) + + result <- BayesTools:::.filter_parameters(prior_list, keep_formulas = "y", remove_spike_0 = FALSE) + expect_false("intercept_y" %in% result) + expect_false("slope_y" %in% result) + expect_true("intercept_x" %in% result) + expect_true("sigma" %in% result) +}) + + +test_that(".filter_parameters combines keep_parameters and keep_formulas", { + skip_on_cran() + skip_if_not_installed("rjags") + + # Create priors with different formula attributes + prior_y <- prior("normal", list(0, 1)) + attr(prior_y, "parameter") <- "y" + + prior_x <- prior("normal", list(0, 1)) + attr(prior_x, "parameter") <- "x" + + prior_list <- list( + intercept_y = prior_y, + slope_y = prior_y, + intercept_x = prior_x, + sigma = prior("normal", list(1, 1)) # no formula attribute + ) + + # Keep formula "y" and parameter "sigma" + result <- BayesTools:::.filter_parameters(prior_list, keep_parameters = "sigma", keep_formulas = "y", remove_spike_0 = FALSE) + expect_false("intercept_y" %in% result) + expect_false("slope_y" %in% result) + expect_false("sigma" %in% result) + expect_true("intercept_x" %in% result) +}) + + +test_that(".filter_parameters removes bias-related parameters when bias is removed", { + skip_on_cran() + skip_if_not_installed("rjags") + + # Create a mixture prior with PET component (simulating bias) + bias_prior <- prior_mixture(list( + prior_none(prior_weights = 1), + prior_PET("normal", list(0, 1), prior_weights = 1) + )) + + prior_list <- list( + mu = prior("normal", list(0, 1)), + bias = bias_prior + ) + + # When bias is removed, PET should also be removed + result <- BayesTools:::.filter_parameters(prior_list, remove_parameters = "bias", remove_spike_0 = FALSE) + expect_true("bias" %in% result) + expect_true("PET" %in% result) + expect_false("mu" %in% result) +}) + + +test_that(".filter_parameters removes bias-related parameters when bias contains PEESE", { + skip_on_cran() + skip_if_not_installed("rjags") + + # Create a mixture prior with PEESE component + bias_prior <- prior_mixture(list( + prior_none(prior_weights = 1), + prior_PEESE("normal", list(0, 1), prior_weights = 1) + )) + + prior_list <- list( + mu = prior("normal", list(0, 1)), + bias = bias_prior + ) + + result <- BayesTools:::.filter_parameters(prior_list, remove_parameters = "bias", remove_spike_0 = FALSE) + expect_true("bias" %in% result) + expect_true("PEESE" %in% result) + expect_false("mu" %in% result) +}) + + +test_that(".filter_parameters removes bias-related parameters when bias contains weightfunction", { + skip_on_cran() + skip_if_not_installed("rjags") + + # Create a mixture prior with weightfunction component + bias_prior <- prior_mixture(list( + prior_none(prior_weights = 1), + prior_weightfunction("one.sided", list(c(0.05), c(1, 1)), prior_weights = 1) + )) + + prior_list <- list( + mu = prior("normal", list(0, 1)), + bias = bias_prior + ) + + result <- BayesTools:::.filter_parameters(prior_list, remove_parameters = "bias", remove_spike_0 = FALSE) + expect_true("bias" %in% result) + expect_true("omega" %in% result) + expect_false("mu" %in% result) +}) + + +test_that(".filter_parameters keeps bias-related parameters when bias is kept", { + skip_on_cran() + skip_if_not_installed("rjags") + + # Create a mixture prior with PET component + bias_prior <- prior_mixture(list( + prior_none(prior_weights = 1), + prior_PET("normal", list(0, 1), prior_weights = 1) + )) + + prior_list <- list( + mu = prior("normal", list(0, 1)), + tau = prior("normal", list(1, 1)), + bias = bias_prior + ) + + # When only bias is kept, mu and tau should be removed, but PET should be kept + result <- BayesTools:::.filter_parameters(prior_list, keep_parameters = "bias", remove_spike_0 = FALSE) + expect_false("bias" %in% result) + expect_false("PET" %in% result) + expect_true("mu" %in% result) + expect_true("tau" %in% result) +}) + + +test_that(".filter_parameters handles non-mixture bias priors", { + skip_on_cran() + skip_if_not_installed("rjags") + + # Create a single PET prior named bias (not a mixture) + bias_prior <- prior_PET("normal", list(0, 1)) + + prior_list <- list( + mu = prior("normal", list(0, 1)), + bias = bias_prior + ) + + result <- BayesTools:::.filter_parameters(prior_list, remove_parameters = "bias", remove_spike_0 = FALSE) + expect_true("bias" %in% result) + expect_true("PET" %in% result) + expect_false("mu" %in% result) +}) + + +test_that(".filter_parameters removes bias-related parameters when bias is not in keep list", { + skip_on_cran() + skip_if_not_installed("rjags") + + # Create a mixture prior with PET component + bias_prior <- prior_mixture(list( + prior_none(prior_weights = 1), + prior_PET("normal", list(0, 1), prior_weights = 1) + )) + + prior_list <- list( + mu = prior("normal", list(0, 1)), + tau = prior("normal", list(1, 1)), + bias = bias_prior + ) + + # When only mu is kept, bias should be removed along with PET + result <- BayesTools:::.filter_parameters(prior_list, keep_parameters = "mu", remove_spike_0 = FALSE) + expect_false("mu" %in% result) + expect_true("bias" %in% result) + expect_true("PET" %in% result) + expect_true("tau" %in% result) +}) + + +test_that("helper functions work with runjags estimates extraction", { + skip_on_cran() + skip_if_not_installed("rjags") + + # Test the helper functions with mock data (not full integration) + # This tests that our refactored code correctly uses the helpers + + # Create mock posterior samples + set.seed(123) + model_samples <- matrix(rnorm(200), ncol = 2, dimnames = list(NULL, c("mu", "inv_sigma"))) + + prior_list <- list( + mu = prior("normal", list(0, 1)), + sigma = prior("invgamma", list(1, 1)) + ) + + # Test that remove_auxiliary_parameters helper works + cleaned <- BayesTools:::.remove_auxiliary_parameters(model_samples, prior_list, NULL) + + # Should remove inv_sigma + expect_false("inv_sigma" %in% colnames(cleaned$model_samples)) + expect_true("mu" %in% colnames(cleaned$model_samples)) + expect_equal(ncol(cleaned$model_samples), 1) +}) diff --git a/tests/testthat/test-JAGS-summary-tables.R b/tests/testthat/test-JAGS-summary-tables.R new file mode 100644 index 00000000..1b999afd --- /dev/null +++ b/tests/testthat/test-JAGS-summary-tables.R @@ -0,0 +1,358 @@ +# ============================================================================ # +# TEST FILE: JAGS Summary Tables Functions +# ============================================================================ # +# +# PURPOSE: +# Tests for runjags_estimates_table, runjags_inference_table, and related +# summary table generation functions. +# +# DEPENDENCIES: +# - rjags, bridgesampling: For tests using pre-fitted models +# - common-functions.R: temp_fits_dir, skip_if_no_fits, test_reference_table +# +# SKIP CONDITIONS: +# - First section (empty tables): Can run on CRAN (pure R) +# - Second section (advanced features): skip_if_no_fits() +# +# MODELS/FIXTURES: +# - fit_formula_interaction_cont, fit_factor_treatment, fit_spike_slab_factor +# - fit_factor_orthonormal +# +# TAGS: @evaluation, @JAGS, @summary-tables +# ============================================================================ # + +REFERENCE_DIR <<- testthat::test_path("..", "results", "JAGS-summary-tables") +source(testthat::test_path("common-functions.R")) + +# ============================================================================ # +# SECTION 1: Test Empty Tables +# ============================================================================ # +test_that("Empty summary tables work correctly", { + + runjags_summary_empty <- runjags_estimates_empty_table() + + expect_equal(nrow(runjags_summary_empty), 0, ignore_attr = TRUE) + + # Test that empty tables have correct structure + expect_s3_class(runjags_summary_empty, "BayesTools_table") + + test_reference_table(runjags_summary_empty, "empty_runjags_estimates.txt", "Empty runjags_estimates table mismatch") +}) + +# ============================================================================ # +# SECTION 2: Test Advanced Features (Transformations, Formula Handling, etc.) +# ============================================================================ # +test_that("Summary table advanced features work correctly", { + + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + skip_if_no_fits() + + # Use fit_formula_interaction_cont for testing advanced features + # This model has continuous interactions and formulas + fit_complex <- readRDS(file.path(temp_fits_dir, "fit_formula_interaction_cont.RDS")) + + # Test 1: Parameter transformations + runjags_summary_transform <- runjags_estimates_table( + fit_complex, + transformations = list("mu_intercept" = list(fun = exp)) + ) + + # Test 2: Formula handling with prefix + runjags_summary_prefix_true <- runjags_estimates_table(fit_complex, formula_prefix = TRUE) + runjags_summary_prefix_false <- runjags_estimates_table(fit_complex, formula_prefix = FALSE) + + # Test 3: Conditional vs unconditional + runjags_summary_conditional <- runjags_estimates_table(fit_complex, conditional = TRUE) + runjags_summary_unconditional <- runjags_estimates_table(fit_complex, conditional = FALSE) + + # Test 4: Factor transformations (use fit_factor_treatment for this) + fit_factor <- readRDS(file.path(temp_fits_dir, "fit_factor_treatment.RDS")) + + runjags_summary_factor <- runjags_estimates_table(fit_factor) + + # Test 5: Use fit with spike and slab + fit_spike <- readRDS(file.path(temp_fits_dir, "fit_spike_slab_factor.RDS")) + + runjags_summary_spike <- runjags_estimates_table(fit_spike) + runjags_inference_spike <- runjags_inference_table(fit_spike) + + # Test 6: Orthonormal contrast transformations to differences from the mean + fit_orthonormal <- readRDS(file.path(temp_fits_dir, "fit_factor_orthonormal.RDS")) + + runjags_summary_orthonormal <- suppressMessages(runjags_estimates_table(fit_orthonormal, transform_factors = TRUE)) + runjags_summary_orthonormal2 <- suppressMessages(runjags_estimates_table(fit_orthonormal, transform_factors = TRUE, + transformations = list("p1" = list(fun = exp)))) + + # Test 7: Custom transformations with transform_factors = FALSE + # Use a model with factor parameters for transformation testing + runjags_summary_custom_transform <- suppressMessages(runjags_estimates_table( + fit_factor, + transform_factors = FALSE, + transformations = list("mu_x_fac3t[2]" = list(fun = exp)) + )) + + # Test 8: Conditional with remove_inclusion + runjags_summary_remove_inclusion <- suppressMessages(runjags_estimates_table( + fit_spike, + transform_factors = TRUE, + conditional = TRUE, + remove_inclusion = TRUE + )) + + # Test 9: Conditional estimates with mixture priors + fit_complex_bias <- readRDS(file.path(temp_fits_dir, "fit_complex_bias.RDS")) + fit_complex_mixed <- readRDS(file.path(temp_fits_dir, "fit_complex_mixed.RDS")) + runjags_summary_complex2 <- runjags_estimates_table(fit_complex_bias, conditional = TRUE) + runjags_summary_complex3 <- runjags_estimates_table(fit_complex_mixed, conditional = TRUE) + + # Test basic properties + expect_s3_class(runjags_summary_transform, "BayesTools_table") + expect_s3_class(runjags_summary_prefix_true, "BayesTools_table") + expect_s3_class(runjags_summary_prefix_false, "BayesTools_table") + expect_s3_class(runjags_summary_conditional, "BayesTools_table") + expect_s3_class(runjags_summary_unconditional, "BayesTools_table") + expect_s3_class(runjags_summary_factor, "BayesTools_table") + expect_s3_class(runjags_summary_spike, "BayesTools_table") + expect_s3_class(runjags_inference_spike, "BayesTools_table") + expect_s3_class(runjags_summary_orthonormal, "BayesTools_table") + expect_s3_class(runjags_summary_orthonormal2, "BayesTools_table") + expect_s3_class(runjags_summary_custom_transform, "BayesTools_table") + expect_s3_class(runjags_summary_remove_inclusion, "BayesTools_table") + expect_s3_class(runjags_summary_complex2, "BayesTools_table") + expect_s3_class(runjags_summary_complex3, "BayesTools_table") + + # Test that row names differ with different formula_prefix settings + expect_false(identical(rownames(runjags_summary_prefix_true), + rownames(runjags_summary_prefix_false))) + + # Test that remove_inclusion reduces the number of rows + expect_true(nrow(runjags_summary_remove_inclusion) <= nrow(runjags_summary_spike)) + + test_reference_table(runjags_summary_transform, "advanced_transform.txt", "Transform table mismatch") + test_reference_table(runjags_summary_prefix_true, "advanced_formula_prefix_true.txt", "Formula prefix true table mismatch") + test_reference_table(runjags_summary_prefix_false, "advanced_formula_prefix_false.txt", "Formula prefix false table mismatch") + test_reference_table(runjags_summary_conditional, "advanced_conditional.txt", "Conditional table mismatch") + test_reference_table(runjags_summary_unconditional, "advanced_unconditional.txt", "Unconditional table mismatch") + test_reference_table(runjags_summary_factor, "advanced_factor_treatment.txt", "Factor treatment table mismatch") + test_reference_table(runjags_summary_spike, "advanced_spike_slab_estimates.txt", "Spike slab estimates table mismatch") + test_reference_table(runjags_inference_spike, "advanced_spike_slab_inference.txt", "Spike slab inference table mismatch") + test_reference_table(runjags_summary_orthonormal, "advanced_orthonormal_transform.txt", "Orthonormal transform table mismatch") + test_reference_table(runjags_summary_orthonormal2, "advanced_orthonormal_transform2.txt", "Orthonormal transform2 table mismatch") + test_reference_table(runjags_summary_custom_transform, "advanced_custom_transform.txt", "Custom transform table mismatch") + test_reference_table(runjags_summary_remove_inclusion, "advanced_remove_inclusion.txt", "Remove inclusion table mismatch") + test_reference_table(runjags_summary_complex2, "runjags_summary_complex2.txt", "Custom probs table mismatch") + test_reference_table(runjags_summary_complex3, "runjags_summary_complex3.txt", "Custom probs table mismatch") + + # Removal of formula and parameter names + fit_dual_param <- readRDS(file.path(temp_fits_dir, "fit_dual_param_regression.RDS")) + + runjags_summary_removal_01 <- JAGS_estimates_table(fit_dual_param) + runjags_summary_removal_02 <- JAGS_estimates_table(fit_dual_param, remove_formulas = "mu") + runjags_summary_removal_03 <- JAGS_estimates_table(fit_dual_param, keep_formulas = "log_sigma") + runjags_summary_removal_04 <- JAGS_estimates_table(fit_complex_mixed) + runjags_summary_removal_05 <- JAGS_estimates_table(fit_complex_mixed, remove_parameters = TRUE) + runjags_summary_removal_06 <- JAGS_estimates_table(fit_complex_mixed, remove_parameters = TRUE, remove_inclusion = TRUE) + runjags_summary_removal_07 <- JAGS_estimates_table(fit_complex_mixed, remove_parameters = "bias") + runjags_summary_removal_08 <- JAGS_estimates_table(fit_complex_mixed, remove_parameters = "sigma") + runjags_summary_removal_09 <- JAGS_estimates_table(fit_complex_mixed, remove_formulas = "mu") + + test_reference_table(runjags_summary_removal_01, "summary_parameter_or_formula_removal01.txt", "Parameter/formula removal") + test_reference_table(runjags_summary_removal_02, "summary_parameter_or_formula_removal02.txt", "Parameter/formula removal") + test_reference_table(runjags_summary_removal_03, "summary_parameter_or_formula_removal03.txt", "Parameter/formula removal") + test_reference_table(runjags_summary_removal_04, "summary_parameter_or_formula_removal04.txt", "Parameter/formula removal") + test_reference_table(runjags_summary_removal_05, "summary_parameter_or_formula_removal05.txt", "Parameter/formula removal") + test_reference_table(runjags_summary_removal_06, "summary_parameter_or_formula_removal06.txt", "Parameter/formula removal") + test_reference_table(runjags_summary_removal_07, "summary_parameter_or_formula_removal07.txt", "Parameter/formula removal") + test_reference_table(runjags_summary_removal_08, "summary_parameter_or_formula_removal08.txt", "Parameter/formula removal") + test_reference_table(runjags_summary_removal_09, "summary_parameter_or_formula_removal09.txt", "Parameter/formula removal") + + # Custom probs + runjags_summary_probs_01 <- JAGS_estimates_table(fit_dual_param) + runjags_summary_probs_02 <- JAGS_estimates_table(fit_dual_param, probs = c(0.5)) + runjags_summary_probs_03 <- JAGS_estimates_table(fit_dual_param, probs = c(0.25, 0.20, 0.99)) + + test_reference_table(runjags_summary_probs_01, "summary_parameter_probs1.txt", "Parameter/formula removal") + test_reference_table(runjags_summary_probs_02, "summary_parameter_probs2.txt", "Parameter/formula removal") + test_reference_table(runjags_summary_probs_03, "summary_parameter_probs3.txt", "Parameter/formula removal") + + # Remove diagnostics + runjags_remove_diagnostics <- JAGS_estimates_table(fit_dual_param, remove_diagnostics = TRUE) + test_reference_table(runjags_remove_diagnostics, "runjags_remove_diagnostics.txt", "Diagnostics removal") +}) + +# ============================================================================ # +# SECTION 3: Test Summary Tables for All Saved Models +# ============================================================================ # +test_that("Summary tables for all saved models", { + + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + skip_if_no_fits() + + runjags::runjags.options(silent.jags = TRUE, silent.runjags = TRUE) + + # Load model registry to get list of all fitted models + registry_file <- file.path(test_files_dir, "model_registry.RDS") + + model_registry <- readRDS(registry_file) + model_names <- model_registry$model_name + + print_dir <- testthat::test_path("..", "results", "print") + + for (model_name in model_names) { + fit_file <- file.path(temp_fits_dir, paste0(model_name, ".RDS")) + marglik_file <- file.path(temp_marglik_dir, paste0(model_name, ".RDS")) + + fit <- readRDS(fit_file) + has_marglik <- file.exists(marglik_file) + + if (has_marglik) { + marglik <- readRDS(marglik_file) + } + + # Process model summary table + if (has_marglik) { + model_list <- list( + list(fit = fit, marglik = marglik, prior_weights = 1, + fit_summary = runjags_estimates_table(fit)) + ) + model_list <- models_inference(model_list) + model_summary <- model_summary_table(model_list[[1]]) + test_reference_table(model_summary, paste0(model_name, "_model_summary.txt"), + paste0("Model summary mismatch for ", model_name)) + } + + # Process runjags estimates table + runjags_summary <- runjags_estimates_table(fit) + test_reference_table(runjags_summary, paste0(model_name, "_runjags_estimates.txt"), + paste0("Runjags estimates mismatch for ", model_name)) + + } +}) + + +# ============================================================================ # +# SECTION 4: Test runjags_estimates_table with conditional=TRUE on various priors +# ============================================================================ # +test_that("runjags_estimates_table with conditional=TRUE on various prior types", { + + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + skip_on_cran() + skip_if_no_fits() + + # Test with publication bias priors + fit_pub_bias <- readRDS(file.path(temp_fits_dir, "fit_simple_pub_bias.RDS")) + runjags_pub_bias_conditional <- runjags_estimates_table(fit_pub_bias, conditional = TRUE) + test_reference_table(runjags_pub_bias_conditional, "runjags_pub_bias_conditional.txt") + + # Test with factor priors + fit_factor <- readRDS(file.path(temp_fits_dir, "fit_factor_orthonormal.RDS")) + runjags_factor_conditional <- runjags_estimates_table(fit_factor, conditional = TRUE) + test_reference_table(runjags_factor_conditional, "runjags_factor_conditional.txt") + + runjags_factor_conditional_transformed <- runjags_estimates_table(fit_factor, conditional = TRUE, transform_factors = TRUE) + test_reference_table(runjags_factor_conditional_transformed, "runjags_factor_conditional_transformed.txt") + + # Test with mixture priors + fit_mixture <- readRDS(file.path(temp_fits_dir, "fit_mixture_simple.RDS")) + runjags_mixture_conditional <- runjags_estimates_table(fit_mixture, conditional = TRUE) + test_reference_table(runjags_mixture_conditional, "runjags_mixture_conditional.txt") + + # Test with spike and slab priors + fit_spike_slab <- readRDS(file.path(temp_fits_dir, "fit_spike_slab_simple.RDS")) + runjags_spike_slab_conditional <- runjags_estimates_table(fit_spike_slab, conditional = TRUE) + test_reference_table(runjags_spike_slab_conditional, "runjags_spike_slab_conditional.txt") + +}) + + +# ============================================================================ # +# SECTION 5: Test runjags_inference_table with mixture and formula priors +# ============================================================================ # +test_that("runjags_inference_table with mixture priors", { + + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + skip_on_cran() + skip_if_no_fits() + + # Test with mixture priors + fit_mixture <- readRDS(file.path(temp_fits_dir, "fit_mixture_simple.RDS")) + runjags_mixture_inference <- runjags_inference_table(fit_mixture) + test_reference_table(runjags_mixture_inference, "runjags_mixture_inference.txt") + + # Test with mixture containing spike + fit_mixture_spike <- readRDS(file.path(temp_fits_dir, "fit_mixture_spike.RDS")) + runjags_mixture_spike_inference <- runjags_inference_table(fit_mixture_spike) + test_reference_table(runjags_mixture_spike_inference, "runjags_mixture_spike_inference.txt") + +}) + + +test_that("runjags_inference_table with formula priors", { + + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + skip_on_cran() + skip_if_no_fits() + + # Test with formula + mixture priors (mixture on factor predictor) + fit_formula_mixture <- readRDS(file.path(temp_fits_dir, "fit_formula_factor_mixture.RDS")) + runjags_formula_mixture_inference <- runjags_inference_table(fit_formula_mixture) + test_reference_table(runjags_formula_mixture_inference, "runjags_formula_mixture_inference.txt") + + # Test with joint complex model (formula + mixture + spike-and-slab) + fit_joint <- readRDS(file.path(temp_fits_dir, "fit_joint_complex.RDS")) + runjags_joint_inference <- runjags_inference_table(fit_joint) + test_reference_table(runjags_joint_inference, "runjags_joint_complex_inference.txt") + +}) + + +# ============================================================================ # +# SECTION 6: Test empty tables +# ============================================================================ # +test_that("model_summary_empty_table works correctly", { + + empty_table <- model_summary_empty_table() + expect_s3_class(empty_table, "BayesTools_table") + expect_true(nrow(empty_table) > 0) + expect_true(ncol(empty_table) > 0) + test_reference_table(empty_table, "model_summary_empty.txt") + +}) + + +test_that("runjags_inference_empty_table works correctly", { + + empty_table <- runjags_inference_empty_table() + expect_s3_class(empty_table, "BayesTools_table") + expect_equal(nrow(empty_table), 0) + expect_true(ncol(empty_table) > 0) + test_reference_table(empty_table, "runjags_inference_empty.txt") + +}) + + +# ============================================================================ # +# SECTION 7: Test stan_estimates_table with stored RDS file +# ============================================================================ # +test_that("stan_estimates_table works with stored fit", { + + skip_if_not_installed("rstan") + + # Load stored stan fit from tests/results/fits + stan_fit_file <- testthat::test_path("..", "results", "fits", "fit_RoBTT.RDS") + + fit_stan <- readRDS(stan_fit_file) + + # Test basic stan_estimates_table + stan_summary <- stan_estimates_table(fit_stan) + test_reference_table(stan_summary, "stan_estimates_basic.txt") + + stan_summary2 <- stan_estimates_table(fit_stan, transformations = list("mu" = list(fun = exp))) + test_reference_table(stan_summary2, "stan_estimates_basic2.txt") + +}) diff --git a/tests/testthat/test-distributions-mpoint.R b/tests/testthat/test-distributions-mpoint.R index aa85096f..994adb0c 100644 --- a/tests/testthat/test-distributions-mpoint.R +++ b/tests/testthat/test-distributions-mpoint.R @@ -1,4 +1,19 @@ -context("Distributions - Multivariate point") +# ============================================================================ # +# TEST FILE: Distributions - Multivariate Point +# ============================================================================ # +# +# PURPOSE: +# Tests for multivariate point distribution functions (dmpoint, rmpoint, +# pmpoint, qmpoint). +# +# DEPENDENCIES: +# - None (pure R) +# +# SKIP CONDITIONS: +# - None (can run on CRAN) +# +# TAGS: @evaluation, @distributions, @multivariate +# ============================================================================ # test_that("Density function works", { diff --git a/tests/testthat/test-distributions-point.R b/tests/testthat/test-distributions-point.R index b2dcf645..97fa9a55 100644 --- a/tests/testthat/test-distributions-point.R +++ b/tests/testthat/test-distributions-point.R @@ -1,4 +1,18 @@ -context("Distributions - Point") +# ============================================================================ # +# TEST FILE: Distributions - Point +# ============================================================================ # +# +# PURPOSE: +# Tests for point distribution functions (dpoint, rpoint, ppoint, qpoint). +# +# DEPENDENCIES: +# - None (pure R) +# +# SKIP CONDITIONS: +# - None (can run on CRAN) +# +# TAGS: @evaluation, @distributions, @point +# ============================================================================ # test_that("Density function works", { diff --git a/tests/testthat/test-distributions-tools.R b/tests/testthat/test-distributions-tools.R new file mode 100644 index 00000000..90def75d --- /dev/null +++ b/tests/testthat/test-distributions-tools.R @@ -0,0 +1,109 @@ +# ============================================================================ # +# TEST FILE: Distribution Tools Helpers +# ============================================================================ # +# +# PURPOSE: +# Tests for internal distribution helper functions like .check_log, +# .check_log.p, and .check_lower.tail. +# +# DEPENDENCIES: +# - None (pure R) +# +# SKIP CONDITIONS: +# - None (can run on CRAN) +# +# TAGS: @evaluation, @distributions, @tools +# ============================================================================ # + + +test_that(".check_log works", { + + expect_null(BayesTools:::.check_log(TRUE)) + expect_null(BayesTools:::.check_log(FALSE)) + + expect_error(BayesTools:::.check_log("TRUE"), "must be a logical") + expect_error(BayesTools:::.check_log(1), "must be a logical") + expect_error(BayesTools:::.check_log(NULL), "cannot be NULL") + +}) + + +test_that(".check_log.p works", { + + expect_null(BayesTools:::.check_log.p(TRUE)) + expect_null(BayesTools:::.check_log.p(FALSE)) + + expect_error(BayesTools:::.check_log.p("TRUE"), "must be a logical") + expect_error(BayesTools:::.check_log.p(1), "must be a logical") + +}) + + +test_that(".check_lower.tail works", { + + expect_null(BayesTools:::.check_lower.tail(TRUE)) + expect_null(BayesTools:::.check_lower.tail(FALSE)) + + expect_error(BayesTools:::.check_lower.tail("TRUE"), "must be a logical") + expect_error(BayesTools:::.check_lower.tail(1), "must be a logical") + +}) + + +test_that(".check_x works", { + + expect_null(BayesTools:::.check_x(0.5)) + expect_null(BayesTools:::.check_x(c(0, 0.5, 1))) + expect_null(BayesTools:::.check_x(0.5, lower = 0, upper = 1)) + + expect_error(BayesTools:::.check_x(-1, lower = 0), "must be equal or higher than 0") + expect_error(BayesTools:::.check_x(2, upper = 1), "must be equal or lower than 1") + expect_error(BayesTools:::.check_x("a"), "must be a numeric") + +}) + + +test_that(".check_n works", { + + expect_null(BayesTools:::.check_n(1)) + expect_null(BayesTools:::.check_n(100)) + + expect_error(BayesTools:::.check_n(0), "must be equal or higher than 1") + expect_error(BayesTools:::.check_n(-1), "must be equal or higher than 1") + expect_error(BayesTools:::.check_n(c(1, 2)), "must have length '1'") + expect_error(BayesTools:::.check_n("a"), "must be a numeric") + +}) + + +test_that(".check_q works", { + + expect_null(BayesTools:::.check_q(0.5)) + expect_null(BayesTools:::.check_q(c(-1, 0, 1))) + expect_null(BayesTools:::.check_q(0.5, lower = 0, upper = 1)) + + expect_error(BayesTools:::.check_q(-1, lower = 0), "must be equal or higher than 0") + expect_error(BayesTools:::.check_q(2, upper = 1), "must be equal or lower than 1") + expect_error(BayesTools:::.check_q("a"), "must be a numeric") + +}) + + +test_that(".check_p works", { + + # Standard probability checks (log.p = FALSE) + expect_null(BayesTools:::.check_p(0.5, FALSE)) + expect_null(BayesTools:::.check_p(c(0, 0.5, 1), FALSE)) + + expect_error(BayesTools:::.check_p(-0.1, FALSE), "must be equal or higher than 0") + expect_error(BayesTools:::.check_p(1.1, FALSE), "must be equal or lower than 1") + expect_error(BayesTools:::.check_p("a", FALSE), "must be a numeric") + + # Log probability checks (log.p = TRUE) + expect_null(BayesTools:::.check_p(-1, TRUE)) + expect_null(BayesTools:::.check_p(c(-2, -1, 0), TRUE)) + + expect_error(BayesTools:::.check_p(0.1, TRUE), "must be equal or lower than 0") + expect_error(BayesTools:::.check_p("a", TRUE), "must be a numeric") + +}) diff --git a/tests/testthat/test-distributions-weightfunctions.R b/tests/testthat/test-distributions-weightfunctions.R index 76749872..ab4eb448 100644 --- a/tests/testthat/test-distributions-weightfunctions.R +++ b/tests/testthat/test-distributions-weightfunctions.R @@ -1,4 +1,22 @@ -context("Distributions - Weight functions") +# ============================================================================ # +# TEST FILE: Distributions - Weight Functions +# ============================================================================ # +# +# PURPOSE: +# Visual regression tests for weight function distributions including +# one-sided and two-sided weight functions. +# +# DEPENDENCIES: +# - vdiffr: Visual regression testing +# - extraDistr: Additional distributions for comparison +# +# SKIP CONDITIONS: +# - skip_if_not_installed("vdiffr") +# +# TAGS: @evaluation, @visual, @distributions, @weightfunctions +# ============================================================================ # + +skip_if_not_installed("vdiffr") test_that("Density function works", { @@ -208,6 +226,7 @@ test_that("Quantile function works", { stats::qbeta(p_seq, 1, 1), c(0, rep(1, length(p_seq) - 1)) )) + expect_equal(mqone.sided(p_seq, alpha = c(.3, 1, 2)), cbind( stats::qbeta(p_seq, 0.3, 3), @@ -253,3 +272,180 @@ test_that("Quantile function works", { expect_equal(mqone.sided_fixed(c(0, .5, 1), omega = c(.5, 1), lower.tail = FALSE), matrix(c(1, .5, .5, 1, 1, 1), ncol = 2, nrow = 3)) expect_equal(mqone.sided_fixed(c(0, .5, 1), omega = c(.5, 1)), mqtwo.sided_fixed(c(0, .5, 1), omega = c(.5, 1))) }) + +test_that("Non-monotonic (general) weight functions are not implemented", { + + ### density function + expect_error(mdone.sided(0.5, alpha1 = c(1, 1), alpha2 = c(1, 1)), "Not implemented") + + ### distribution function + expect_error(mpone.sided(0.5, alpha1 = c(1, 1), alpha2 = c(1, 1)), "Not implemented") + + ### quantile function + expect_error(mqone.sided(0.5, alpha1 = c(1, 1), alpha2 = c(1, 1)), "Not implemented") +}) + +test_that("Input validation for alpha parameter works", { + + ### alpha must be numeric vector or matrix + + expect_error(mdone.sided(0.5, alpha = "not_numeric"), "'alpha' must be a numeric vector or a matrix.") + expect_error(mpone.sided(0.5, alpha = list(1, 1)), "'alpha' must be a numeric vector or a matrix.") + expect_error(mqone.sided(0.5, alpha = data.frame(a = 1, b = 1)), "'alpha' must be a numeric vector or a matrix.") + expect_error(rone.sided(5, alpha = "not_numeric"), "'alpha' must be a numeric vector or a matrix.") + + ### alpha must have at least 2 elements + expect_error(mdone.sided(0.5, alpha = 1), "'alpha' must be a vector of length at least 2.") + expect_error(mpone.sided(0.5, alpha = 1), "'alpha' must be a vector of length at least 2.") + expect_error(mqone.sided(0.5, alpha = 1), "'alpha' must be a vector of length at least 2.") + expect_error(rone.sided(5, alpha = 1), "'alpha' must be a vector of length at least 2.") + + ### alpha matrix must have at least 2 columns + expect_error(mdone.sided(0.5, alpha = matrix(1, nrow = 2, ncol = 1)), "'alpha' must be a matrix with at least 2 columns.") + + ### alpha must be positive + expect_error(mdone.sided(0.5, alpha = c(-1, 1)), "'alpha' must be positive.") + expect_error(mdone.sided(0.5, alpha = c(0, 1)), "'alpha' must be positive.") + expect_error(mpone.sided(0.5, alpha = c(-1, 1)), "'alpha' must be positive.") + expect_error(rone.sided(5, alpha = c(0, 1)), "'alpha' must be positive.") +}) + +test_that("Input validation for omega parameter works", { + + ### omega must be numeric vector or matrix + expect_error(mdone.sided_fixed(0.5, omega = "not_numeric"), "'omega' must be a numeric vector or a matrix.") + expect_error(mpone.sided_fixed(0.5, omega = list(0.5, 1)), "'omega' must be a numeric vector or a matrix.") + expect_error(mqone.sided_fixed(0.5, omega = data.frame(a = 0.5, b = 1)), "'omega' must be a numeric vector or a matrix.") + expect_error(rone.sided_fixed(5, omega = "not_numeric"), "'omega' must be a numeric vector or a matrix.") + + ### omega must have at least 2 elements + expect_error(mdone.sided_fixed(0.5, omega = 0.5), "'omega' must be a vector of length at least 2.") + expect_error(mpone.sided_fixed(0.5, omega = 0.5), "'omega' must be a vector of length at least 2.") + expect_error(mqone.sided_fixed(0.5, omega = 0.5), "'omega' must be a vector of length at least 2.") + expect_error(rone.sided_fixed(5, omega = 0.5), "'omega' must be a vector of length at least 2.") + + ### omega matrix must have at least 2 columns + expect_error(mdone.sided_fixed(0.5, omega = matrix(0.5, nrow = 2, ncol = 1)), "'omega' must be a matrix with at least 2 columns.") + + ### omega must be between 0 and 1 + expect_error(mdone.sided_fixed(0.5, omega = c(-0.1, 1)), "'omega' must be between 0 and 1.") + expect_error(mdone.sided_fixed(0.5, omega = c(0.5, 1.1)), "'omega' must be between 0 and 1.") + expect_error(mpone.sided_fixed(0.5, omega = c(-0.1, 1)), "'omega' must be between 0 and 1.") + expect_error(rone.sided_fixed(5, omega = c(0.5, 1.5)), "'omega' must be between 0 and 1.") +}) + +test_that("Dimension mismatch errors work correctly", { + + ### density function with matrix alpha - dimension mismatch + alpha_mat <- matrix(c(1, 1, 2, 2), nrow = 2, byrow = TRUE) + expect_error(mdone.sided(c(0.5, 0.6, 0.7), alpha = alpha_mat), "Non matching dimensions of 'alpha' and 'x'.") + + ### density function with matrix omega - dimension mismatch + omega_mat <- matrix(c(0.5, 1, 0.6, 1), nrow = 2, byrow = TRUE) + expect_error(mdone.sided_fixed(c(0.5, 0.6, 0.7), omega = omega_mat), "Non matching dimensions of 'omega' and 'x'.") + + ### distribution function with matrix alpha - dimension mismatch + expect_error(mpone.sided(c(0.5, 0.6, 0.7), alpha = alpha_mat), "Non matching dimensions of 'alpha' and 'q'.") + + ### distribution function with matrix omega - dimension mismatch + expect_error(mpone.sided_fixed(c(0.5, 0.6, 0.7), omega = omega_mat), "Non matching dimensions of 'omega' and 'q'.") + + ### quantile function with matrix alpha - dimension mismatch + expect_error(mqone.sided(c(0.25, 0.5, 0.75), alpha = alpha_mat), "Non matching dimensions of 'alpha' and 'p'.") + + ### quantile function with matrix omega - dimension mismatch + expect_error(mqone.sided_fixed(c(0.25, 0.5, 0.75), omega = omega_mat), "Non matching dimensions of 'omega' and 'p'.") + + ### random generator with matrix alpha - dimension mismatch + expect_error(rone.sided(5, alpha = alpha_mat), "Incompatible dimensions of requested number of samples and 'alpha'.") + + ### random generator with matrix omega - dimension mismatch + expect_error(rone.sided_fixed(5, omega = omega_mat), "Incompatible dimensions of requested number of samples and 'omega'.") + + ### general random generator - mismatched alpha1 and alpha2 dimensions + alpha1_mat <- matrix(c(1, 1, 2, 2), nrow = 2, byrow = TRUE) + alpha2_mat <- matrix(c(1, 1, 2, 2, 3, 3), nrow = 3, byrow = TRUE) + expect_error(rone.sided(5, alpha1 = alpha1_mat, alpha2 = alpha2_mat), "Non matching dimensions of 'alpha1' and 'alpha2'.") + + ### general random generator - incompatible n and alpha dimensions + expect_error(rone.sided(5, alpha1 = alpha1_mat, alpha2 = matrix(c(1, 1, 2, 2), nrow = 2, byrow = TRUE)), + "Incompatible dimensions of requested number of samples and 'alpha'.") +}) + +test_that("Matrix input broadcasting works correctly", { + + ### density function - single x with matrix alpha broadcasts correctly + alpha_mat <- matrix(c(1, 1), nrow = 1) + result <- mdone.sided(0.5, alpha = alpha_mat) + expect_equal(dim(result), c(1, 2)) + + ### density function - single x with matrix omega broadcasts correctly + omega_mat <- matrix(c(0.5, 1), nrow = 1) + result <- mdone.sided_fixed(0.5, omega = omega_mat) + expect_equal(dim(result), c(1, 2)) + + ### density function - multiple x with vector omega broadcasts correctly (omega becomes 1-row matrix) + result <- mdone.sided_fixed(c(0.3, 0.5, 0.7), omega = c(0.5, 1)) + expect_equal(dim(result), c(3, 2)) + + ### density function - multiple x with vector alpha broadcasts correctly (alpha becomes 1-row matrix) + result <- mdone.sided(c(0.3, 0.5, 0.7), alpha = c(1, 1)) + expect_equal(dim(result), c(3, 2)) + + ### distribution function - single q with matrix alpha broadcasts correctly + result <- mpone.sided(0.5, alpha = alpha_mat) + expect_equal(dim(result), c(1, 2)) + + ### distribution function - single q with matrix omega broadcasts correctly + result <- mpone.sided_fixed(0.5, omega = omega_mat) + expect_equal(dim(result), c(1, 2)) + + ### distribution function - multiple q with vector omega broadcasts correctly + result <- mpone.sided_fixed(c(0.3, 0.5, 0.7), omega = c(0.5, 1)) + expect_equal(dim(result), c(3, 2)) + + ### distribution function - multiple q with vector alpha broadcasts correctly + result <- mpone.sided(c(0.3, 0.5, 0.7), alpha = c(1, 1)) + expect_equal(dim(result), c(3, 2)) + + ### quantile function - single p with matrix alpha broadcasts correctly + result <- mqone.sided(0.5, alpha = alpha_mat) + expect_equal(dim(result), c(1, 2)) + + ### quantile function - single p with matrix omega broadcasts correctly + result <- mqone.sided_fixed(0.5, omega = omega_mat) + expect_equal(dim(result), c(1, 2)) + + ### quantile function - multiple p with vector omega broadcasts correctly + result <- mqone.sided_fixed(c(0.25, 0.5, 0.75), omega = c(0.5, 1)) + expect_equal(dim(result), c(3, 2)) + + ### quantile function - multiple p with vector alpha broadcasts correctly + result <- mqone.sided(c(0.25, 0.5, 0.75), alpha = c(1, 1)) + expect_equal(dim(result), c(3, 2)) + + ### random generator - n=2 with 2-row matrix alpha works + alpha_mat2 <- matrix(c(1, 1, 2, 2), nrow = 2, byrow = TRUE) + set.seed(1) + result <- rone.sided(2, alpha = alpha_mat2) + expect_equal(nrow(result), 2) + + ### random generator - n=2 with 2-row matrix omega works + omega_mat2 <- matrix(c(0.3, 1, 0.5, 1), nrow = 2, byrow = TRUE) + result <- rone.sided_fixed(2, omega = omega_mat2) + expect_equal(nrow(result), 2) + + ### general random generator - n=2 with 2-row matrix alpha1/alpha2 works + alpha1_mat2 <- matrix(c(1, 1, 2, 2), nrow = 2, byrow = TRUE) + alpha2_mat2 <- matrix(c(1, 1, 3, 3), nrow = 2, byrow = TRUE) + set.seed(1) + result <- rone.sided(2, alpha1 = alpha1_mat2, alpha2 = alpha2_mat2) + expect_equal(nrow(result), 2) + + ### general random generator - broadcasting n=5 with 1-row matrix alpha1/alpha2 + alpha1_single <- matrix(c(1, 1), nrow = 1) + alpha2_single <- matrix(c(1, 1), nrow = 1) + set.seed(1) + result <- rone.sided(5, alpha1 = alpha1_single, alpha2 = alpha2_single) + expect_equal(nrow(result), 5) +}) diff --git a/tests/testthat/test-interpret.R b/tests/testthat/test-interpret.R new file mode 100644 index 00000000..cf047099 --- /dev/null +++ b/tests/testthat/test-interpret.R @@ -0,0 +1,234 @@ +# ============================================================================ # +# TEST FILE: Interpret Functions +# ============================================================================ # +# +# PURPOSE: +# Tests for interpret and interpret2 functions that generate human-readable +# summaries of Bayesian inference results. +# +# DEPENDENCIES: +# - common-functions.R: test_reference_text, REFERENCE_DIR +# +# SKIP CONDITIONS: +# - None (can run on CRAN - pure R with reference file testing) +# +# TAGS: @evaluation, @interpret, @output +# ============================================================================ # + +REFERENCE_DIR <<- testthat::test_path("..", "results", "interpret") +source(testthat::test_path("common-functions.R")) + + +test_that("interpret2 function works", { + + set.seed(1) + + # Test basic interpret2 with all fields + info1 <- list( + list( + inference_name = "Effect", + inference_BF_name = "BF10", + inference_BF = 3.5, + estimate_name = "mu", + estimate_samples = rnorm(1000, 0.3, 0.15), + estimate_units = "kg", + estimate_conditional = FALSE + ) + ) + + result1 <- interpret2(info1, "RoBMA") + test_reference_text(result1, "interpret2_basic.txt") + expect_match(result1, "RoBMA found moderate evidence in favor of the Effect") + expect_match(result1, "BF10 = 3.50") + expect_match(result1, "model-averaged") + expect_match(result1, "kg") + + # Test with conditional = TRUE + info2 <- list( + list( + inference_name = "Effect", + inference_BF_name = "BF10", + inference_BF = 15, + estimate_name = "mu", + estimate_samples = rnorm(1000, 0.5, 0.1), + estimate_units = NULL, + estimate_conditional = TRUE + ) + ) + + result2 <- interpret2(info2, "Test") + test_reference_text(result2, "interpret2_conditional.txt") + expect_match(result2, "strong evidence in favor") + expect_match(result2, "conditional") + expect_false(grepl("model-averaged", result2)) + + # Test evidence against (BF < 1) + info3 <- list( + list( + inference_name = "Effect", + inference_BF_name = "BF01", + inference_BF = 0.1, + estimate_name = "mu", + estimate_samples = rnorm(1000, 0, 0.05), + estimate_units = NULL, + estimate_conditional = NULL + ) + ) + + result3 <- interpret2(info3, "Method") + test_reference_text(result3, "interpret2_evidence_against.txt") + expect_match(result3, "moderate evidence against the Effect") + expect_match(result3, "BF01 = 0.100") + + # Test weak evidence + info4 <- list( + list( + inference_name = "Effect", + inference_BF_name = "BF", + inference_BF = 1.5, + estimate_name = "delta", + estimate_samples = rnorm(1000, 0.1, 0.1), + estimate_units = NULL, + estimate_conditional = FALSE + ) + ) + + result4 <- interpret2(info4, "Test") + test_reference_text(result4, "interpret2_weak_evidence.txt") + expect_match(result4, "weak evidence in favor") + + # Test without estimate samples (inference only) + info5 <- list( + list( + inference_name = "Bias", + inference_BF_name = "BF_pb", + inference_BF = 5 + ) + ) + + result5 <- interpret2(info5, "RoBMA") + test_reference_text(result5, "interpret2_inference_only.txt") + expect_match(result5, "RoBMA found moderate evidence in favor of the Bias") + expect_match(result5, "BF_pb = 5.00") + expect_false(grepl("estimate", result5)) + + # Test multiple specifications + info6 <- list( + list( + inference_name = "Effect", + inference_BF_name = "BF10", + inference_BF = 10, + estimate_name = "mu", + estimate_samples = rnorm(1000, 0.3, 0.1), + estimate_units = NULL, + estimate_conditional = FALSE + ), + list( + inference_name = "Bias", + inference_BF_name = "BF_pb", + inference_BF = 0.5 + ) + ) + + result6 <- interpret2(info6, "Test") + test_reference_text(result6, "interpret2_multiple.txt") + expect_match(result6, "Effect") + expect_match(result6, "Bias") + + # Test without method + info7 <- list( + list( + inference_name = "Effect", + inference_BF_name = "BF", + inference_BF = 2 + ) + ) + + result7 <- interpret2(info7, NULL) + test_reference_text(result7, "interpret2_no_method.txt") + expect_match(result7, "found weak evidence") + +}) + + +test_that(".interpret.BF helper function works", { + + # Strong evidence in favor (BF > 10) + result_strong_favor <- BayesTools:::.interpret.BF(15, "effect", "BF10") + test_reference_text(result_strong_favor, "interpret_BF_strong_favor.txt") + expect_match(result_strong_favor, "strong evidence in favor of the effect") + expect_match(result_strong_favor, "BF10 = 15.00") + + # Moderate evidence in favor (3 < BF < 10) + result_moderate_favor <- BayesTools:::.interpret.BF(5, "effect", NULL) + test_reference_text(result_moderate_favor, "interpret_BF_moderate_favor.txt") + expect_match(result_moderate_favor, "moderate evidence in favor") + expect_match(result_moderate_favor, "BF = 5.00") + + # Weak evidence in favor (1 < BF < 3) + result_weak_favor <- BayesTools:::.interpret.BF(1.5, "effect", "BF") + test_reference_text(result_weak_favor, "interpret_BF_weak_favor.txt") + expect_match(result_weak_favor, "weak evidence in favor") + + # Strong evidence against (BF < 0.1) + result_strong_against <- BayesTools:::.interpret.BF(0.05, "effect", "BF01") + test_reference_text(result_strong_against, "interpret_BF_strong_against.txt") + expect_match(result_strong_against, "strong evidence against the effect") + expect_match(result_strong_against, "BF01 = 0.050") + + # Moderate evidence against (0.1 <= BF < 1/3) + result_moderate_against1 <- BayesTools:::.interpret.BF(0.1, "effect", NULL) + test_reference_text(result_moderate_against1, "interpret_BF_moderate_against1.txt") + expect_match(result_moderate_against1, "moderate evidence against") + + result_moderate_against2 <- BayesTools:::.interpret.BF(0.2, "effect", NULL) + test_reference_text(result_moderate_against2, "interpret_BF_moderate_against2.txt") + expect_match(result_moderate_against2, "moderate evidence against") + + # Weak evidence against (1/3 < BF < 1) + result_weak_against <- BayesTools:::.interpret.BF(0.5, "effect", NULL) + test_reference_text(result_weak_against, "interpret_BF_weak_against.txt") + expect_match(result_weak_against, "weak evidence against") + +}) + + +test_that(".interpret.par helper function works", { + + set.seed(42) + samples <- rnorm(10000, 0.5, 0.1) + + # Test model-averaged (conditional = FALSE) + result1 <- BayesTools:::.interpret.par(samples, "mu", NULL, FALSE) + test_reference_text(result1, "interpret_par_model_averaged.txt") + expect_match(result1, "model-averaged estimate mu") + expect_match(result1, "95% CI") + + # Test model-averaged (conditional = NULL) + result2 <- BayesTools:::.interpret.par(samples, "delta", NULL, NULL) + test_reference_text(result2, "interpret_par_model_averaged_null.txt") + expect_match(result2, "model-averaged") + + # Test conditional + result3 <- BayesTools:::.interpret.par(samples, "mu", NULL, TRUE) + test_reference_text(result3, "interpret_par_conditional.txt") + expect_match(result3, "conditional estimate mu") + expect_false(grepl("model-averaged", result3)) + + # Test with units + result4 <- BayesTools:::.interpret.par(samples, "weight", "kg", FALSE) + test_reference_text(result4, "interpret_par_with_units.txt") + expect_match(result4, "kg") + +}) + + +test_that("interpret function input validation works", { + + # Test specification validation + expect_error(interpret(list(), list(), "not a list", "Test")) + + # Test invalid specification elements + expect_error(interpret(list(), list(), list(list(inference = 1)), "Test")) + +}) diff --git a/tests/testthat/test-model-averaging-edge-cases.R b/tests/testthat/test-model-averaging-edge-cases.R new file mode 100644 index 00000000..9b55be28 --- /dev/null +++ b/tests/testthat/test-model-averaging-edge-cases.R @@ -0,0 +1,168 @@ +# ============================================================================ # +# TEST FILE: Model Averaging Edge Cases +# ============================================================================ # +# +# PURPOSE: +# Edge case tests for model averaging functions including input validation, +# boundary conditions for Bayes factors, and weightfunction mapping edge cases. +# +# DEPENDENCIES: +# - common-functions.R: test_reference_text +# +# SKIP CONDITIONS: +# - None (these are simple edge case tests that don't require fitted models) +# +# MODELS/FIXTURES: +# - None required +# +# TAGS: @edge-cases, @model-averaging, @input-validation +# ============================================================================ # + +# Reference directory for text output comparisons +REFERENCE_DIR <<- testthat::test_path("..", "results", "model-averaging-edge-cases") + +source(testthat::test_path("common-functions.R")) + + +# ============================================================================ # +# SECTION 1: inclusion_BF boundary conditions +# ============================================================================ # +test_that("inclusion_BF handles all-null models", { + + # All null models - should return 0 + prior_probs <- c(0.5, 0.5) + post_probs <- c(0.5, 0.5) + is_null <- c(TRUE, TRUE) + + BF <- inclusion_BF(prior_probs = prior_probs, post_probs = post_probs, is_null = is_null) + expect_equal(BF, 0) + +}) + + +test_that("inclusion_BF handles all-alternative models", { + + # All alternative models - should return Inf + prior_probs <- c(0.5, 0.5) + post_probs <- c(0.5, 0.5) + is_null <- c(FALSE, FALSE) + + BF <- inclusion_BF(prior_probs = prior_probs, post_probs = post_probs, is_null = is_null) + expect_equal(BF, Inf) + +}) + + +test_that("inclusion_BF handles single model case", { + + prior_probs <- 1 + post_probs <- 1 + is_null <- FALSE + + BF <- inclusion_BF(prior_probs = prior_probs, post_probs = post_probs, is_null = is_null) + expect_equal(BF, Inf) + + # Single null model + is_null <- TRUE + BF <- inclusion_BF(prior_probs = prior_probs, post_probs = post_probs, is_null = is_null) + expect_equal(BF, 0) + +}) + + +test_that("inclusion_BF works with marginal likelihoods only", { + + # Test with marginal likelihoods instead of posterior probs + prior_probs <- c(0.5, 0.5) + margliks <- c(-10, -10) # Equal margliks + is_null <- c(TRUE, FALSE) + + BF <- inclusion_BF(prior_probs = prior_probs, margliks = margliks, is_null = is_null) + expect_equal(BF, 1) + + # Unequal margliks - alternative has higher marglik + margliks <- c(-10, -8) # Alternative model is better + BF <- inclusion_BF(prior_probs = prior_probs, margliks = margliks, is_null = is_null) + expect_true(BF > 1) + + # Unequal margliks - null has higher marglik + margliks <- c(-8, -10) # Null model is better + BF <- inclusion_BF(prior_probs = prior_probs, margliks = margliks, is_null = is_null) + expect_true(BF < 1) + +}) + + +# ============================================================================ # +# SECTION 2: weightfunctions_mapping edge cases +# ============================================================================ # +test_that("weightfunctions_mapping handles one-sided priors", { + + # Create one-sided weightfunction prior + wf_onesided <- prior_weightfunction("one.sided", list(c(0.05), c(1, 1))) + + mapping <- weightfunctions_mapping(list(wf_onesided)) + + expect_true(is.list(mapping)) + expect_equal(mapping[[1]], c(2, 1)) + +}) + + +test_that("weightfunctions_mapping handles two-sided priors", { + + # Create two-sided weightfunction prior + wf_twosided <- prior_weightfunction("two.sided", list(c(0.05), c(1, 1))) + + mapping <- weightfunctions_mapping(list(wf_twosided)) + + expect_true(is.list(mapping)) + expect_equal(mapping[[1]], c(2, 1)) + +}) + + +test_that("weightfunctions_mapping handles one_sided argument", { + + # Create two-sided weightfunction prior + wf_twosided <- prior_weightfunction("two.sided", list(c(0.05), c(1, 1))) + + # Test with one_sided = TRUE + mapping_one <- weightfunctions_mapping(list(wf_twosided), one_sided = TRUE) + + expect_true(is.list(mapping_one)) + expect_equal(mapping_one[[1]], c(2, 1, 2)) + +}) + + +test_that("weightfunctions_mapping cuts_only option works", { + + # Create one-sided weightfunction prior + wf_onesided <- prior_weightfunction("one.sided", list(c(0.05), c(1, 1))) + + # Test cuts_only = TRUE + cuts <- weightfunctions_mapping(list(wf_onesided), cuts_only = TRUE) + + expect_equal(cuts, c(0.00, 0.05, 1.00)) +}) + + +test_that("weightfunctions_mapping handles mixed prior list", { + + # Multiple weightfunction priors with different configurations + wf_onesided <- prior_weightfunction("one.sided", list(c(0.05), c(1, 1))) + wf_twosided <- prior_weightfunction("two.sided", list(c(0.05, 0.10), c(1, 1, 1))) + + mapping <- weightfunctions_mapping(list(wf_onesided, wf_twosided)) + + expect_true(is.list(mapping)) + + wf_mapping_info <- paste0( + "Mixed mapping length: ", length(mapping), "\n", + "Inx 1: ", paste0(mapping[[1]], collapse = ","), "\n", + "Inx 2: ", paste0(mapping[[2]], collapse = ",") + ) + test_reference_text(wf_mapping_info, "weightfunctions_mapping_info.txt") + +}) diff --git a/tests/testthat/test-model-averaging-plots-edge-cases.R b/tests/testthat/test-model-averaging-plots-edge-cases.R new file mode 100644 index 00000000..a9c14f08 --- /dev/null +++ b/tests/testthat/test-model-averaging-plots-edge-cases.R @@ -0,0 +1,60 @@ +# ============================================================================ # +# TEST FILE: Model Averaging Plots Edge Cases +# ============================================================================ # +# +# PURPOSE: +# Edge case tests for plot functions including input validation and +# error handling for invalid prior configurations. +# +# DEPENDENCIES: +# - None (pure R testing) +# +# SKIP CONDITIONS: +# - None (can run on CRAN) +# +# MODELS/FIXTURES: +# - None required +# +# TAGS: @edge-cases, @plots, @input-validation +# ============================================================================ # + + +# ============================================================================ # +# SECTION 1: plot_prior_list input validation +# ============================================================================ # +test_that("plot_prior_list rejects non-list input", { + + expect_error( + plot_prior_list(prior("normal", list(0, 1))), + "must be a list of priors" + ) + +}) + + +test_that("plot_prior_list rejects PET-PEESE without prior_list_mu", { + + pet_list <- list( + p1 = prior_PET("normal", list(0, 1)) + ) + expect_error( + plot_prior_list(pet_list), + "prior_list_mu" + ) + +}) + + +test_that("plot_prior_list rejects prior_list_mu when not needed", { + + simple_list <- list( + p1 = prior("normal", list(0, 1)) + ) + expect_error( + plot_prior_list(simple_list, prior_list_mu = list(prior("spike", list(0)))), + "prior_list_mu" + ) + +}) + + diff --git a/tests/testthat/test-model-averaging-plots.R b/tests/testthat/test-model-averaging-plots.R index db1559aa..66062c56 100644 --- a/tests/testthat/test-model-averaging-plots.R +++ b/tests/testthat/test-model-averaging-plots.R @@ -1,2012 +1,755 @@ -context("Model-averaging plot functions") -set.seed(1) - -test_that("helper functions work", { - - # join duplicate - prior_list <- list( - p1 = prior("normal", list(0, 1)), - p2 = prior("lognormal", list(0, .5)), - p3 = prior("point", list(1)), - p4 = prior("normal", list(0, 1)) - ) - - simplified_list <- .simplify_prior_list(prior_list) - - expect_equal(simplified_list, list( - p1 = prior("normal", list(0, 1), prior_weights = 2), - p2 = prior("lognormal", list(0, .5)), - p3 = prior("point", list(1)) - )) - +# ============================================================================ # +# TEST FILE: Model Averaging Plots +# ============================================================================ # +# +# PURPOSE: +# Tests for plot_prior_list, plot_posterior, plot_models, and related +# visualization functions in model averaging. +# +# DEPENDENCIES: +# - vdiffr: Visual regression testing +# - rjags: For tests using pre-fitted models +# - common-functions.R: temp_fits_dir, skip_if_no_fits +# +# SKIP CONDITIONS: +# - skip_if_not_installed("vdiffr"): For visual tests +# - skip_if_no_fits(): For tests using pre-fitted models +# +# MODELS/FIXTURES: +# - fit_summary0, fit_summary1, fit_orthonormal_0, fit_orthonormal_1 +# +# TAGS: @evaluation, @visual, @plots, @model-averaging +# ============================================================================ # - # no duplicate - prior_list <- list( - p1 = prior("normal", list(0, 1)), - p2 = prior("lognormal", list(0, .5)), - p3 = prior("point", list(1)) - ) +source(testthat::test_path("common-functions.R")) - simplified_list <- .simplify_prior_list(prior_list) +skip_if_not_installed("vdiffr") - expect_equal(simplified_list, list( - p1 = prior("normal", list(0, 1)), - p2 = prior("lognormal", list(0, .5)), - p3 = prior("point", list(1)) - )) +# ============================================================================ # +# SECTION 1: plot_prior_list basic tests +# ============================================================================ # +test_that("plot_prior_list handles simple cases", { + set.seed(1) - # multiple duplicates - prior_list <- list( - p1 = prior("normal", list(0, 1)), - p2 = prior("lognormal", list(0, .5)), - p3 = prior("point", list(1)), - p1 = prior("normal", list(0, 1)), - p4 = prior("normal", list(0, 1)), - p2 = prior("lognormal", list(0, .5)) + # Test with a single normal prior + prior_list_normal <- list( + p1 = prior("normal", list(0, 1)) ) - simplified_list <- .simplify_prior_list(prior_list) - - expect_equal(simplified_list, list( - p1 = prior("normal", list(0, 1), prior_weights = 3), - p2 = prior("lognormal", list(0, .5), prior_weights = 2), - p3 = prior("point", list(1)) - )) -}) - - -test_that("prior plot functions (simple) work", { + vdiffr::expect_doppelganger("plot-prior-list-single-normal", function() { + plot_prior_list(prior_list_normal) + }) - ### simple cases - # continuous - prior_list <- list( - p1 = prior("normal", list(0, 1)) + # Test with multiple priors + prior_list_multi <- list( + p1 = prior("normal", list(0, 1)), + p2 = prior("normal", list(1, 0.5)), + p3 = prior("cauchy", list(0, 1)) ) - vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-1", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4) - lines(prior_list$p1, col = "blue", lwd = 3, lty = 2) - }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-2", { - plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4) + geom_prior(prior_list$p1, col = "blue", lwd = 3, lty = 2) + vdiffr::expect_doppelganger("plot-prior-list-multi", function() { + plot_prior_list(prior_list_multi) }) - # spike - prior_list <- list( - p1 = prior("spike", list(.5)) + # Test with gamma prior + prior_list_gamma <- list( + p1 = prior("gamma", list(2, 1)) ) - vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-3", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4) - lines(prior_list$p1, col = "blue", lwd = 3, lty = 2) - }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-4", { - plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4) + geom_prior(prior_list$p1, col = "blue", lwd = 3, lty = 2) + vdiffr::expect_doppelganger("plot-prior-list-gamma", function() { + plot_prior_list(prior_list_gamma) }) +}) - ### the prior joining should give the same prior (+ check truncation) - prior_list <- list( - p1 = prior("normal", list(0, 1), truncation = list(0, Inf)), - p2 = prior("normal", list(0, 1.001), truncation = list(0, Inf)) - ) - vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-5", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4) - lines(prior_list$p1, col = "blue", lwd = 3, lty = 2) - }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-6", function(){ - plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4) + geom_prior(prior_list$p1, col = "blue", lwd = 3, lty = 2) - }) +test_that("plot_prior_list handles orthonormal priors", { + set.seed(1) - ### mixtures - prior_list <- list( - p1 = prior("normal", list(0, 1)), - p2 = prior("normal", list(0, 1), list(1, Inf)), - p3 = prior("spike", list(.5)) - ) - vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-7", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_prior_list(prior_list) - }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-8", function(){ - plot_prior_list(prior_list, plot_type = "ggplot") - }) + # Create orthonormal factor prior + prior_orth <- prior_factor("mnorm", list(mean = 0, sd = 1), contrast = "orthonormal") + attr(prior_orth, "levels") <- 3 - # with additional settings - vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-9", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_prior_list(prior_list, xlab = "xlab", ylab = "ylab", ylab2 = "ylab2", main = "main") - }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-10", function(){ - plot_prior_list(prior_list, plot_type = "ggplot", xlab = "xlab", ylab = "ylab", ylab2 = "ylab2", main = "main") - }) + prior_list <- list(p1 = prior_orth) - # and more spikes - prior_list <- list( - p1 = prior("normal", list(0, 1)), - p2 = prior("normal", list(0, 1), list(1, Inf)), - p3 = prior("spike", list(.5)), - p4 = prior("spike", list(-5)) - ) - vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-11", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) + # Base plot + vdiffr::expect_doppelganger("plot-prior-list-orthonormal-base", function() { plot_prior_list(prior_list) }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-12", function(){ + + # ggplot + vdiffr::expect_doppelganger("plot-prior-list-orthonormal-ggplot", { plot_prior_list(prior_list, plot_type = "ggplot") }) - # verify aggregation - prior_list <- list( - p1 = prior("normal", list(0, 1)), - p2 = prior("normal", list(0, 1)), - p3 = prior("spike", list(.5)), - p4 = prior("spike", list(.5)) - ) - vdiffr::expect_doppelganger("model-averaging-plot-prior-simple-13", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_prior_list(prior_list, lwd = 2) - lines_prior_list(prior_list, lty = 2, col = "red", lwd = 2) + vdiffr::expect_doppelganger("plot-prior-list-orthonormal2-ggplot", { + plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lty = 2, linetype = 2) }) -}) -# skip the rest as it takes too long -skip_on_cran() + # Create orthonormal factor prior with spike + prior_orth0 <- prior_factor("spike", list(0), contrast = "orthonormal") + attr(prior_orth0, "levels") <- 3 -test_that("prior plot functions (PET-PEESE) work", { + prior_list0 <- list(p1 = prior_orth0) - ### simple cases - # continuous - prior_list <- list( - p1 = prior_PET("cauchy", list(0, 1)) - ) - prior_list_mu <- list( - m1 = prior("spike", list(0)) - ) - vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-1", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4, col.fill = ggplot2::alpha("red", .20), n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) - lines(prior_list$p1, col = "blue", lwd = 3, lty = 2, col.fill = ggplot2::alpha("blue", .20)) - }) - prior_list <- list( - p1 = prior_PEESE("cauchy", list(0, 2)) - ) - vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-2", { - plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4, col.fill = scales::alpha("red", .20), n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + geom_prior(prior_list$p1, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20), n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + # Base plot + vdiffr::expect_doppelganger("plot-prior-list-orthonormal-spike", function() { + plot_prior_list(prior_list0) }) - # spike - prior_list <- list( - p1 = prior_PET("point", list(.1)) - ) - vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-3", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4, n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) - lines(prior_list$p1, col = "blue", lwd = 3, lty = 2) - }) - prior_list <- list( - p1 = prior_PEESE("point", list(.05)) + prior_list2 <- list( + p1 = prior_orth, + p2 = prior_orth0 ) - vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-4", { - plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4, n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + geom_prior(prior_list$p1, col = "blue", lwd = 3, lty = 2, n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) - }) - - ### the prior joining should give the same prior - prior_list <- list( - PET1 = prior_PET("cauchy", list(0, 1)), - PET2 = prior_PET("cauchy", list(0, 1.001)) - ) - prior_list_mu <- list( - m1 = prior("spike", list(0)), - m2 = prior("spike", list(0)) - ) - vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-5", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4, col.fill = scales::alpha("red", .20), n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) - lines(prior_list$PET1, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20)) - }) - ### the prior joining should give the same prior - prior_list <- list( - PEESE1 = prior_PEESE("cauchy", list(0, 1)), - PEESE2 = prior_PEESE("cauchy", list(0, 1.001)) - ) - vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-6", function(){ - plot_prior_list(prior_list, plot_type = "ggplot", col = "red", col.fill = scales::alpha("red", .20), lwd = 4, n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + geom_prior_list(prior_list, col = "blue", col.fill = scales::alpha("blue", .20), lwd = 3, lty = 2, n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + # Base plot with transformation + vdiffr::expect_doppelganger("plot-prior-list-orthonormal-spike-and-slab", function() { + suppressMessages(plot_prior_list(prior_list2, transformation = "exp", transformation_settings = TRUE, xlim = c(0.01, 5))) }) +}) - ### mixtures - prior_list <- list( - p1 = prior_PET("cauchy", list(0, 1)), - p2 = prior_PEESE("cauchy", list(0, 5)) - ) - vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-7", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4, col.fill = scales::alpha("red", .20), n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) - lines_prior_list(prior_list, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20), n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) - }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-8", function(){ - plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4, col.fill = scales::alpha("red", .20), n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + geom_prior_list(prior_list, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20), n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) - }) - # with additional settings - vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-9", function(){ - plot_prior_list(prior_list, n_samples = 1000, n_points = 50, xlab = "xlab", ylab = "ylab", main = "main", prior_list_mu = prior_list_mu) - }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-10", function(){ - plot_prior_list(prior_list, n_samples = 1000, n_points = 50, plot_type = "ggplot", xlab = "xlab", ylab = "ylab", main = "main", prior_list_mu = prior_list_mu) - }) +test_that("plot_prior_list handles meandif priors", { + set.seed(1) + # Create meandif factor prior + prior_md <- prior_factor("mnorm", list(mean = 0, sd = 0.5), contrast = "meandif") + attr(prior_md, "levels") <- 3 - ### dealing with other type of priors - prior_list <- list( - p1 = prior_PET("cauchy", list(0, 1)), - p2 = prior_PEESE("cauchy", list(0, 5)), - p3 = prior_none(prior_weights = 4) - ) - prior_list_mu <- list( - m1 = prior("spike", list(0)), - m2 = prior("spike", list(0)), - m3 = prior("normal", list(.3, .15)) - ) - vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-11", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4, col.fill = scales::alpha("red", .20), n_samples = 1000, n_points = 50, ylim = c(0, .5), prior_list_mu = prior_list_mu) - lines_prior_list(prior_list, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20), n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + prior_list <- list(p1 = prior_md) + + # Base plot + vdiffr::expect_doppelganger("plot-prior-list-meandif-base", function() { + plot_prior_list(prior_list) }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-PETPEESE-12", function(){ - plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4, col.fill = scales::alpha("red", .20), n_samples = 1000, n_points = 50, ylim = c(0, .5), prior_list_mu = prior_list_mu) + geom_prior_list(prior_list, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20), n_samples = 1000, n_points = 50, prior_list_mu = prior_list_mu) + + # ggplot + vdiffr::expect_doppelganger("plot-prior-list-meandif-ggplot", { + plot_prior_list(prior_list, plot_type = "ggplot") }) + }) -test_that("prior plot functions (weightfunctions) work", { +test_that("plot_prior_list handles weightfunction priors", { + set.seed(1) - ### simple cases - # continuous - prior_list <- list( - p1 = prior_weightfunction("one.sided", list(c(.05, 0.10), c(1, 1, 1))) - ) + # Create one-sided weightfunction prior + wf_prior <- prior_weightfunction("one.sided", list(c(0.05), c(1, 1))) - vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-1", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4, col.fill = scales::alpha("red", .20)) - lines(prior_list$p1, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20)) + prior_list_wf <- list(p1 = wf_prior) + + vdiffr::expect_doppelganger("plot-prior-list-weightfunction", function() { + plot_prior_list(prior_list_wf) }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-2", { - plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4, col.fill = scales::alpha("red", .20)) + geom_prior(prior_list$p1, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20)) + + # Test ggplot version + vdiffr::expect_doppelganger("plot-prior-list-weightfunction-ggplot", { + plot_prior_list(prior_list_wf, plot_type = "ggplot") }) - # spike - prior_list <- list( - p1 = prior_weightfunction("one.sided.fixed", list(c(.05), c(1, .5))) - ) +}) - vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-3", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4) - lines(prior_list$p1, col = "blue", lwd = 3, lty = 2) - }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-4", { - plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4) + geom_prior(prior_list$p1, col = "blue", lwd = 3, lty = 2) - }) +test_that("scale_y2 is handled correctly for mixed distributions", { + set.seed(1) - ### the prior joining should give the same prior - prior_list <- list( - p1 = prior_weightfunction("one.sided", list(c(.05, 0.10), c(1, 1, 1))), - p2 = prior_weightfunction("one.sided", list(c(.05, 0.10), c(1, 1, 1.0001))) + # Create a list with both continuous and point priors + prior_list <- list( + p1 = prior("normal", list(0, 1)), + p2 = prior("spike", list(0)) ) - vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-5", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4, col.fill = scales::alpha("red", .20)) - lines(prior_list$p1, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20)) + + # Base plot should handle dual y-axis + vdiffr::expect_doppelganger("plot-prior-list-dual-axis", function() { + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4)) + plot_prior_list(prior_list) }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-6", function(){ - plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4, col.fill = scales::alpha("red", .20)) + geom_prior_list(prior_list, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20)) + + # ggplot should handle it differently + vdiffr::expect_doppelganger("plot-prior-list-dual-axis-ggplot", { + plot_prior_list(prior_list, plot_type = "ggplot") }) +}) + - ### mixtures - prior_list <- list( - p1 = prior_weightfunction("one.sided", list(c(.025), c(1, 1))), - p2 = prior_weightfunction("two.sided", list(c(.05), c(1, 1))), - p3 = prior_weightfunction("one.sided.fixed", list(c(.05), c(1, .5)), prior_weights = 10) +# ============================================================================ # +# SECTION 2: lines_prior_list tests +# ============================================================================ # +test_that("lines_prior_list handles various configurations", { + set.seed(1) + prior_list <- list( + p1 = prior("normal", list(0, 1)), + p2 = prior("normal", list(0, 2)) ) - vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-7", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4, col.fill = scales::alpha("red", .20), rescale_x = TRUE) - lines_prior_list(prior_list, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20), rescale_x = TRUE) - }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-8", function(){ - plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4, col.fill = scales::alpha("red", .20), rescale_x = TRUE) + geom_prior_list(prior_list, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20), rescale_x = TRUE) - }) - # with additional settings - vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-9", function(){ - plot_prior_list(prior_list, xlab = "xlab", ylab = "ylab", main = "main") + # Test adding lines to existing plot + vdiffr::expect_doppelganger("lines-prior-list-add", function() { + plot(NULL, xlim = c(-5, 5), ylim = c(0, 0.5), xlab = "", ylab = "") + lines_prior_list(prior_list, col = "red", lwd = 2) }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-10", function(){ - plot_prior_list(prior_list, plot_type = "ggplot", xlab = "xlab", ylab = "ylab", main = "main") + + # Test with custom xlim + vdiffr::expect_doppelganger("lines-prior-list-xlim", function() { + plot(NULL, xlim = c(-3, 3), ylim = c(0, 0.5), xlab = "", ylab = "") + lines_prior_list(prior_list, xlim = c(-3, 3), col = "blue") }) +}) + - ### dealing with other type of priors - prior_list <- list( - p1 = prior_weightfunction("one.sided", list(c(.5), c(1, 1))), - p2 = prior_none(), - p3 = prior_none() +# ============================================================================ # +# SECTION 3: geom_prior_list tests +# ============================================================================ # +test_that("geom_prior_list handles various configurations", { + set.seed(1) + prior_list <- list( + p1 = prior("normal", list(0, 1)), + p2 = prior("spike", list(0.5)) ) - vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-11", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4, col.fill = scales::alpha("red", .20), rescale_x = TRUE) - lines_prior_list(prior_list, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20), rescale_x = TRUE) - }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-wf-12", function(){ - plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4, col.fill = scales::alpha("red", .20), rescale_x = TRUE) + geom_prior_list(prior_list, col = "blue", lwd = 3, lty = 2, col.fill = scales::alpha("blue", .20), rescale_x = TRUE) + + # Test adding to ggplot + vdiffr::expect_doppelganger("geom-prior-list-add", { + ggplot2::ggplot() + + ggplot2::xlim(-4, 4) + + ggplot2::ylim(0, 1) + + geom_prior_list(prior_list, col = "red") }) }) -test_that("prior plot functions (orthonormal) work", { +# ============================================================================ # +# SECTION 4: plot_posterior tests +# ============================================================================ # +test_that("plot_posterior handles various sample types", { + set.seed(1) + skip_if_not_installed("rjags") + skip_on_cran() + skip_if_no_fits() - ### simple cases - prior_list <- list( - p1 = prior_factor("mnormal", list(mean = 0, sd = 1), contrast = "orthonormal") - ) - prior_list$p1$parameters$K <- 3 + # Load fits + fit_summary0 <- readRDS(file.path(temp_fits_dir, "fit_summary0.RDS")) + marglik_summary0 <- readRDS(file.path(temp_marglik_dir, "fit_summary0.RDS")) - vdiffr::expect_doppelganger("model-averaging-plot-prior-orthonormal-1", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4) - lines(prior_list$p1, col = "blue", lwd = 3, lty = 2) - }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-orthonormal-2", { - plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4) + - geom_prior(prior_list$p1, col = "blue", lwd = 3, lty = 2) - }) + fit_summary1 <- readRDS(file.path(temp_fits_dir, "fit_summary1.RDS")) + marglik_summary1 <- readRDS(file.path(temp_marglik_dir, "fit_summary1.RDS")) - # spike & slab mixture - prior_list <- list( - p1 = prior_factor("mnormal", list(mean = 0, sd = 1), contrast = "orthonormal"), - p2 = prior("spike", list(0)) + models <- list( + list(fit = fit_summary0, marglik = marglik_summary0, prior_weights = 1), + list(fit = fit_summary1, marglik = marglik_summary1, prior_weights = 1) ) - prior_list$p1$parameters$K <- 3 - vdiffr::expect_doppelganger("model-averaging-plot-prior-orthonormal-3", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4) - }) + mixed_posteriors <- mix_posteriors( + model_list = models, + parameters = c("m", "omega"), + is_null_list = list("m" = c(FALSE, FALSE), "omega" = c(TRUE, FALSE)), + seed = 1, + n_samples = 1000 + ) - vdiffr::expect_doppelganger("model-averaging-plot-prior-orthonormal-4", { - plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4) + # Test simple posterior plot + vdiffr::expect_doppelganger("plot-posterior-simple", function() { + plot_posterior(mixed_posteriors, "m") }) + # Test with prior overlay + vdiffr::expect_doppelganger("plot-posterior-with-prior", function() { + plot_posterior(mixed_posteriors, "m", prior = TRUE) + }) - ### mixtures - prior_list <- list( - p1 = prior_factor("mnormal", list(mean = 0, sd = 1), contrast = "orthonormal"), - p2 = prior_factor("mcauchy", list(location = 0, scale = 1), contrast = "orthonormal"), - p3 = prior("spike", list(0)) - ) - prior_list$p1$parameters$K <- 3 - prior_list$p2$parameters$K <- 3 - - vdiffr::expect_doppelganger("model-averaging-plot-prior-orthonormal-5", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4) - lines_prior_list(prior_list, col = "blue", lwd = 3, lty = 2) + # Test ggplot version + vdiffr::expect_doppelganger("plot-posterior-ggplot", { + plot_posterior(mixed_posteriors, "m", plot_type = "ggplot") }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-orthonormal-6", function(){ - plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 2) + - geom_prior_list(prior_list, col = "blue", lwd = 1, lty = 2) + + # Test with custom xlim + vdiffr::expect_doppelganger("plot-posterior-xlim", function() { + plot_posterior(mixed_posteriors, "m", xlim = c(-2, 2)) }) }) -test_that("prior plot functions (treatment) work", { +test_that("plot_posterior handles weightfunction posteriors", { + set.seed(1) + skip_if_not_installed("rjags") + skip_on_cran() + skip_if_no_fits() - ### simple cases - prior_list <- list( - p1 = prior_factor("beta", list(alpha = 4, beta = 5), contrast = "treatment") - ) + # Load fits + fit_summary0 <- readRDS(file.path(temp_fits_dir, "fit_summary0.RDS")) + marglik_summary0 <- readRDS(file.path(temp_marglik_dir, "fit_summary0.RDS")) - vdiffr::expect_doppelganger("model-averaging-plot-prior-treatment-1", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4) - lines(prior_list$p1, col = "blue", lwd = 3, lty = 2) - }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-treatment-2", { - plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4) + - geom_prior(prior_list$p1, col = "blue", lwd = 3, lty = 2) - }) + fit_summary1 <- readRDS(file.path(temp_fits_dir, "fit_summary1.RDS")) + marglik_summary1 <- readRDS(file.path(temp_marglik_dir, "fit_summary1.RDS")) - # spike & slab mixture - prior_list <- list( - p1 = prior_factor("beta", list(alpha = 4, beta = 5), contrast = "treatment"), - p2 = prior("spike", list(0)) + models <- list( + list(fit = fit_summary0, marglik = marglik_summary0, prior_weights = 1), + list(fit = fit_summary1, marglik = marglik_summary1, prior_weights = 1) ) - vdiffr::expect_doppelganger("model-averaging-plot-prior-treatment-3", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4) - }) - - vdiffr::expect_doppelganger("model-averaging-plot-prior-treatment-4", { - plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 2) - }) - - - ### mixtures - prior_list <- list( - p1 = prior_factor("normal", list(mean = 0, sd = 1), contrast = "treatment"), - p2 = prior_factor("beta", list(alpha = 4, beta = 5), contrast = "treatment"), - p3 = prior("spike", list(0)) + mixed_posteriors <- mix_posteriors( + model_list = models, + parameters = c("m", "omega"), + is_null_list = list("m" = c(FALSE, FALSE), "omega" = c(TRUE, FALSE)), + seed = 1, + n_samples = 1000 ) - vdiffr::expect_doppelganger("model-averaging-plot-prior-treatment-5", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4) - lines_prior_list(prior_list, col = "blue", lwd = 3, lty = 2) - }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-treatment-6", function(){ - plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 2) + - geom_prior_list(prior_list, col = "blue", lwd = 1, lty = 2) + # Test weightfunction posterior plot + vdiffr::expect_doppelganger("plot-posterior-omega", function() { + plot_posterior(mixed_posteriors, "omega", n_points = 50, n_samples = 500) }) }) -test_that("prior plot functions (independent) work", { +# ============================================================================ # +# SECTION 5: plot_models tests +# ============================================================================ # +test_that("plot_models handles various configurations", { + set.seed(1) + skip_if_not_installed("rjags") + skip_on_cran() + skip_if_no_fits() - ### simple cases - prior_list <- list( - p1 = prior_factor("beta", list(alpha = 4, beta = 5), contrast = "independent") - ) + # Load fits + fit_summary0 <- readRDS(file.path(temp_fits_dir, "fit_summary0.RDS")) + marglik_summary0 <- readRDS(file.path(temp_marglik_dir, "fit_summary0.RDS")) - vdiffr::expect_doppelganger("model-averaging-plot-prior-independent-1", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4) - lines(prior_list$p1, col = "blue", lwd = 3, lty = 2) - }) + fit_summary1 <- readRDS(file.path(temp_fits_dir, "fit_summary1.RDS")) + marglik_summary1 <- readRDS(file.path(temp_marglik_dir, "fit_summary1.RDS")) - # spike & slab mixture - prior_list <- list( - p1 = prior_factor("beta", list(alpha = 4, beta = 5), contrast = "independent"), - p2 = prior("spike", list(0)) + models <- list( + list(fit = fit_summary0, marglik = marglik_summary0, prior_weights = 1, fit_summary = runjags_estimates_table(fit_summary0)), + list(fit = fit_summary1, marglik = marglik_summary1, prior_weights = 1, fit_summary = runjags_estimates_table(fit_summary1)) ) + models <- models_inference(models) - vdiffr::expect_doppelganger("model-averaging-plot-prior-independent-2", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4) - }) + inference <- ensemble_inference( + model_list = models, + parameters = c("m"), + is_null_list = list("m" = c(FALSE, FALSE)) + ) - ### mixtures - prior_list <- list( - p1 = prior_factor("normal", list(mean = 0, sd = 1), contrast = "independent"), - p2 = prior_factor("beta", list(alpha = 4, beta = 5), contrast = "independent"), - p3 = prior("spike", list(0)) + mixed_posteriors <- mix_posteriors( + model_list = models, + parameters = c("m"), + is_null_list = list("m" = c(FALSE, FALSE)), + seed = 1, + n_samples = 1000 ) - vdiffr::expect_doppelganger("model-averaging-plot-prior-independent-3", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4) - lines_prior_list(prior_list, col = "blue", lwd = 3, lty = 2) + # Test basic plot_models + vdiffr::expect_doppelganger("plot-models-basic", function() { + plot_models(models, mixed_posteriors, inference, "m") }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-independent-4", function(){ - plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 2) + - geom_prior_list(prior_list, col = "blue", lwd = 1, lty = 2) + + # Test ggplot version + vdiffr::expect_doppelganger("plot-models-ggplot", { + plot_models(models, mixed_posteriors, inference, "m", plot_type = "ggplot") }) }) -test_that("prior plot functions (meandif) work", { +test_that("plot_models handles order argument", { + set.seed(1) + skip_if_not_installed("rjags") + skip_on_cran() + skip_if_no_fits() + + # Load fits + fit_summary0 <- readRDS(file.path(temp_fits_dir, "fit_summary0.RDS")) + marglik_summary0 <- readRDS(file.path(temp_marglik_dir, "fit_summary0.RDS")) + + fit_summary1 <- readRDS(file.path(temp_fits_dir, "fit_summary1.RDS")) + marglik_summary1 <- readRDS(file.path(temp_marglik_dir, "fit_summary1.RDS")) - ### simple cases - prior_list <- list( - p1 = prior_factor("mnormal", list(mean = 0, sd = 1), contrast = "meandif") + models <- list( + list(fit = fit_summary0, marglik = marglik_summary0, prior_weights = 1, fit_summary = runjags_estimates_table(fit_summary0)), + list(fit = fit_summary1, marglik = marglik_summary1, prior_weights = 1, fit_summary = runjags_estimates_table(fit_summary1)) ) - prior_list$p1$parameters$K <- 3 + models <- models_inference(models) - vdiffr::expect_doppelganger("model-averaging-plot-prior-meandif-1", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4) - lines(prior_list$p1, col = "blue", lwd = 3, lty = 2) - }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-meandif-2", { - plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4) + - geom_prior(prior_list$p1, col = "blue", lwd = 3, lty = 2) - }) + inference <- ensemble_inference( + model_list = models, + parameters = c("m"), + is_null_list = list("m" = c(FALSE, FALSE)) + ) - # spike & slab mixture - prior_list <- list( - p1 = prior_factor("mnormal", list(mean = 0, sd = 1), contrast = "meandif"), - p2 = prior("spike", list(0)) + mixed_posteriors <- mix_posteriors( + model_list = models, + parameters = c("m"), + is_null_list = list("m" = c(FALSE, FALSE)), + seed = 1, + n_samples = 1000 ) - prior_list$p1$parameters$K <- 3 - vdiffr::expect_doppelganger("model-averaging-plot-prior-meandif-3", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4) + # Test with order = decreasing by estimate + vdiffr::expect_doppelganger("plot-models-order-decreasing-estimate", function() { + BayesTools::plot_models(models, mixed_posteriors, inference, "m", order = list("decreasing", "estimate")) }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-meandif-4", { - plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 4) + # Test with order = increasing by BF + vdiffr::expect_doppelganger("plot-models-order-increasing-bf", function() { + BayesTools::plot_models(models, mixed_posteriors, inference, "m", order = list("decreasing", "BF")) }) + # Test with order = decreasing by probability + vdiffr::expect_doppelganger("plot-models-order-decreasing-prob", function() { + BayesTools::plot_models(models, mixed_posteriors, inference, "m", order = list("decreasing", "probability")) + }) - ### mixtures - prior_list <- list( - p1 = prior_factor("mnormal", list(mean = 0, sd = 1), contrast = "meandif"), - p2 = prior_factor("mcauchy", list(location = 0, scale = 1), contrast = "meandif"), - p3 = prior("spike", list(0)) - ) - prior_list$p1$parameters$K <- 3 - prior_list$p2$parameters$K <- 3 + # Test with transformation + vdiffr::expect_doppelganger("plot-models-order-trans", function() { + BayesTools::plot_models(models, mixed_posteriors, inference, "m", transformation = "exp") + }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-meandif-5", function(){ - plot_prior_list(prior_list, col = "red", lwd = 4) - lines_prior_list(prior_list, col = "blue", lwd = 3, lty = 2) + # Test with transformation and prior + vdiffr::expect_doppelganger("plot-models-order-trans-prior", function() { + BayesTools::plot_models(models, mixed_posteriors, inference, "m", prior = TRUE, transformation = "exp") }) - vdiffr::expect_doppelganger("model-averaging-plot-prior-meandif-6", function(){ - plot_prior_list(prior_list, plot_type = "ggplot", col = "red", lwd = 2) + - geom_prior_list(prior_list, col = "blue", lwd = 1, lty = 2) + + # Test with transformation ggplot + vdiffr::expect_doppelganger("plot-models-order-trans-ggplot", function() { + BayesTools::plot_models(models, mixed_posteriors, inference, "m", transformation = "exp", plot_type = "ggplot") }) -}) + # Test with transformation and prior ggplot + vdiffr::expect_doppelganger("plot-models-order-trans-prior-ggplot", function() { + BayesTools::plot_models(models, mixed_posteriors, inference, "m", prior = TRUE, transformation = "exp", plot_type = "ggplot") + }) +}) -test_that("posterior plot functions (simple) work", { +test_that("plot_models handles orthonormal priors", { set.seed(1) - data <- NULL - priors_list0 <- list( - m = prior("spike", list(0)), - s = prior("normal", list(0, 1), list(0, Inf)) - ) - priors_list1 <- list( - m = prior("normal", list(0, .3)), - s = prior("normal", list(0, 1), list(0, Inf)) - ) - model_syntax <- - "model - { - }" - log_posterior <- function(parameters, data){ - return(0) - } - # fit the models - fit0 <- suppressWarnings(JAGS_fit(model_syntax, data, priors_list0, chains = 1, adapt = 100, burnin = 150, sample = 500, seed = 0)) - fit1 <- suppressWarnings(JAGS_fit(model_syntax, data, priors_list1, chains = 1, adapt = 100, burnin = 150, sample = 500, seed = 1)) - # get marginal likelihoods - marglik0 <- JAGS_bridgesampling(fit0, log_posterior = log_posterior, data = data, prior_list = priors_list0) - marglik1 <- JAGS_bridgesampling(fit1, log_posterior = log_posterior, data = data, prior_list = priors_list1) - # automatically mix posteriors + skip_if_not_installed("rjags") + skip_on_cran() + skip_if_no_fits() + + # Load orthonormal models with marginal likelihoods + fit_orthonormal_0 <- readRDS(file.path(temp_fits_dir, "fit_orthonormal_0.RDS")) + marglik_orthonormal_0 <- readRDS(file.path(temp_marglik_dir, "fit_orthonormal_0.RDS")) + + fit_orthonormal_1 <- readRDS(file.path(temp_fits_dir, "fit_orthonormal_1.RDS")) + marglik_orthonormal_1 <- readRDS(file.path(temp_marglik_dir, "fit_orthonormal_1.RDS")) + models <- list( - list(fit = fit0, marglik = marglik0, prior_weights = 1), - list(fit = fit1, marglik = marglik1, prior_weights = 1) + list(fit = fit_orthonormal_0, marglik = marglik_orthonormal_0, prior_weights = 1, + fit_summary = suppressMessages(runjags_estimates_table(fit_orthonormal_0, transform_factors = TRUE))), + list(fit = fit_orthonormal_1, marglik = marglik_orthonormal_1, prior_weights = 1, + fit_summary = suppressMessages(runjags_estimates_table(fit_orthonormal_1, transform_factors = TRUE))) ) - mixed_posteriors <- mix_posteriors(model_list = models, parameters = c("m", "s"), is_null_list = list("m" = 1, "s" = 0), seed = 1) + models <- models_inference(models) + # Get factor parameter names from the model + prior_list <- attr(fit_orthonormal_1, "prior_list") + factor_params <- names(prior_list)[sapply(prior_list, is.prior.factor)] - vdiffr::expect_doppelganger("model-averaging-plot-posterior-simple-1", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "m", lwd = 2, col = "red", par_name = expression(mu)) - lines_prior_list(attr(mixed_posteriors$m, "prior_list"), col = "blue") - }) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-simple-2", { - plot_posterior(mixed_posteriors, "m", plot_type = "ggplot", lwd = 2, col = "red") + geom_prior_list(attr(mixed_posteriors$m, "prior_list"), col = "blue") - }) + inference <- ensemble_inference( + model_list = models, + parameters = factor_params, + is_null_list = setNames(list(c(TRUE, FALSE)), factor_params) + ) - # checks truncation - vdiffr::expect_doppelganger("model-averaging-plot-posterior-simple-3", function(){ - plot_posterior(mixed_posteriors, "s") - lines_prior_list(attr(mixed_posteriors$s, "prior_list"), col = "blue") - }) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-simple-4", { - plot_posterior(mixed_posteriors, "s", plot_type = "ggplot") - }) + mixed_posteriors <- mix_posteriors( + model_list = models, + parameters = factor_params, + is_null_list = setNames(list(c(TRUE, FALSE)), factor_params), + seed = 1, + n_samples = 1000 + ) - # check transformation - vdiffr::expect_doppelganger("model-averaging-plot-posterior-simple-5", function(){ - plot_posterior(mixed_posteriors, "s", transformation = "exp") - lines_prior_list(attr(mixed_posteriors$s, "prior_list"), col = "blue", transformation = "exp") + # Test with orthonormal priors + vdiffr::expect_doppelganger("plot-models-orthonormal", function() { + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4), mfrow = c(3, 1)) + BayesTools::plot_models(models, mixed_posteriors, inference, factor_params) }) - # prior and posterior - vdiffr::expect_doppelganger("model-averaging-plot-posterior-simple-6", function(){ + vdiffr::expect_doppelganger("plot-models-orthonormal-2", function() { oldpar <- graphics::par(no.readonly = TRUE) on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "m", lwd = 2, col = "red", prior = TRUE, dots_prior = list(col = "blue", lty = 2)) + par(mar = c(4, 4, 1, 4), mfrow = c(3, 1)) + BayesTools::plot_models(models, mixed_posteriors, inference, factor_params, transformation = "exp") }) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-simple-7", function(){ - plot_posterior(mixed_posteriors, "m", plot_type = "ggplot", lwd = 2, col = "red", prior = TRUE, dots_prior = list(col = "blue", lty = 2)) + vdiffr::expect_doppelganger("plot-models-orthonormal-3", function() { + oldpar <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(mar = oldpar[["mar"]])) + par(mar = c(4, 4, 1, 4), mfrow = c(3, 1)) + BayesTools::plot_models(models, mixed_posteriors, inference, factor_params, transformation = "exp", prior = TRUE) }) - # check transformation - vdiffr::expect_doppelganger("model-averaging-plot-posterior-simple-8", function(){ - plot_posterior(mixed_posteriors, "s", transformation = "exp", lwd = 2, col = "red", prior = TRUE, dots_prior = list(col = "blue", lty = 2)) - }) }) -test_that("posterior plot functions (PET-PEESE) work", { - +# ============================================================================ # +# SECTION 6: .plot_prior_list.factor tests +# ============================================================================ # +test_that(".plot_prior_list.factor handles point priors within factor", { set.seed(1) - data <- NULL - priors_list0 <- list( - mu = prior("spike", list(0)), - PET = prior_PET("normal", list(0, .2)) - ) - priors_list1 <- list( - mu = prior("spike", list(0)), - PEESE = prior_PEESE("normal", list(0, .8)) - ) - model_syntax <- - "model - { - }" - log_posterior <- function(parameters, data){ - return(0) - } - # fit the models - fit0 <- suppressWarnings(JAGS_fit(model_syntax, data, priors_list0, chains = 1, adapt = 100, burnin = 150, sample = 2000, seed = 0)) - fit1 <- suppressWarnings(JAGS_fit(model_syntax, data, priors_list1, chains = 1, adapt = 100, burnin = 150, sample = 2000, seed = 1)) - # get marginal likelihoods - marglik0 <- JAGS_bridgesampling(fit0, log_posterior = log_posterior, data = data, prior_list = priors_list0) - marglik1 <- JAGS_bridgesampling(fit1, log_posterior = log_posterior, data = data, prior_list = priors_list1) - # automatically mix posteriors - models <- list( - list(fit = fit0, marglik = marglik0, prior_weights = 1), - list(fit = fit1, marglik = marglik1, prior_weights = 1) - ) - mixed_posteriors <- mix_posteriors(model_list = models, parameters = c("mu", "PET", "PEESE"), is_null_list = list("mu" = c(T, T), "PET" = c(F,T), "PEESE" = c(T,F)), seed = 1) + # Test factor prior with spike + prior_spike <- prior("spike", list(0)) + prior_factor_treat <- prior_factor("normal", list(mean = 0, sd = 1), contrast = "treatment") + attr(prior_factor_treat, "levels") <- 3 + prior_list <- list(p1 = prior_spike, p2 = prior_factor_treat) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-PETPEESE-1", function(){ - plot_posterior(mixed_posteriors, "PETPEESE", lwd = 2, col = "red", col.fill = scales::alpha("red", .20), par_name = "PET-PEESE", n_points = 50, ylim = c(0, 1)) - lines_prior_list(list(priors_list0$PET, priors_list1$PEESE), n_points = 50, n_samples = 1000, col = "blue", col.fill = scales::alpha("blue", .20), prior_list_mu = list(priors_list0$mu, priors_list1$mu)) - }) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-PETPEESE-2", { - plot_posterior(mixed_posteriors, "PETPEESE", plot_type = "ggplot", lwd = 2, col = "red", col.fill = scales::alpha("red", .20), ylim = c(0, .5)) + geom_prior_list(list(priors_list0$PET, priors_list1$PEESE), n_points = 50, n_samples = 1000, col = "blue", col.fill = scales::alpha("blue", .20), prior_list_mu = list(priors_list0$mu, priors_list1$mu)) + vdiffr::expect_doppelganger("plot-factor-with-spike", function() { + plot_prior_list(prior_list) }) - # check transformation - vdiffr::expect_doppelganger("model-averaging-plot-posterior-PETPEESE-5", function(){ - plot_posterior(mixed_posteriors, "PETPEESE", transformation = "lin", transformation_arguments = list(a = 0, b = 0.5), main = "PET-PEESE (1/2x)") - lines_prior_list(list(priors_list0$PET, priors_list1$PEESE), n_points = 50, n_samples = 1000, col = "blue", col.fill = scales::alpha("blue", .20), transformation = "lin", transformation_arguments = list(a = 0, b = 0.5), prior_list_mu = list(priors_list0$mu, priors_list1$mu)) + vdiffr::expect_doppelganger("plot-factor-with-spike-trans", function() { + plot_prior_list(prior_list, transformation = "tanh") }) - # prior and posterior - vdiffr::expect_doppelganger("model-averaging-plot-posterior-PETPEESE-6", function(){ - plot_posterior(mixed_posteriors, "PETPEESE", lwd = 2, col = "red", col.fill = scales::alpha("red", .20), prior = TRUE, n_points = 50, n_samples = 1000, dots_prior = list(col = "blue", col.fill = scales::alpha("blue", .20), lty = 2)) + vdiffr::expect_doppelganger("plot-factor-with-spike-trans-settings", function() { + plot_prior_list(prior_list, transformation = "tanh", transformation_settings = T, xlim = c(-0.5, 0.5)) }) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-PETPEESE-7", function(){ - plot_posterior(mixed_posteriors, "PETPEESE", plot_type = "ggplot", lwd = 2, col = "red", col.fill = scales::alpha("red", .20), n_points = 50, n_samples = 1000, prior = TRUE, dots_prior = list(col = "blue", col.fill = scales::alpha("blue", .20), lty = 2)) - }) +}) - # check transformation - vdiffr::expect_doppelganger("model-averaging-plot-posterior-PETPEESE-8", function(){ - plot_posterior(mixed_posteriors, "PETPEESE", transformation = "lin", transformation_arguments = list(a = 0, b = 0.5), lwd = 2, col = "red", n_points = 50, n_samples = 1000, col.fill = scales::alpha("red", .20), prior = TRUE, dots_prior = list(col = "blue", col.fill = scales::alpha("blue", .20), lty = 2)) - }) - # add an overhelming missing model - priors_list2 <- list( - mu = prior("normal", list(.2, .2), prior_weights = 4) - ) - # fit the models - fit2 <- suppressWarnings(JAGS_fit(model_syntax, data, priors_list2, chains = 1, adapt = 100, burnin = 150, sample = 2000, seed = 1)) - # get marginal likelihoods - marglik2 <- JAGS_bridgesampling(fit2, log_posterior = log_posterior, data = data, prior_list = priors_list2) - # automatically mix posteriors - models <- list( - list(fit = fit0, marglik = marglik0, prior_weights = 1), - list(fit = fit1, marglik = marglik1, prior_weights = 1), - list(fit = fit2, marglik = marglik2, prior_weights = 4) - ) - mixed_posteriors <- mix_posteriors(model_list = models, parameters = c("mu" ,"PET", "PEESE"), is_null_list = list("mu" = c(T, T, F),"PET" = c(F,T,F), "PEESE" = c(T,F,F)), seed = 1) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-PETPEESE-9", function(){ - plot_posterior(mixed_posteriors, "PETPEESE", ylim = c(0, 3), lwd = 2, col = "red", col.fill = scales::alpha("red", .20), n_points = 50, n_samples = 1000, prior = TRUE, dots_prior = list(col = "blue", col.fill = scales::alpha("blue", .20), lty = 2)) +test_that(".plot_prior_list.factor handles transformation", { + set.seed(1) + + # Create treatment factor prior with normal distribution + prior_treat <- prior_factor("normal", list(mean = 0, sd = 0.5), contrast = "treatment") + attr(prior_treat, "levels") <- 3 + + prior_list <- list(p1 = prior_treat) + + vdiffr::expect_doppelganger("plot-factor-transformation", function() { + plot_prior_list(prior_list, transformation = "exp") }) }) +# ============================================================================ # +# SECTION: get_scale_transformation with plotting +# ============================================================================ # -test_that("posterior plot functions (weightfunctions) work", { +test_that("exp_lin transformation functions are defined correctly", { + # Test that exp_lin transformation is correctly defined + # (used for log-intercept unscaling) - set.seed(1) - data <- NULL - priors_list0 <- list( - omega = prior_weightfunction("one.sided", list(c(.025), c(1, 1))) - ) - priors_list1 <- list( - omega = prior_weightfunction("two.sided", list(c(.05), c(1, 1))) - ) - model_syntax <- - "model - { - }" - log_posterior <- function(parameters, data){ - return(0) - } - # fit the models - fit0 <- suppressWarnings(JAGS_fit(model_syntax, data, priors_list0, chains = 1, adapt = 100, burnin = 150, sample = 2000, seed = 0)) - fit1 <- suppressWarnings(JAGS_fit(model_syntax, data, priors_list1, chains = 1, adapt = 100, burnin = 150, sample = 2000, seed = 1)) - # get marginal likelihoods - marglik0 <- JAGS_bridgesampling(fit0, log_posterior = log_posterior, data = data, prior_list = priors_list0) - marglik1 <- JAGS_bridgesampling(fit1, log_posterior = log_posterior, data = data, prior_list = priors_list1) - # automatically mix posteriors - models <- list( - list(fit = fit0, marglik = marglik0, prior_weights = 1), - list(fit = fit1, marglik = marglik1, prior_weights = 1) - ) - mixed_posteriors <- mix_posteriors(model_list = models, parameters = "omega", is_null_list = list("omega" = c(F,F)), seed = 1) + # Get the transformation functions + trans_funcs <- BayesTools:::.density.prior_transformation_functions("exp_lin") + # Verify the functions exist + expect_true(is.function(trans_funcs$fun)) + expect_true(is.function(trans_funcs$inv)) + expect_true(is.function(trans_funcs$jac)) + # Test the transformation: exp(a + b * log(x)) + x <- 2 + a <- 0.5 + b <- 1 - vdiffr::expect_doppelganger("model-averaging-plot-posterior-wf-1", function(){ - plot_posterior(mixed_posteriors, "omega", lwd = 2, col = "red", col.fill = scales::alpha("red", .20), par_name = "Selection Models", n_points = 50, ylim = c(0, 1)) - lines_prior_list(list(priors_list0$omega, priors_list1$omega), n_points = 50, n_samples = 1000, col = "blue", col.fill = scales::alpha("blue", .20)) - }) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-wf-2", { - plot_posterior(mixed_posteriors, "omega", plot_type = "ggplot", lwd = 2, col = "red", col.fill = scales::alpha("red", .20)) + geom_prior_list(list(priors_list0$omega, priors_list1$omega), n_points = 50, n_samples = 1000, col = "blue", col.fill = scales::alpha("blue", .20)) - }) + # fun: exp(0.5 + 1 * log(2)) = exp(0.5 + 0.693) = exp(1.193) โ‰ˆ 3.30 + expected <- exp(a + b * log(x)) + expect_equal(trans_funcs$fun(x, a, b), expected) - # rescale-x - vdiffr::expect_doppelganger("model-averaging-plot-posterior-wf-3", function(){ - plot_posterior(mixed_posteriors, "omega", lwd = 2, rescale_x = TRUE, col = "red", col.fill = scales::alpha("red", .20), par_name = "Selection Models", n_points = 50, ylim = c(0, 1)) - lines_prior_list(list(priors_list0$omega, priors_list1$omega), rescale_x = TRUE, n_points = 50, n_samples = 1000, col = "blue", col.fill = scales::alpha("blue", .20)) - }) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-wf-4", { - plot_posterior(mixed_posteriors, "omega", rescale_x = TRUE, plot_type = "ggplot", lwd = 2, col = "red", col.fill = scales::alpha("red", .20)) + geom_prior_list(list(priors_list0$omega, priors_list1$omega), rescale_x = TRUE, n_points = 50, n_samples = 1000, col = "blue", col.fill = scales::alpha("blue", .20)) - }) + # inv: should reverse the transformation + y <- trans_funcs$fun(x, a, b) + expect_equal(trans_funcs$inv(y, a, b), x, tolerance = 1e-10) - # prior and posterior - vdiffr::expect_doppelganger("model-averaging-plot-posterior-wf-6", function(){ - plot_posterior(mixed_posteriors, "omega", lwd = 2, col = "red", col.fill = scales::alpha("red", .20), prior = TRUE, n_points = 50, n_samples = 1000, dots_prior = list(col = "blue", col.fill = scales::alpha("blue", .20), lty = 2)) - }) + # jac: 1 / (b * x) + expect_equal(trans_funcs$jac(x, a, b), 1 / (b * x)) +}) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-wf-7", function(){ - plot_posterior(mixed_posteriors, "omega", plot_type = "ggplot", lwd = 2, col = "red", col.fill = scales::alpha("red", .20), n_points = 50, n_samples = 1000, prior = TRUE, dots_prior = list(col = "blue", col.fill = scales::alpha("blue", .20), lty = 2)) - }) +test_that("linear transformation matches expected behavior", { + set.seed(1) - # rescale-x - vdiffr::expect_doppelganger("model-averaging-plot-posterior-wf-8", function(){ - plot_posterior(mixed_posteriors, "omega", rescale_x = TRUE, lwd = 2, col = "red", col.fill = scales::alpha("red", .20), prior = TRUE, n_points = 50, n_samples = 1000, dots_prior = list(col = "blue", col.fill = scales::alpha("blue", .20), lty = 2)) - }) + # Create a normal prior + prior_list <- list(p1 = prior("normal", list(0, 1))) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-wf-9", function(){ - plot_posterior(mixed_posteriors, "omega", rescale_x = TRUE, plot_type = "ggplot", lwd = 2, col = "red", col.fill = scales::alpha("red", .20), n_points = 50, n_samples = 1000, prior = TRUE, dots_prior = list(col = "blue", col.fill = scales::alpha("blue", .20), lty = 2)) + # Apply linear transformation: a + b*x with a=0, b=0.5 + # This should compress the distribution by half + vdiffr::expect_doppelganger("plot-normal-lin-compress", function() { + plot_prior_list(prior_list, + transformation = "lin", + transformation_arguments = list(a = 0, b = 0.5)) }) - # add an overhelming missing model - priors_list2 <- list( - mu = prior("normal", list(0, .8), prior_weights = 4) - ) - # fit the models - fit2 <- suppressWarnings(JAGS_fit(model_syntax, data, priors_list2, chains = 1, adapt = 100, burnin = 150, sample = 2000, seed = 1)) - # get marginal likelihoods - marglik2 <- JAGS_bridgesampling(fit2, log_posterior = log_posterior, data = data, prior_list = priors_list2) - # automatically mix posteriors - models <- list( - list(fit = fit0, marglik = marglik0, prior_weights = 1), - list(fit = fit1, marglik = marglik1, prior_weights = 1), - list(fit = fit2, marglik = marglik2, prior_weights = 5) - ) - mixed_posteriors <- mix_posteriors(model_list = models, parameters = "omega", is_null_list = list("omega" = c(F,F,F)), seed = 1) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-wf-10", function(){ - plot_posterior(mixed_posteriors, "omega", lwd = 2, col = "red", col.fill = scales::alpha("red", .20), n_points = 50, n_samples = 1000, prior = TRUE, dots_prior = list(col = "blue", col.fill = scales::alpha("blue", .20), lty = 2)) + # Apply linear transformation with offset: a + b*x with a=2, b=0.5 + # This should compress and shift + vdiffr::expect_doppelganger("plot-normal-lin-shift-compress", function() { + plot_prior_list(prior_list, + transformation = "lin", + transformation_arguments = list(a = 2, b = 0.5)) }) - }) -test_that("posterior plot functions (orthonormal) work", { +# ============================================================================ # +# SECTION: transform_scaled visual tests +# ============================================================================ # +# These tests use pre-fitted regression models with formula_scale to visually +# verify that the transform_scaled feature correctly transforms priors and +# posteriors from standardized to original scale. + +test_that("transform_scaled is auto-detected from samples attribute", { + skip_if_no_fits() + + # Load a model with formula_scale + fit_path <- file.path(temp_fits_dir, "fit_formula_auto_scaled.RDS") + skip_if_not(file.exists(fit_path), "Pre-fitted model not available") + + fit <- readRDS(fit_path) + + # Extract with transform_scaled = TRUE + samples_scaled <- as_mixed_posteriors(fit, parameters = "mu_intercept", transform_scaled = TRUE) + + # Verify the attribute is set - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes + expect_true(isTRUE(attr(samples_scaled, "transform_scaled"))) + expect_false(is.null(attr(samples_scaled, "prior_samples"))) + + # Extract without transform_scaled + samples_unscaled <- as_mixed_posteriors(fit, parameters = "mu_intercept", transform_scaled = FALSE) + + # Verify the attribute is NOT set + expect_null(attr(samples_unscaled, "transform_scaled")) +}) + + +test_that("transform_scaled visual: auto-scaled continuous predictors intercept", { skip_on_cran() + skip_if_no_fits() - set.seed(1) + fit_path <- file.path(temp_fits_dir, "fit_formula_auto_scaled.RDS") + skip_if_not(file.exists(fit_path), "Pre-fitted model not available") - data_formula <- data.frame( - x_fac3o = factor(rep(c("A", "B", "C"), 40), levels = c("A", "B", "C")) - ) - data <- list( - y = rnorm(120, .4 + ifelse(data_formula$x_fac3o == "A", 0.0, ifelse(data_formula$x_fac3o == "B", -0.5, 0.5)), 1), - N = 120 - ) + fit <- readRDS(fit_path) - # create model with mix of a formula and free parameters --- - formula_list0 <- list(mu = ~ 1) - formula_list1 <- list(mu = ~ x_fac3o) + # Extract posteriors with and without transform_scaled + samples_scaled <- as_mixed_posteriors(fit, parameters = c("mu_intercept", "mu_x_cont1", "mu_x_cont2"), + transform_scaled = TRUE) + samples_unscaled <- as_mixed_posteriors(fit, parameters = c("mu_intercept", "mu_x_cont1", "mu_x_cont2"), + transform_scaled = FALSE) - formula_prior_list0 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)) - ) - ) - formula_prior_list1 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_fac3o" = prior_factor("mnormal", contrast = "orthonormal", list(0, 0.5)) - ) - ) + # Visual test: intercept - scaled (left) vs original (right) + vdiffr::expect_doppelganger("transform-scaled-intercept-comparison", function() { + par(mfrow = c(1, 2)) - prior_list <- list(sigma = prior("lognormal", list(0, 1))) - formula_data_list <- list(mu = data_formula) + # Left: Standardized scale + plot_posterior(samples_unscaled, "mu_intercept", prior = TRUE, + main = "Intercept (Standardized Scale)", dots_prior = list(col = "grey")) - model_syntax <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) + # Right: Original scale (auto-detected from samples) + plot_posterior(samples_scaled, "mu_intercept", prior = TRUE, + main = "Intercept (Original Scale)", dots_prior = list(col = "grey")) + }) +}) - log_posterior <- function(parameters, data){ - sum(stats::dnorm(data$y, parameters[["mu"]], parameters[["sigma"]], log = TRUE)) - } - - fit0 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list0, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0, seed = 1) - fit1 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list1, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1, seed = 2) - - marglik0 <- JAGS_bridgesampling( - fit0, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list0, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0) - marglik1 <- JAGS_bridgesampling( - fit1, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list1, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1) - - # mix posteriors - models <- list( - list(fit = fit0, marglik = marglik0, prior_weights = 1), - list(fit = fit1, marglik = marglik1, prior_weights = 1) - ) - mixed_posteriors <- mix_posteriors( - model_list = models, - parameters = c("mu_intercept", "mu_x_fac3o"), - is_null_list = list( - "mu_intercept" = c(TRUE, TRUE), - "mu_x_fac3o" = c(FALSE, TRUE) - ), - seed = 1, n_samples = 10000) - - vdiffr::expect_doppelganger("model-averaging-plot-posterior-o-1", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_fac3o") - }) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-o-2", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_fac3o", col = c("red", "green", "blue")) - }) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-o-3", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_fac3o", lty = c(2, 3, 4), col = "blue", lwd = 2) - }) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-o-4", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_fac3o", legend = FALSE) - }) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-o-5", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_fac3o", col = c("red", "green", "blue"), dots_prior = list(col = "grey"), prior = TRUE) - }) - - vdiffr::expect_doppelganger("model-averaging-ggplot-posterior-o-1", plot_posterior(mixed_posteriors, "mu_x_fac3o", plot_type = "ggplot")) - vdiffr::expect_doppelganger("model-averaging-ggplot-posterior-o-2", plot_posterior(mixed_posteriors, "mu_x_fac3o", col = c("red", "green", "blue"), plot_type = "ggplot")) - vdiffr::expect_doppelganger("model-averaging-ggplot-posterior-o-3", plot_posterior(mixed_posteriors, "mu_x_fac3o", lty = c(2, 3, 4), col = "blue", lwd = 2, plot_type = "ggplot")) - vdiffr::expect_doppelganger("model-averaging-ggplot-posterior-o-4", plot_posterior(mixed_posteriors, "mu_x_fac3o", legend = FALSE, plot_type = "ggplot")) - vdiffr::expect_doppelganger("model-averaging-ggplot-posterior-o-5", plot_posterior(mixed_posteriors, "mu_x_fac3o", col = c("red", "green", "blue"), prior = TRUE, plot_type = "ggplot")) - -}) - - -test_that("posterior plot functions (treatment) work", { - - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes - skip_on_cran() - - set.seed(1) - - data_formula <- data.frame( - x_fac2t = factor(rep(c("A", "B"), 60), levels = c("A", "B")) - ) - data <- list( - y = rnorm(120, .4 + ifelse(data_formula$x_fac2t == "A", 0.0, 0.5), 1), - N = 120 - ) - - # create model with mix of a formula and free parameters --- - formula_list0 <- list(mu = ~ 1) - formula_list1 <- list(mu = ~ x_fac2t) - - formula_prior_list0 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)) - ) - ) - formula_prior_list1 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_fac2t" = prior_factor("normal", contrast = "treatment", list(0, 0.5)) - ) - ) - - prior_list <- list(sigma = prior("lognormal", list(0, 1))) - formula_data_list <- list(mu = data_formula) - - model_syntax <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) - - log_posterior <- function(parameters, data){ - sum(stats::dnorm(data$y, parameters[["mu"]], parameters[["sigma"]], log = TRUE)) - } - - fit0 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list0, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0, seed = 1) - fit1 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list1, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1, seed = 2) - - marglik0 <- JAGS_bridgesampling( - fit0, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list0, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0) - marglik1 <- JAGS_bridgesampling( - fit1, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list1, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1) - - # mix posteriors - models <- list( - list(fit = fit0, marglik = marglik0, prior_weights = 1), - list(fit = fit1, marglik = marglik1, prior_weights = 1) - ) - - mixed_posteriors <- mix_posteriors( - model_list = models, - parameters = c("mu_intercept", "mu_x_fac2t"), - is_null_list = list( - "mu_intercept" = c(TRUE, TRUE), - "mu_x_fac2t" = c(FALSE, TRUE) - ), - seed = 1, n_samples = 10000) - - vdiffr::expect_doppelganger("model-averaging-plot-posterior-t-1", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_fac2t") - }) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-t-2", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_fac2t", col = "red") - }) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-t-3", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_fac2t", lty = 2, col = "blue", lwd = 2) - }) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-t-4", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_fac2t", legend = FALSE) - }) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-t-5", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_fac2t", col = "red", prior = TRUE) - }) - - vdiffr::expect_doppelganger("model-averaging-ggplot-posterior-t-1", plot_posterior(mixed_posteriors, "mu_x_fac2t", plot_type = "ggplot")) - vdiffr::expect_doppelganger("model-averaging-ggplot-posterior-t-2", plot_posterior(mixed_posteriors, "mu_x_fac2t", col = "red", plot_type = "ggplot")) - vdiffr::expect_doppelganger("model-averaging-ggplot-posterior-t-3", plot_posterior(mixed_posteriors, "mu_x_fac2t", lty = 2, col = "blue", lwd = 2, plot_type = "ggplot")) - vdiffr::expect_doppelganger("model-averaging-ggplot-posterior-t-4", plot_posterior(mixed_posteriors, "mu_x_fac2t", legend = FALSE, plot_type = "ggplot")) - vdiffr::expect_doppelganger("model-averaging-ggplot-posterior-t-5", plot_posterior(mixed_posteriors, "mu_x_fac2t", col = "red", prior = TRUE, plot_type = "ggplot")) - -}) - - -test_that("posterior plot functions (independent) work", { - - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes - skip_on_cran() - - set.seed(1) - - data_formula <- data.frame( - x_fac2i = factor(rep(c("A", "B"), 150), levels = c("A", "B")) - ) - data <- list( - y = rnorm(300, ifelse(data_formula$x_fac2i == "A", 0.0, 0.5), 1), - N = 300 - ) - - # create model with mix of a formula and free parameters --- - formula_list0 <- list(mu = ~ x_fac2i - 1) - formula_list1 <- list(mu = ~ x_fac2i - 1) - - formula_prior_list0 <- list( - mu = list( - "x_fac2i" = prior_factor("spike", contrast = "independent", list(0)) - ) - ) - formula_prior_list1 <- list( - mu = list( - "x_fac2i" = prior_factor("normal", contrast = "independent", list(0, 1/4)) - ) - ) - - prior_list <- list(sigma = prior("lognormal", list(0, 1))) - formula_data_list <- list(mu = data_formula) - - model_syntax <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) - - log_posterior <- function(parameters, data){ - sum(stats::dnorm(data$y, parameters[["mu"]], parameters[["sigma"]], log = TRUE)) - } - - fit0 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list0, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0, seed = 1) - fit1 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list1, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1, seed = 2) - - marglik0 <- JAGS_bridgesampling( - fit0, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list0, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0) - marglik1 <- JAGS_bridgesampling( - fit1, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list1, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1) - - # mix posteriors - models <- list( - list(fit = fit0, marglik = marglik0, prior_weights = 1), - list(fit = fit1, marglik = marglik1, prior_weights = 1) - ) - - mixed_posteriors <- mix_posteriors( - model_list = models, - parameters = c("mu_x_fac2i"), - is_null_list = list( - "mu_x_fac2i" = c(FALSE, TRUE) - ), - seed = 1, n_samples = 10000) - - vdiffr::expect_doppelganger("model-averaging-plot-posterior-i-1", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_fac2i") - }) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-i-2", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_fac2i", col = c("green", "yellow")) - }) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-i-3", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_fac2i", col = "red", prior = TRUE) - }) - - vdiffr::expect_doppelganger("model-averaging-ggplot-posterior-i-1", plot_posterior(mixed_posteriors, "mu_x_fac2i", plot_type = "ggplot")) - vdiffr::expect_doppelganger("model-averaging-ggplot-posterior-i-2", plot_posterior(mixed_posteriors, "mu_x_fac2i", prior = TRUE, plot_type = "ggplot")) - -}) - - -test_that("posterior plot functions (meandif) work", { - - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes +test_that("transform_scaled visual: auto-scaled continuous predictor coefficient", { skip_on_cran() + skip_if_no_fits() - set.seed(1) - - data_formula <- data.frame( - x_fac3md = factor(rep(c("A", "B", "C"), 40), levels = c("A", "B", "C")) - ) - data <- list( - y = rnorm(120, .4 + ifelse(data_formula$x_fac3md == "A", 0.0, ifelse(data_formula$x_fac3md == "B", -0.5, 0.5)), 1), - N = 120 - ) - - # create model with mix of a formula and free parameters --- - formula_list0 <- list(mu = ~ 1) - formula_list1 <- list(mu = ~ x_fac3md) + fit_path <- file.path(temp_fits_dir, "fit_formula_auto_scaled.RDS") + skip_if_not(file.exists(fit_path), "Pre-fitted model not available") - formula_prior_list0 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)) - ) - ) - formula_prior_list1 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_fac3md" = prior_factor("mnormal", contrast = "meandif", list(0, 0.5)) - ) - ) + fit <- readRDS(fit_path) - prior_list <- list(sigma = prior("lognormal", list(0, 1))) - formula_data_list <- list(mu = data_formula) + samples_scaled <- as_mixed_posteriors(fit, parameters = c("mu_intercept", "mu_x_cont1", "mu_x_cont2"), + transform_scaled = TRUE, n_prior_samples = 1e5) + samples_unscaled <- as_mixed_posteriors(fit, parameters = c("mu_intercept", "mu_x_cont1", "mu_x_cont2"), + transform_scaled = FALSE) - model_syntax <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) + # Visual test: coefficient x_cont1 - scaled (left) vs original (right) + vdiffr::expect_doppelganger("transform-scaled-coef-x_cont1-comparison", function() { + par(mfrow = c(1, 2)) - log_posterior <- function(parameters, data){ - sum(stats::dnorm(data$y, parameters[["mu"]], parameters[["sigma"]], log = TRUE)) - } - - fit0 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list0, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0, seed = 1) - fit1 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list1, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1, seed = 2) - - marglik0 <- JAGS_bridgesampling( - fit0, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list0, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0) - marglik1 <- JAGS_bridgesampling( - fit1, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list1, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1) - - # mix posteriors - models <- list( - list(fit = fit0, marglik = marglik0, prior_weights = 1), - list(fit = fit1, marglik = marglik1, prior_weights = 1) - ) + # Left: Standardized scale + plot_posterior(samples_unscaled, "mu_x_cont1", prior = TRUE, + main = "x_cont1 (Standardized Scale)", dots_prior = list(col = "grey")) - mixed_posteriors <- mix_posteriors( - model_list = models, - parameters = c("mu_intercept", "mu_x_fac3md"), - is_null_list = list( - "mu_intercept" = c(TRUE, TRUE), - "mu_x_fac3md" = c(FALSE, TRUE) - ), - seed = 1, n_samples = 10000) - - vdiffr::expect_doppelganger("model-averaging-plot-posterior-md-1", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_fac3md") - }) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-md-2", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_fac3md", col = c("red", "green", "blue")) - }) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-md-3", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_fac3md", lty = c(2, 3, 4), col = "blue", lwd = 2) + # Right: Original scale (auto-detected from samples) + plot_posterior(samples_scaled, "mu_x_cont1", prior = TRUE, + main = "x_cont1 (Original Scale)", dots_prior = list(col = "grey")) }) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-md-4", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_fac3md", legend = FALSE) - }) - vdiffr::expect_doppelganger("model-averaging-plot-posterior-md-5", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_fac3md", col = c("red", "green", "blue"), dots_prior = list(col = "grey"), prior = TRUE) - }) - - vdiffr::expect_doppelganger("model-averaging-ggplot-posterior-md-1", plot_posterior(mixed_posteriors, "mu_x_fac3md", plot_type = "ggplot")) - vdiffr::expect_doppelganger("model-averaging-ggplot-posterior-md-2", plot_posterior(mixed_posteriors, "mu_x_fac3md", col = c("red", "green", "blue"), plot_type = "ggplot")) - vdiffr::expect_doppelganger("model-averaging-ggplot-posterior-md-3", plot_posterior(mixed_posteriors, "mu_x_fac3md", lty = c(2, 3, 4), col = "blue", lwd = 2, plot_type = "ggplot")) - vdiffr::expect_doppelganger("model-averaging-ggplot-posterior-md-4", plot_posterior(mixed_posteriors, "mu_x_fac3md", legend = FALSE, plot_type = "ggplot")) - vdiffr::expect_doppelganger("model-averaging-ggplot-posterior-md-5", plot_posterior(mixed_posteriors, "mu_x_fac3md", col = c("red", "green", "blue"), prior = TRUE, plot_type = "ggplot")) -}) + # Visual test: coefficient x_cont2 + vdiffr::expect_doppelganger("transform-scaled-coef-x_cont2-comparison", function() { + par(mfrow = c(1, 2)) + plot_posterior(samples_unscaled, "mu_x_cont2", prior = TRUE, + main = "x_cont2 (Standardized Scale)", dots_prior = list(col = "grey")) -test_that("models plot functions work", { - - set.seed(1) - ### prior distribution related functions - p0 <- prior(distribution = "point", parameters = list(location = 0)) - p1 <- prior(distribution = "normal", parameters = list(mean = 0, sd = 1)) - p2 <- prior(distribution = "normal", parameters = list(mean = 0, sd = 1), truncation = list(0, Inf)) - - data <- list( - x = rnorm(10), - N = 10 - ) - - ## create and fit models - priors_list0 <- list(mu = p0) - priors_list1 <- list(mu = p1) - priors_list2 <- list(tau = p2) - - # define likelihood for the data - model_syntax <- - "model{ - for(i in 1:N){ - x[i] ~ dnorm(mu, 1) - } - }" - model_syntax2 <- - "model{ - for(i in 1:N){ - x[i] ~ dnorm(0, pow(tau, -2)) - } - }" - - # define log posterior for bridge sampling - log_posterior <- function(parameters, data){ - sum(dnorm(data$x, parameters$mu, 1, log = TRUE)) - } - log_posterior2 <- function(parameters, data){ - sum(dnorm(data$x, 0, parameters$tau, log = TRUE)) - } - # fit the models - fit0 <- JAGS_fit(model_syntax, data, priors_list0, chains = 1, adapt = 100, burnin = 200, sample = 1000, seed = 0) - fit1 <- JAGS_fit(model_syntax, data, priors_list1, chains = 1, adapt = 100, burnin = 200, sample = 1000, seed = 1) - fit2 <- JAGS_fit(model_syntax2, data, priors_list2, chains = 1, adapt = 100, burnin = 200, sample = 1000, seed = 2) - # get marginal likelihoods - marglik0 <- list( - logml = sum(dnorm(data$x, mean(p0), 1, log = TRUE)) - ) - class(marglik0) <- "bridge" - marglik1 <- JAGS_bridgesampling(fit1, log_posterior = log_posterior, data = data, prior_list = priors_list1) - marglik2 <- JAGS_bridgesampling(fit2, log_posterior = log_posterior2, data = data, prior_list = priors_list2) - ## create an object with the models - models <- list( - list(fit = fit0, marglik = marglik0, prior_weights = 1, fit_summary = runjags_estimates_table(fit0)), - list(fit = fit1, marglik = marglik1, prior_weights = 1, fit_summary = runjags_estimates_table(fit1)), - list(fit = fit2, marglik = marglik2, prior_weights = 1, fit_summary = runjags_estimates_table(fit2)) - ) - # compare and summarize the models - models <- models_inference(models) - inference <- ensemble_inference(model_list = models, parameters = c("mu", "tau"), is_null_list = list("mu" = c(1, 3), "tau" = c(1, 2))) - mixed_posteriors <- mix_posteriors(model_list = models, parameters = c("mu", "tau"), is_null_list = list("mu" = c(1, 3), "tau" = c(1, 2)), seed = 1) - - - vdiffr::expect_doppelganger("model-averaging-plot-models-1", function(){ - plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "mu") - }) - vdiffr::expect_doppelganger("model-averaging-plot-models-2", function(){ - plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "tau") - }) - vdiffr::expect_doppelganger("model-averaging-plot-models-3", function(){ - plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "mu", prior = TRUE) - }) - vdiffr::expect_doppelganger("model-averaging-plot-models-4", function(){ - plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "tau", prior = TRUE) - }) - vdiffr::expect_doppelganger("model-averaging-plot-models-5", function(){ - plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "mu", conditional = TRUE) - }) - vdiffr::expect_doppelganger("model-averaging-plot-models-6", function(){ - plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "tau", prior = TRUE, conditional = TRUE) + plot_posterior(samples_scaled, "mu_x_cont2", prior = TRUE, + main = "x_cont2 (Original Scale)", dots_prior = list(col = "grey")) }) - vdiffr::expect_doppelganger("model-averaging-plot-models-7", { - plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "mu", plot_type = "ggplot") - }) - vdiffr::expect_doppelganger("model-averaging-plot-models-8", { - plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "tau", prior = TRUE, plot_type = "ggplot") - }) - vdiffr::expect_doppelganger("model-averaging-plot-models-9", { - plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "mu", plot_type = "ggplot", y_axis2 = FALSE) - }) - vdiffr::expect_doppelganger("model-averaging-plot-models-10", { - plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "mu", plot_type = "ggplot", show_estimates = FALSE) - }) - }) -test_that("models plot functions work (formulas + factors)", { - - - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes +test_that("transform_scaled visual: all parameters side-by-side", { skip_on_cran() + skip_if_no_fits() - set.seed(1) - - data_formula <- data.frame( - x_cont1 = rnorm(60), - x_fac2t = factor(rep(c("A", "B"), 30), levels = c("A", "B")), - x_fac3o = factor(rep(c("A", "B", "C"), 20), levels = c("A", "B", "C")), - x_fac3t = factor(rep(c("A", "B", "C"), 20), levels = c("A", "B", "C")) - ) - data <- list( - y = rnorm(60, .4 * data_formula$x_cont1 + ifelse(data_formula$x_fac3t == "A", 0.0, ifelse(data_formula$x_fac3t == "B", -0.2, 0.4)), 1), - N = 60 - ) - - # create model with mix of a formula and free parameters --- - formula_list0 <- list(mu = ~ x_fac2t) - formula_list1 <- list(mu = ~ x_cont1 + x_fac3t) - formula_list2 <- list(mu = ~ x_fac3o) - formula_list3 <- list(mu = ~ x_cont1 * x_fac3o) - - formula_prior_list0 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_fac2t" = prior_factor("normal", contrast = "treatment", list(0, 1)) - ) - ) - formula_prior_list1 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior("normal", list(0, 1)), - "x_fac3t" = prior_factor("normal", contrast = "treatment", list(0, 1)) - ) - ) - formula_prior_list2 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_fac3o" = prior_factor("mnormal", contrast = "orthonormal", list(0, 1)) - ) - ) - formula_prior_list3 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior("normal", list(0, 1)), - "x_fac3o" = prior_factor("mnormal", contrast = "orthonormal", list(0, 1)), - "x_cont1:x_fac3o" = prior_factor("mnormal", contrast = "orthonormal", list(0, 1)) - ) - ) - - prior_list <- list(sigma = prior("lognormal", list(0, 1))) - formula_data_list <- list(mu = data_formula) + fit_path <- file.path(temp_fits_dir, "fit_formula_auto_scaled.RDS") + skip_if_not(file.exists(fit_path), "Pre-fitted model not available") - model_syntax <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) + fit <- readRDS(fit_path) - log_posterior <- function(parameters, data){ - sum(stats::dnorm(data$y, parameters[["mu"]], parameters[["sigma"]], log = TRUE)) - } - - fit0 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list0, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0, seed = 1) - fit1 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list1, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1, seed = 2) - fit2 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list2, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list2, seed = 3) - fit3 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list3, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list3, seed = 4) - - marglik0 <- JAGS_bridgesampling( - fit0, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list0, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0) - marglik1 <- JAGS_bridgesampling( - fit1, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list1, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1) - marglik2 <- JAGS_bridgesampling( - fit2, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list2, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list2) - marglik3 <- JAGS_bridgesampling( - fit3, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list3, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list3) - - - # mix posteriors - models <- list( - list(fit = fit0, marglik = marglik0, fit_summary = runjags_estimates_table(fit0), prior_weights = 1), - list(fit = fit1, marglik = marglik1, fit_summary = runjags_estimates_table(fit1), prior_weights = 1), - list(fit = fit2, marglik = marglik2, fit_summary = runjags_estimates_table(fit2), prior_weights = 1), - list(fit = fit3, marglik = marglik3, fit_summary = runjags_estimates_table(fit3), prior_weights = 1) - ) - models <- models_inference(models) + samples_scaled <- as_mixed_posteriors(fit, parameters = c("mu_intercept", "mu_x_cont1", "mu_x_cont2"), + transform_scaled = TRUE) + samples_unscaled <- as_mixed_posteriors(fit, parameters = c("mu_intercept", "mu_x_cont1", "mu_x_cont2"), + transform_scaled = FALSE) + # Visual test: 3x2 grid showing all parameters + vdiffr::expect_doppelganger("transform-scaled-all-params-grid", function() { + par(mfrow = c(3, 2), mar = c(4, 4, 2, 1)) - inference <- ensemble_inference( - model_list = models, - parameters = c("mu_x_cont1", "mu_x_fac2t", "mu_x_fac3t", "mu_x_fac3o", "mu_x_cont1__xXx__x_fac3o"), - is_null_list = list( - "mu_x_cont1" = c(TRUE, FALSE, TRUE, FALSE), - "mu_x_fac2t" = c(FALSE, TRUE, TRUE, TRUE), - "mu_x_fac3t" = c(TRUE, FALSE, TRUE, TRUE), - "mu_x_fac3o" = c(TRUE, TRUE, FALSE, FALSE), - "mu_x_cont1__xXx__x_fac3o" = c(TRUE, TRUE, TRUE, FALSE) - ), - conditional = FALSE) + # Row 1: Intercept + plot_posterior(samples_unscaled, "mu_intercept", prior = TRUE, main = "Intercept (Scaled)", dots_prior = list(col = "grey")) + plot_posterior(samples_scaled, "mu_intercept", prior = TRUE, main = "Intercept (Original)", dots_prior = list(col = "grey")) - mixed_posteriors <- mix_posteriors( - model_list = models, - parameters = c("mu_x_cont1", "mu_x_fac2t", "mu_x_fac3t", "mu_x_fac3o", "mu_x_cont1__xXx__x_fac3o"), - is_null_list = list( - "mu_x_cont1" = c(TRUE, FALSE, TRUE, FALSE), - "mu_x_fac2t" = c(FALSE, TRUE, TRUE, TRUE), - "mu_x_fac3t" = c(TRUE, FALSE, TRUE, TRUE), - "mu_x_fac2t" = c(TRUE, TRUE, FALSE, FALSE), - "mu_x_cont1__xXx__x_fac3o" = c(TRUE, TRUE, TRUE, FALSE) - ), - seed = 1, n_samples = 10000) + # Row 2: x_cont1 + plot_posterior(samples_unscaled, "mu_x_cont1", prior = TRUE, main = "x_cont1 (Scaled)", dots_prior = list(col = "grey")) + plot_posterior(samples_scaled, "mu_x_cont1", prior = TRUE, main = "x_cont1 (Original)", dots_prior = list(col = "grey")) - - - vdiffr::expect_doppelganger("model-averaging-plot-models-formula-1", function(){ - plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "mu_x_cont1") - }) - vdiffr::expect_doppelganger("model-averaging-plot-models-formula-2", function(){ - plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "mu_x_fac2t") - }) - vdiffr::expect_doppelganger("model-averaging-plot-models-formula-3", function(){ - plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "mu_x_fac2t", prior = TRUE) - }) - vdiffr::expect_doppelganger("model-averaging-plot-models-formula-4", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(2, 1)) - plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "mu_x_fac3t") - }) - vdiffr::expect_doppelganger("model-averaging-plot-models-formula-5", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(2, 1)) - plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "mu_x_fac3t", prior = TRUE) - }) - vdiffr::expect_doppelganger("model-averaging-plot-models-formula-6", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(3, 1)) - plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "mu_x_fac3o") - }) - vdiffr::expect_doppelganger("model-averaging-plot-models-formula-7", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(3, 1)) - plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "mu_x_fac3o", prior = TRUE) - }) - vdiffr::expect_doppelganger("model-averaging-plot-models-formula-8", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(3, 1)) - plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "mu_x_cont1__xXx__x_fac3o") + # Row 3: x_cont2 + plot_posterior(samples_unscaled, "mu_x_cont2", prior = TRUE, main = "x_cont2 (Scaled)", dots_prior = list(col = "grey")) + plot_posterior(samples_scaled, "mu_x_cont2", prior = TRUE, main = "x_cont2 (Original)", dots_prior = list(col = "grey")) }) - vdiffr::expect_doppelganger("model-averaging-plot-models-formula-9", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(3, 1)) - plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "mu_x_cont1__xXx__x_fac3o", prior = TRUE) - }) - - # ggplot versions - p1 <- plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "mu_x_cont1", plot_type = "ggplot") - p2 <- plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "mu_x_fac3o", prior = TRUE, plot_type = "ggplot") - - vdiffr::expect_doppelganger("model-averaging-plot-models-formula-10", p1) - vdiffr::expect_doppelganger("model-averaging-plot-models-formula-11", p2[[1]]) - vdiffr::expect_doppelganger("model-averaging-plot-models-formula-12", p2[[2]]) - vdiffr::expect_doppelganger("model-averaging-plot-models-formula-13", p2[[3]]) - }) -test_that("models plot functions work (formulas + spike factors)", { - - - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes +test_that("transform_scaled visual: dual parameter regression with log(intercept)", { skip_on_cran() + skip_if_no_fits() - set.seed(1) - - data_formula <- data.frame( - x_fac3md = factor(rep(c("A", "B", "C"), 100), levels = c("A", "B", "C")) - ) - data <- list( - y = rnorm(300, ifelse(data_formula$x_fac3md == "A", 0.0, ifelse(data_formula$x_fac3md == "B", -0.2, 0.4))), - N = 300 - ) - + fit_path <- file.path(temp_fits_dir, "fit_dual_param_regression.RDS") + fit <- readRDS(fit_path) - formula_list <- list( - mu = ~ x_fac3md - ) - formula_data_list <- list( - mu = data_formula - ) - formula_prior_list0 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_fac3md" = prior_factor("spike", contrast = "meandif", list(0)) - ) - ) - formula_prior_list1 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_fac3md" = prior_factor("mnormal", contrast = "meandif", list(0, 0.25)) - ) - ) + # Get available mu parameters (those with formula_scale applied) + params <- names(attr(fit, "prior_list")) + sigma_params <- params[grepl("^log_sigma_", params)] - prior_list <- list( - sigma = prior("lognormal", list(0, 1)) - ) - model_syntax <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) + samples_scaled <- as_mixed_posteriors(fit, parameters = sigma_params, transform_scaled = TRUE) + samples_unscaled <- as_mixed_posteriors(fit, parameters = sigma_params, transform_scaled = FALSE) - fit0 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0) - fit1 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1) - - log_posterior <- function(parameters, data){ - return(sum(stats::dnorm(data$y, mean = parameters[["mu"]], sd = parameters[["sigma"]], log = TRUE))) - } - - marglik0 <- JAGS_bridgesampling( - fit = fit0, - log_posterior = log_posterior, - data = data, - prior_list = prior_list, - formula_list = formula_list, - formula_data_list = formula_data_list, - formula_prior_list = formula_prior_list0) - marglik1 <- JAGS_bridgesampling( - fit = fit1, - log_posterior = log_posterior, - data = data, - prior_list = prior_list, - formula_list = formula_list, - formula_data_list = formula_data_list, - formula_prior_list = formula_prior_list1) - - - # mix posteriors - models <- list( - list(fit = fit0, marglik = marglik0, fit_summary = runjags_estimates_table(fit0, remove_spike_0 = FALSE, transform_factors = TRUE), prior_weights = 1), - list(fit = fit1, marglik = marglik1, fit_summary = runjags_estimates_table(fit1, remove_spike_0 = FALSE, transform_factors = TRUE), prior_weights = 1) - ) - models <- models_inference(models) - - inference <- ensemble_inference( - model_list = models, - parameters = c("mu_x_fac3md"), - is_null_list = list( - "mu_x_fac3md" = c(TRUE, FALSE) - ), - conditional = FALSE) - - mixed_posteriors <- mix_posteriors( - model_list = models, - parameters = c("mu_x_fac3md"), - is_null_list = list( - "mu_x_fac3md" = c(TRUE, FALSE) - ), - seed = 1, n_samples = 10000) - - - - vdiffr::expect_doppelganger("model-averaging-plot-models-formula-s-1", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(3, 1)) - plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "mu_x_fac3md") - }) - vdiffr::expect_doppelganger("model-averaging-plot-models-formula-s-2", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfrow = oldpar[["mfrow"]])) - par(mfrow = c(3, 1)) - plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "mu_x_fac3md", prior = TRUE) + # Visual test: intercept for dual-parameter model + vdiffr::expect_doppelganger("transform-scaled-dual-param-intercept", function() { + par(mfrow = c(2, 2)) + plot_posterior(samples_unscaled, "log_sigma_intercept", prior = TRUE, main = "Dual: Intercept (Scaled)", dots_prior = list(col = "grey"), xlim = c(0, 1)) + plot_posterior(samples_scaled, "log_sigma_intercept", prior = TRUE, main = "Dual: Intercept (Original)", dots_prior = list(col = "grey"), xlim = c(0, 1)) + plot_posterior(samples_unscaled, "log_sigma_x_sigma", prior = TRUE, main = "Dual: Slope (Scaled)", dots_prior = list(col = "grey"), xlim = c(-1, 1)) + plot_posterior(samples_scaled, "log_sigma_x_sigma", prior = TRUE, main = "Dual: Slope (Original)", dots_prior = list(col = "grey"), xlim = c(-1, 1)) }) - }) - -test_that("posterior plot model averaging based on simple single JAGS models (formulas)", { - - set.seed(1) - - data_formula <- data.frame( - x_cont1 = rnorm(300), - x_fac2t = factor(rep(c("A", "B"), 150), levels = c("A", "B")), - x_fac3t = factor(rep(c("A", "B", "C"), 100), levels = c("A", "B", "C")) - ) - data <- list( - y = rnorm(300, -0.15 + 0.20 * data_formula$x_cont1 + ifelse(data_formula$x_fac3t == "A", 0.0, ifelse(data_formula$x_fac3t == "B", -0.2, 0.2)), ifelse(data_formula$x_fac2t == "A", 0.5, 1)), - N = 300 - ) - - # create model with mix of a formula and free parameters --- - formula_list1 <- list( - mu = ~ x_cont1 + x_fac2t + x_fac3t - ) - formula_data_list1 <- list( - mu = data_formula - ) - formula_prior_list1 <- list( - mu = list( - "intercept" = prior("normal", list(-1, 0.5), prior_weights = 1), - "x_cont1" = prior("normal", list(0, 1), prior_weights = 1), - "x_fac2t" = prior_factor("mnormal", list(0, 1), contrast = "orthonormal"), - "x_fac3t" = prior_factor("mnormal", list(0, 1), contrast = "meandif") - ) - ) - - attr(formula_prior_list1$mu$x_cont1, "multiply_by") <- "sigma" - prior_list1 <- list( - "sigma" = prior("lognormal", list(0, 1)) - ) - model_syntax1 <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) - - - fit1 <- JAGS_fit( - model_syntax = model_syntax1, data = data, prior_list = prior_list1, - formula_list = formula_list1, formula_data_list = formula_data_list1, formula_prior_list = formula_prior_list1) - # BayesTools::JAGS_estimates_table(fit1) - - mixed_posteriors <- as_mixed_posteriors( - mode = fit1, - parameters = names(attr(fit1, "prior_list")) - ) - - vdiffr::expect_doppelganger("model-averaging-simple-plot-ss-posterior-intercept", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_intercept", prior = T, dots_prior = list(col = "grey")) - }) - - vdiffr::expect_doppelganger("model-averaging-simple-plot-ss-posterior-x_cont1", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_cont1", prior = T, dots_prior = list(col = "grey")) - }) - - vdiffr::expect_doppelganger("model-averaging-simple-plot-ss-posterior-x_fac2t", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_fac2t", prior = T, dots_prior = list(col = "grey")) - }) - - vdiffr::expect_doppelganger("model-averaging-simple-plot-ss-posterior-x_fac3t", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_fac3t", prior = T, dots_prior = list(col = "grey")) - }) - - vdiffr::expect_doppelganger("model-averaging-simple-plot-ss-posterior-sigma", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "sigma", prior = T, dots_prior = list(col = "grey")) - }) -}) - -test_that("posterior plot model averaging based on complex single JAGS models (formulas + spike factors + mixture)", { - - set.seed(1) - - data_formula <- data.frame( - x_cont1 = rnorm(300), - x_fac2t = factor(rep(c("A", "B"), 150), levels = c("A", "B")), - x_fac3t = factor(rep(c("A", "B", "C"), 100), levels = c("A", "B", "C")) - ) - data <- list( - y = rnorm(300, -0.15 + 0.20 * data_formula$x_cont1 + ifelse(data_formula$x_fac3t == "A", 0.0, ifelse(data_formula$x_fac3t == "B", -0.2, 0.2)), ifelse(data_formula$x_fac2t == "A", 0.5, 1)), - N = 300 - ) - - # create model with mix of a formula and free parameters --- - formula_list1 <- list( - mu = ~ x_cont1 + x_fac2t + x_fac3t - ) - formula_data_list1 <- list( - mu = data_formula - ) - formula_prior_list1 <- list( - mu = list( - "intercept" = prior_mixture( - list( - prior("spike", list(0), prior_weights = 2), - prior("normal", list(-1, 0.5), prior_weights = 1), - prior("normal", list( 1, 0.5), prior_weights = 1) - ), - is_null = c(T, F, F) - ), - "x_cont1" = prior_spike_and_slab(prior("normal", list(0, 1), prior_weights = 1)), - "x_fac2t" = prior_mixture(list( - prior("spike", list(0), prior_weights = 1), - prior_factor("mnormal", list(0, 1), contrast = "orthonormal") - ), - is_null = c(T, F) - ), - "x_fac3t" = prior_mixture(list( - prior("spike", list(0), prior_weights = 1), - prior_factor("mnormal", list(0, 1), contrast = "orthonormal") - ), - is_null = c(T, F) - ) - ) - ) - - attr(formula_prior_list1$mu$x_cont1, "multiply_by") <- "sigma" - prior_list1 <- list( - "sigma" = prior_mixture( - list( - prior("normal", list(0, 1), truncation = list(0, Inf)), - prior("lognormal", list(0, 1)) - ), - components = c("normal", "lognormal") - ), - "bias" = prior_mixture(list( - prior_none(prior_weights = 1), - prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/3), - prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/3), - prior_PET("normal", list(0, 1), prior_weights = 1/3) - ), is_null = c(T, F, F, F)) - ) - model_syntax1 <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) - - if("RoBMA" %in% rownames(installed.packages())){ - require("RoBMA") - }else{ - skip() - } - - fit1 <- JAGS_fit( - model_syntax = model_syntax1, data = data, prior_list = prior_list1, - formula_list = formula_list1, formula_data_list = formula_data_list1, formula_prior_list = formula_prior_list1) - - mixed_posteriors <- as_mixed_posteriors( - mode = fit1, - parameters = names(attr(fit1, "prior_list")) - ) - - vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-intercept", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_intercept", prior = T, dots_prior = list(col = "grey")) - }) - - vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-x_cont1", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_cont1", prior = T, dots_prior = list(col = "grey")) - }) - - vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-x_fac2t", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_fac2t", prior = T, dots_prior = list(col = "grey")) - }) - - vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-x_fac3t", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "mu_x_fac3t", prior = T, dots_prior = list(col = "grey")) - }) - - vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-sigma", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors, "sigma", prior = T, dots_prior = list(col = "grey")) - }) - - vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-bias-PET", function(){ - PET <- mixed_posteriors$bias[,"PET",drop=FALSE] - attributes(PET) <- c(attributes(PET), attributes(mixed_posteriors$bias)[!names(attributes(mixed_posteriors$bias)) %in% c("dimnames", "dim")]) - attr(PET, "prior_list")[!sapply(attr(PET, "prior_list"), is.prior.PET)] <- lapply(1:sum(!sapply(attr(PET, "prior_list"), is.prior.PET)), function(i) prior("point", list(0))) - attr(PET, "prior_list")[ sapply(attr(PET, "prior_list"), is.prior.PET)] <- lapply(attr(PET, "prior_list")[sapply(attr(PET, "prior_list"), is.prior.PET)], function(p) { - class(p) <- class(p)[!class(p) %in% "prior.PET"] - return(p) - }) - plot_posterior(list(PET = PET), "PET", prior = T, dots_prior = list(col = "grey")) - }) - - - mixed_posteriors_conditional1 <- as_mixed_posteriors( - mode = fit1, - parameters = "mu_intercept", - conditional = "mu_intercept", - force_plots = TRUE - ) - - vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-intercept-con", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors_conditional1, "mu_intercept", prior = TRUE, dots_prior = list(col = "grey")) - }) - - mixed_posteriors_conditional2 <- as_mixed_posteriors( - mode = fit1, - parameters = "mu_x_cont1", - conditional = "mu_x_cont1", - force_plots = TRUE - ) - - vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-x_cont1-con", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors_conditional2, "mu_x_cont1", prior = TRUE, dots_prior = list(col = "grey")) - }) - - mixed_posteriors_conditional3 <- as_mixed_posteriors( - mode = fit1, - parameters = "mu_x_fac2t", - conditional = "mu_x_fac2t", - force_plots = TRUE - ) - - vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-x_fac2t-con", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors_conditional3, "mu_x_fac2t", prior = TRUE, dots_prior = list(col = "grey")) - }) - - mixed_posteriors_conditional4 <- as_mixed_posteriors( - mode = fit1, - parameters = "mu_x_fac3t", - conditional = "mu_x_fac3t", - force_plots = TRUE - ) - - vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-x_fac3t-con", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors_conditional4, "mu_x_fac3t", prior = TRUE, dots_prior = list(col = "grey")) - }) - - - mixed_posteriors_conditional5a <- as_mixed_posteriors( - mode = fit1, - parameters = "bias" - ) - - mixed_posteriors_conditional5b <- as_mixed_posteriors( - mode = fit1, - parameters = "bias", - conditional = "bias", - force_plots = TRUE - ) - - mixed_posteriors_conditional6a <- as_mixed_posteriors( - mode = fit1, - parameters = "bias", - conditional = "PET", - force_plots = TRUE - ) - - mixed_posteriors_conditional6b <- as_mixed_posteriors( - mode = fit1, - parameters = "bias", - conditional = "omega", - force_plots = TRUE - ) - - - vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-weightfunction", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors_conditional5a, parameter = "weightfunction", prior = TRUE, col = "black", col.fill = ggplot2::alpha("grey", 0.2), dots_prior = list(col = "red", col.fill = ggplot2::alpha("red", 0.5))) - }) - - vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-weightfunction-con", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4)) - plot_posterior(mixed_posteriors_conditional6b, parameter = "weightfunction", prior = TRUE, col = "black", col.fill = ggplot2::alpha("grey", 0.2), dots_prior = list(col = "red", col.fill = ggplot2::alpha("red", 0.5))) - }) - -# # TODO: at some point -# vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-PETPEESE", function(){ -# oldpar <- graphics::par(no.readonly = TRUE) -# on.exit(graphics::par(mar = oldpar[["mar"]])) -# par(mar = c(4, 4, 1, 4)) -# plot_posterior(mixed_posteriors_conditional5a, parameter = "PETPEESE", prior = TRUE, col = "black", col.fill = ggplot2::alpha("grey", 0.2), dots_prior = list(col = "red", col.fill = ggplot2::alpha("red", 0.5))) -# }) -# -# vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-PETPEESE-con", function(){ -# oldpar <- graphics::par(no.readonly = TRUE) -# on.exit(graphics::par(mar = oldpar[["mar"]])) -# par(mar = c(4, 4, 1, 4)) -# plot_posterior(mixed_posteriors_conditional6b, parameter = "PETPEESE", prior = TRUE, col = "black", col.fill = ggplot2::alpha("grey", 0.2), dots_prior = list(col = "red", col.fill = ggplot2::alpha("red", 0.5))) -# }) - - vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-PET-con", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4), mfrow = c(3, 1)) - hist(mixed_posteriors_conditional5a$bias[,"PET"], breaks = 50, col = "grey", main = "PET", xlab = "PET") - hist(mixed_posteriors_conditional5b$bias[,"PET"], breaks = 50, col = "grey", main = "PET", xlab = "PET") - hist(mixed_posteriors_conditional6a$bias[,"PET"], breaks = 50, col = "grey", main = "PET", xlab = "PET") - }) - - vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-omega-con", function(){ - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mar = oldpar[["mar"]])) - par(mar = c(4, 4, 1, 4), mfrow = c(3, 3)) - hist(mixed_posteriors_conditional5a$bias[,"omega[0,0.025]"], breaks = 50, col = "grey", main = "omega[0,0.025]", xlab = "omega[0,0.025]") - hist(mixed_posteriors_conditional5a$bias[,"omega[0.025,0.05]"], breaks = 50, col = "grey", main = "omega[0.025,0.05]", xlab = "omega[0.025,0.05]") - hist(mixed_posteriors_conditional5a$bias[,"omega[0.05,0.975]"], breaks = 50, col = "grey", main = "omega[0.05,0.975]", xlab = "omega[0.05,0.975]") - - hist(mixed_posteriors_conditional5b$bias[,"omega[0,0.025]"], breaks = 50, col = "grey", main = "omega[0,0.025]", xlab = "omega[0,0.025]") - hist(mixed_posteriors_conditional5b$bias[,"omega[0.025,0.05]"], breaks = 50, col = "grey", main = "omega[0.025,0.05]", xlab = "omega[0.025,0.05]") - hist(mixed_posteriors_conditional5b$bias[,"omega[0.05,0.975]"], breaks = 50, col = "grey", main = "omega[0.05,0.975]", xlab = "omega[0.05,0.975]") - - hist(mixed_posteriors_conditional6b$bias[,"omega[0,0.025]"], breaks = 50, col = "grey", main = "omega[0,0.025]", xlab = "omega[0,0.025]") - hist(mixed_posteriors_conditional6b$bias[,"omega[0.025,0.05]"], breaks = 50, col = "grey", main = "omega[0.025,0.05]", xlab = "omega[0.025,0.05]") - hist(mixed_posteriors_conditional6b$bias[,"omega[0.05,0.975]"], breaks = 50, col = "grey", main = "omega[0.05,0.975]", xlab = "omega[0.05,0.975]") - }) - - - - -}) diff --git a/tests/testthat/test-model-averaging.R b/tests/testthat/test-model-averaging.R index 9b03f1ca..300cb9c5 100644 --- a/tests/testthat/test-model-averaging.R +++ b/tests/testthat/test-model-averaging.R @@ -1,64 +1,494 @@ -context("Model-averaging functions") - -test_that("Model-averaging functions work", { - - expect_equal(compute_inference(c(1,1), c(1, 1))$prior_probs, c(0.5, 0.5)) - expect_equal(compute_inference(c(1,1), c(1, 1))$post_probs, c(0.5, 0.5)) - expect_equal(compute_inference(c(1,1), c(1, 1))$BF, Inf) - expect_equal(attr(compute_inference(c(1,1), c(1, 1)), "is_null"), c(FALSE, FALSE)) - - expect_equal(compute_inference(c(1,4), c(1, 1))$prior_probs, c(0.2, 0.8)) - expect_equal(compute_inference(c(1,1,3), c(1, 1, 1))$prior_probs, c(0.2, 0.2, 0.6)) - expect_equal(compute_inference(c(1,1,4), c(1, 1, 1), c(F, T, F), conditional = TRUE)$prior_probs, c(0.2, 0, 0.8)) - - expect_equal(compute_inference(c(1,4), c(1, 1))$post_probs, c(0.2, 0.8)) - expect_equal(compute_inference(c(1,1,3), c(1, 1, 1))$post_probs, c(0.2, 0.2, 0.6)) - expect_equal(compute_inference(c(1,1,4), c(1, 1, 1), c(F, T, F), conditional = TRUE)$post_probs, c(0.2, 0, 0.8)) - expect_equal(attr(compute_inference(c(1,1,4), c(1, 1, 1), c(2)), "is_null"), c(F, T, F)) - - # automatically tests inclusion_bf as well - expect_equal(compute_inference(c(1,1), c(1, 1), 1)$BF, 1) - expect_equal(compute_inference(c(1,1), c(1, 2), c(F, T))$BF, exp(1-2)) - expect_equal(compute_inference(c(1,1,1), c(1, 1, 1), c(F, T, F))$BF, 1) - expect_equal(compute_inference(c(1,1,1), c(1, 2, 1), c(F, T, F))$BF, exp(1-2)) - - # and check BF formatting - expect_equivalent(format_BF(c(0, 1, 2, Inf)), c(0, 1, 2, Inf)) - expect_equivalent(format_BF(c(0, 1, 2, Inf), BF01 = TRUE), 1/c(0, 1, 2, Inf)) - expect_equivalent(format_BF(c(0, 1, 2, Inf), logBF = TRUE), log(c(0, 1, 2, Inf))) - expect_equivalent(format_BF(c(0, 1, 2, Inf), BF01 = TRUE, logBF = TRUE), log(1/c(0, 1, 2, Inf))) - expect_equal(attr(format_BF(1), "name"), "BF") - expect_equal(attr(format_BF(1, logBF = TRUE), "name"), "log(BF)") - expect_equal(attr(format_BF(1, BF01 = TRUE, logBF = TRUE), "name"), "log(1/BF)") - - # additional BF checks - expect_equal(inclusion_BF(prior_probs = c(.5, .5), post_probs = c(.5, .5), is_null = c(T, F)), 1) - expect_equal(inclusion_BF(prior_probs = c(.5, .5), post_probs = c(.75, .25), is_null = c(T, F)), 1/3) - expect_equal(inclusion_BF(prior_probs = c(.25, .25, .25, .25), post_probs = c(.75, 0, .25, 0), is_null = c(T, T, F, F)), 1/3) - expect_equal(inclusion_BF(prior_probs = c(.25, .25, .25, .25), post_probs = c(.65, .10, .20, 0.05), is_null = c(T, T, F, F)), 1/3) - expect_equal(inclusion_BF(prior_probs = c(1, 0), post_probs = c(1, 0), is_null = c(T, F)), 0) - expect_equal(inclusion_BF(prior_probs = c(1, 0), post_probs = c(1, 0), is_null = c(F, T)), Inf) - - # test the marglik versions of BF - temp_prior_probs <- 1:6/sum(1:6) - temp_margliks <- -2:3 - temp_post_probs <- bridgesampling::post_prob(temp_margliks, prior_prob = temp_prior_probs) - expect_equal( - inclusion_BF(prior_probs = temp_prior_probs, post_probs = temp_post_probs, is_null = rep(c(T, F), 3)), - inclusion_BF(prior_probs = temp_prior_probs, margliks = temp_margliks, is_null = rep(c(T, F), 3)) - ) - - # check for over/underflow - temp_prior_probs <- 1:6/sum(1:6) - temp_margliks <- c(-2:2, 100) - temp_post_probs <- bridgesampling::post_prob(temp_margliks, prior_prob = temp_prior_probs) - expect_true(is.infinite(inclusion_BF(prior_probs = temp_prior_probs, post_probs = temp_post_probs, is_null = rep(c(T, F), 3)))) - expect_false(is.infinite(inclusion_BF(prior_probs = temp_prior_probs, margliks = temp_margliks, is_null = rep(c(T, F), 3)))) - - # additional omega mapping checks - expect_equal(weightfunctions_mapping(prior_list = list( - prior_none(), - prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/2), - prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.10)), prior_weights = 1/2) - )), list(NULL, c(2, 1, 1), c(3, 2, 1))) +# ============================================================================ # +# TEST FILE: Model Averaging Functions +# ============================================================================ # +# +# PURPOSE: +# Tests for compute_inference, ensemble_inference, mix_posteriors, models_inference, +# inclusion_BF, weightfunctions_mapping, and related Bayesian model averaging +# functions in R/model-averaging.R +# +# DEPENDENCIES: +# - bridgesampling: Required for marginal likelihood computation +# - rjags: For tests using pre-fitted models +# - common-functions.R: Test helpers +# +# SKIP CONDITIONS: +# - skip_if_not_installed("bridgesampling") +# - skip_if_not_installed("rjags") +# - skip_if_no_fits() +# +# MODELS/FIXTURES: +# - Uses pre-computed marginal likelihoods and pre-fitted models +# +# TAGS: @evaluation, @model-averaging +# ============================================================================ # + +# Reference directory for text output comparisons +REFERENCE_DIR <<- testthat::test_path("..", "results", "model-averaging") + +source(testthat::test_path("common-functions.R")) + + +# ============================================================================ # +# SECTION 1: compute_inference tests +# ============================================================================ # +test_that("compute_inference works correctly", { + + skip_if_not_installed("bridgesampling") + + # Test basic inference computation + prior_weights <- c(1, 1) + margliks <- c(-10, -11) + + result <- compute_inference(prior_weights, margliks) + + expect_equal(length(result$prior_probs), 2) + expect_equal(sum(result$prior_probs), 1) + expect_equal(length(result$post_probs), 2) + expect_true(sum(result$post_probs) > 0.99) # Should be close to 1 + expect_true(is.numeric(result$BF)) + + # Test with is_null as logical vector + result2 <- compute_inference(prior_weights, margliks, is_null = c(TRUE, FALSE)) + expect_true(is.numeric(result2$BF)) + expect_true(attr(result2, "is_null")[1]) + expect_false(attr(result2, "is_null")[2]) + + # Test with is_null as integer vector + result3 <- compute_inference(prior_weights, margliks, is_null = 1) + expect_true(attr(result3, "is_null")[1]) + expect_false(attr(result3, "is_null")[2]) + + # Test conditional inference + result4 <- compute_inference(prior_weights, margliks, is_null = c(TRUE, FALSE), conditional = TRUE) + expect_equal(result4$prior_probs[1], 0) # Null model should have 0 prior prob in conditional + expect_equal(result4$prior_probs[2], 1) # Alternative should have all weight + expect_true(attr(result4, "conditional")) + + # Test with unequal prior weights + result5 <- compute_inference(c(1, 3), margliks) + expect_equal(result5$prior_probs[1], 0.25) + expect_equal(result5$prior_probs[2], 0.75) + +}) + + +test_that("compute_inference input validation works", { + + skip_if_not_installed("bridgesampling") + + # Wrong is_null type + expect_error( + compute_inference(c(1, 1), c(-10, -11), is_null = "TRUE"), + "must be either logical vector, integer vector, or NULL" + ) + + # is_null wrong length + expect_error( + compute_inference(c(1, 1), c(-10, -11), is_null = c(TRUE, FALSE, TRUE)), + "must have length" + ) + + # mismatched lengths + expect_error( + compute_inference(c(1, 1, 1), c(-10, -11)), + "must have length" + ) + +}) + + +test_that("inclusion_BF works correctly", { + + # Test with posterior probabilities + prior_probs <- c(0.5, 0.5) + post_probs <- c(0.8, 0.2) + is_null <- c(TRUE, FALSE) + + BF <- inclusion_BF(prior_probs = prior_probs, post_probs = post_probs, is_null = is_null) + expect_true(is.numeric(BF)) + # With equal prior, BF should be 0.2/0.8 = 0.25 (for alternative vs null) + expect_equal(BF, 0.25) + + # Test with marginal likelihoods + margliks <- c(-10, -10) # Equal margliks + BF2 <- inclusion_BF(prior_probs = prior_probs, margliks = margliks, is_null = is_null) + expect_equal(BF2, 1) # Should be 1 with equal margliks and equal priors + + # Test with integer is_null + BF3 <- inclusion_BF(prior_probs = prior_probs, post_probs = post_probs, is_null = 1) + expect_equal(BF3, BF) + + # Test all null scenario + BF_all_null <- inclusion_BF(prior_probs = prior_probs, post_probs = post_probs, is_null = c(TRUE, TRUE)) + expect_equal(BF_all_null, 0) + + # Test all alternative scenario + BF_all_alt <- inclusion_BF(prior_probs = prior_probs, post_probs = post_probs, is_null = c(FALSE, FALSE)) + expect_equal(BF_all_alt, Inf) + +}) + + +test_that("inclusion_BF input validation works", { + + # Wrong is_null type + expect_error( + inclusion_BF(prior_probs = c(0.5, 0.5), post_probs = c(0.8, 0.2), is_null = "TRUE"), + "must be either logical vector, integer vector, or NULL" + ) + + # Missing arguments + expect_error( + inclusion_BF(prior_probs = c(0.5, 0.5), is_null = c(TRUE, FALSE)), + "'prior_probs' and either 'post_probs' or 'marglik' must be specified" + ) + +}) + + +test_that(".inclusion_BF.probs edge cases work", { + + # Test when posterior is fully concentrated on alternative + prior_probs <- c(0.5, 0.5) + post_probs <- c(0, 1) + is_null <- c(TRUE, FALSE) + + BF <- BayesTools:::.inclusion_BF.probs(prior_probs, post_probs, is_null) + expect_equal(BF, Inf) + + # Test when posterior is fully concentrated on null + post_probs2 <- c(1, 0) + BF2 <- BayesTools:::.inclusion_BF.probs(prior_probs, post_probs2, is_null) + expect_equal(BF2, 0) + +}) + + +test_that("weightfunctions_mapping works correctly", { + + # Create weightfunction priors + wf_onesided <- prior_weightfunction("one.sided", list(c(0.05), c(1, 1))) + wf_twosided <- prior_weightfunction("two.sided", list(c(0.05), c(1, 1))) + + # Test with single weightfunction + mapping1 <- weightfunctions_mapping(list(wf_onesided)) + expect_true(is.list(mapping1)) + + # Test with cuts_only = TRUE + cuts <- weightfunctions_mapping(list(wf_onesided), cuts_only = TRUE) + expect_true(is.numeric(cuts)) + expect_true(0 %in% cuts) + expect_true(1 %in% cuts) + + # Test with one_sided = TRUE + mapping2 <- weightfunctions_mapping(list(wf_twosided), one_sided = TRUE) + expect_true(is.list(mapping2)) + + # Test with point prior (should be handled gracefully) + p_point <- prior("point", list(1)) + mapping3 <- weightfunctions_mapping(list(wf_onesided, p_point)) + expect_true(is.list(mapping3)) + +}) + + +test_that("weightfunctions_mapping input validation works", { + + # Non-weightfunction priors should fail + p_normal <- prior("normal", list(0, 1)) + expect_error( + weightfunctions_mapping(list(p_normal)), + "must be a list of weightfunction priors" + ) + +}) + +# ============================================================================ # +# SECTION 5: mix_posteriors tests +# ============================================================================ # +test_that("mix_posteriors handles various prior types correctly", { + + skip_on_cran() + skip_if_not_installed("rjags") + skip_if_no_fits() + + # Load fits with margliks + fit_simple_normal <- readRDS(file.path(temp_fits_dir, "fit_simple_normal.RDS")) + marglik_simple_normal <- readRDS(file.path(temp_marglik_dir, "fit_simple_normal.RDS")) + + fit_simple_spike <- readRDS(file.path(temp_fits_dir, "fit_simple_spike.RDS")) + marglik_simple_spike <- readRDS(file.path(temp_marglik_dir, "fit_simple_spike.RDS")) + + # Create model list for simple priors + models_simple <- list( + list(fit = fit_simple_normal, marglik = marglik_simple_normal, prior_weights = 1), + list(fit = fit_simple_spike, marglik = marglik_simple_spike, prior_weights = 1) + ) + + # Test mix_posteriors with simple priors + mixed <- mix_posteriors( + model_list = models_simple, + parameters = c("m", "s"), + is_null_list = list("m" = c(FALSE, TRUE), "s" = c(FALSE, FALSE)), + seed = 1, + n_samples = 1000 + ) + + expect_true(inherits(mixed, "mixed_posteriors")) + # Capture a summary of the mixed posteriors structure for reference + mixed_info <- paste0( + "Class: ", paste(class(mixed), collapse = ", "), "\n", + "Parameters: ", paste(names(mixed), collapse = ", "), "\n", + "Sample size m: ", length(mixed$m), "\n", + "Sample size s: ", length(mixed$s) + ) + test_reference_text(mixed_info, "mix_posteriors_simple_info.txt") + expect_equal(length(mixed$m), 1000) + expect_equal(length(mixed$s), 1000) + + # Test with conditional = TRUE + mixed_conditional <- mix_posteriors( + model_list = models_simple, + parameters = c("m"), + is_null_list = list("m" = c(FALSE, TRUE)), + conditional = TRUE, + seed = 1, + n_samples = 1000 + ) + + expect_true(inherits(mixed_conditional, "mixed_posteriors")) +}) + + +test_that("mix_posteriors handles weightfunction priors", { + + skip_on_cran() + skip_if_not_installed("rjags") + skip_if_no_fits() + + # Load summary models which have weightfunction priors + fit_summary0 <- readRDS(file.path(temp_fits_dir, "fit_summary0.RDS")) + marglik_summary0 <- readRDS(file.path(temp_marglik_dir, "fit_summary0.RDS")) + + fit_summary1 <- readRDS(file.path(temp_fits_dir, "fit_summary1.RDS")) + marglik_summary1 <- readRDS(file.path(temp_marglik_dir, "fit_summary1.RDS")) + + fit_summary2 <- readRDS(file.path(temp_fits_dir, "fit_summary2.RDS")) + marglik_summary2 <- readRDS(file.path(temp_marglik_dir, "fit_summary2.RDS")) + + models_wf <- list( + list(fit = fit_summary0, marglik = marglik_summary0, prior_weights = 1), + list(fit = fit_summary1, marglik = marglik_summary1, prior_weights = 1), + list(fit = fit_summary2, marglik = marglik_summary2, prior_weights = 1) + ) + + mixed_wf <- mix_posteriors( + model_list = models_wf, + parameters = c("m", "omega"), + is_null_list = list("m" = c(FALSE, FALSE, FALSE), "omega" = c(TRUE, FALSE, FALSE)), + seed = 1, + n_samples = 1000 + ) + + expect_true(inherits(mixed_wf, "mixed_posteriors")) +}) + + +test_that("mix_posteriors handles factor priors", { + + skip_on_cran() + skip_if_not_installed("rjags") + skip_if_no_fits() + + # Load the orthonormal factor models (have both factor priors and marginal likelihoods) + fit_orthonormal_0 <- readRDS(file.path(temp_fits_dir, "fit_orthonormal_0.RDS")) + marglik_orthonormal_0 <- readRDS(file.path(temp_marglik_dir, "fit_orthonormal_0.RDS")) + + fit_orthonormal_1 <- readRDS(file.path(temp_fits_dir, "fit_orthonormal_1.RDS")) + marglik_orthonormal_1 <- readRDS(file.path(temp_marglik_dir, "fit_orthonormal_1.RDS")) + + # Create model list with two different models + models_factor <- list( + list(fit = fit_orthonormal_0, marglik = marglik_orthonormal_0, prior_weights = 1), + list(fit = fit_orthonormal_1, marglik = marglik_orthonormal_1, prior_weights = 1) + ) + + # Get the parameters from the model + prior_list <- attr(fit_orthonormal_1, "prior_list") + param_names <- names(prior_list) + + # Filter to factor parameters only + factor_params <- param_names[sapply(prior_list, is.prior.factor)] + + mixed_factor <- mix_posteriors( + model_list = models_factor, + parameters = factor_params[1], # Just test one + is_null_list = setNames(list(c(TRUE, FALSE)), factor_params[1]), + seed = 1, + n_samples = 1000 + ) + + expect_true(inherits(mixed_factor, "mixed_posteriors")) +}) + + +test_that("mix_posteriors handles vector priors", { + + skip_on_cran() + skip_if_not_installed("rjags") + skip_if_no_fits() + + # Load vector prior models + fit_vector_mnormal <- readRDS(file.path(temp_fits_dir, "fit_vector_mnormal.RDS")) + + # Create a mock marglik for testing (we only need the structure) + mock_marglik <- structure( + list(logml = -100, niter = 1000, method = "warp3"), + class = "bridge" + ) + + models_vector <- list( + list(fit = fit_vector_mnormal, marglik = mock_marglik, prior_weights = 1), + list(fit = fit_vector_mnormal, marglik = mock_marglik, prior_weights = 1) + ) + + prior_list <- attr(fit_vector_mnormal, "prior_list") + vector_params <- names(prior_list)[sapply(prior_list, is.prior.vector)] + + mixed_vector <- mix_posteriors( + model_list = models_vector, + parameters = vector_params[1], + is_null_list = setNames(list(c(FALSE, FALSE)), vector_params[1]), + seed = 1, + n_samples = 1000 + ) + + expect_true(inherits(mixed_vector, "mixed_posteriors")) +}) + + +# ============================================================================ # +# SECTION 6: ensemble_inference tests +# ============================================================================ # +test_that("ensemble_inference handles different configurations", { + + skip_on_cran() + skip_if_not_installed("rjags") + skip_if_no_fits() + + # Load fits with margliks + fit_simple_normal <- readRDS(file.path(temp_fits_dir, "fit_simple_normal.RDS")) + marglik_simple_normal <- readRDS(file.path(temp_marglik_dir, "fit_simple_normal.RDS")) + + fit_simple_spike <- readRDS(file.path(temp_fits_dir, "fit_simple_spike.RDS")) + marglik_simple_spike <- readRDS(file.path(temp_marglik_dir, "fit_simple_spike.RDS")) + + models <- list( + list(fit = fit_simple_normal, marglik = marglik_simple_normal, prior_weights = 1), + list(fit = fit_simple_spike, marglik = marglik_simple_spike, prior_weights = 1) + ) + + # Test with integer is_null specification + inference_int <- ensemble_inference( + model_list = models, + parameters = "m", + is_null_list = list("m" = 2) # Second model is null + ) + + expect_true(inherits(inference_int$m, "inference")) + inference_int_info <- paste0( + "BF: ", round(inference_int$m$BF, 4), "\n", + "is_null: ", paste(attr(inference_int$m, "is_null"), collapse = ", "), "\n", + "prior_probs: ", paste(round(inference_int$m$prior_probs, 4), collapse = ", "), "\n", + "post_probs: ", paste(round(inference_int$m$post_probs, 4), collapse = ", ") + ) + test_reference_text(inference_int_info, "ensemble_inference_int_spec.txt") + + # Test conditional inference + inference_cond <- ensemble_inference( + model_list = models, + parameters = "m", + is_null_list = list("m" = c(FALSE, TRUE)), + conditional = TRUE + ) + + expect_true(attr(inference_cond, "conditional")) + inference_cond_info <- paste0( + "Conditional: ", attr(inference_cond, "conditional"), "\n", + "BF: ", round(inference_cond$m$BF, 4) + ) + test_reference_text(inference_cond_info, "ensemble_inference_conditional.txt") + +}) + + +# ============================================================================ # +# SECTION 7: models_inference tests +# ============================================================================ # +test_that("models_inference computes correctly", { + + skip_on_cran() + skip_if_not_installed("rjags") + skip_if_no_fits() + + # Load fits with margliks + fit_simple_normal <- readRDS(file.path(temp_fits_dir, "fit_simple_normal.RDS")) + marglik_simple_normal <- readRDS(file.path(temp_marglik_dir, "fit_simple_normal.RDS")) + + fit_simple_spike <- readRDS(file.path(temp_fits_dir, "fit_simple_spike.RDS")) + marglik_simple_spike <- readRDS(file.path(temp_marglik_dir, "fit_simple_spike.RDS")) + + models <- list( + list(fit = fit_simple_normal, marglik = marglik_simple_normal, prior_weights = 1), + list(fit = fit_simple_spike, marglik = marglik_simple_spike, prior_weights = 2) + ) + + models_with_inference <- models_inference(models) + + # Check that inference was added to each model + expect_true("inference" %in% names(models_with_inference[[1]])) + expect_true("inference" %in% names(models_with_inference[[2]])) + + # Create reference output for models_inference structure + models_inf_info <- paste0( + "Model 1 inference:\n", + " m_number: ", models_with_inference[[1]]$inference$m_number, "\n", + " prior_prob: ", round(models_with_inference[[1]]$inference$prior_prob, 6), "\n", + " post_prob: ", round(models_with_inference[[1]]$inference$post_prob, 6), "\n", + "Model 2 inference:\n", + " m_number: ", models_with_inference[[2]]$inference$m_number, "\n", + " prior_prob: ", round(models_with_inference[[2]]$inference$prior_prob, 6), "\n", + " post_prob: ", round(models_with_inference[[2]]$inference$post_prob, 6), "\n", + "Total post_prob: ", round(sum(sapply(models_with_inference, function(m) m$inference$post_prob)), 6) + ) + test_reference_text(models_inf_info, "models_inference_output.txt") + + # Check prior probs reflect weights (1:2 ratio) + expect_equal(models_with_inference[[1]]$inference$prior_prob, 1/3, tolerance = 1e-10) + expect_equal(models_with_inference[[2]]$inference$prior_prob, 2/3, tolerance = 1e-10) + + # Check posterior probs sum to 1 + total_post_prob <- sum(sapply(models_with_inference, function(m) m$inference$post_prob)) + expect_equal(total_post_prob, 1, tolerance = 1e-10) + +}) + + +# ============================================================================ # +# SECTION 8: as_mixed_posteriors and as_marginal_inference tests +# ============================================================================ # +test_that("as_mixed_posteriors works correctly with BayesTools_fit objects", { + + skip_on_cran() + skip_if_not_installed("rjags") + skip_if_no_fits() + + # Load a fitted model + fit_simple_normal <- readRDS(file.path(temp_fits_dir, "fit_simple_normal.RDS")) + + # as_mixed_posteriors needs a BayesTools_fit object + mixed <- as_mixed_posteriors(fit_simple_normal, parameters = c("m", "s")) + + expect_true(inherits(mixed, "mixed_posteriors")) }) diff --git a/tests/testthat/test-priors-coverage.R b/tests/testthat/test-priors-coverage.R new file mode 100644 index 00000000..a7262650 --- /dev/null +++ b/tests/testthat/test-priors-coverage.R @@ -0,0 +1,496 @@ +# ============================================================================ # +# TEST FILE: Prior Distribution Coverage Tests +# ============================================================================ # +# +# PURPOSE: +# Targeted tests to ensure code coverage for edge cases and error paths +# in R/priors.R that are not covered by main test-priors.R +# +# DEPENDENCIES: +# - No external packages required beyond testthat +# +# SKIP CONDITIONS: +# - None (fast, pure R tests) +# +# MODELS/FIXTURES: +# - None required +# +# TAGS: @coverage, @edge-cases, @priors +# ============================================================================ # + + +# ============================================================================ # +# SECTION: prior() constructor errors +# ============================================================================ # + +test_that("prior() rejects unknown distribution names", { + expect_error(prior("unknown_dist", list(0, 1)), + "The specified distribution name") +}) + + +# ============================================================================ # +# SECTION: prior_factor() contrast validation +# ============================================================================ # + +test_that("prior_factor() requires multivariate prior for orthonormal/meandif contrasts", { + # orthonormal contrast requires multivariate distribution + expect_error(prior_factor("normal", list(0, 1), contrast = "orthonormal"), + "contrasts require multivariate prior") + + # meandif contrast requires multivariate distribution + expect_error(prior_factor("normal", list(0, 1), contrast = "meandif"), + "contrasts require multivariate prior") + + # bernoulli is not a valid multivariate distribution + expect_error(prior_factor("bernoulli", list(0.5), contrast = "orthonormal"), + "contrasts require multivariate prior") +}) + + +test_that("prior_factor() requires univariate prior for treatment contrast", { + expect_error(prior_factor("mnormal", list(0, 1, 2), contrast = "treatment"), + "contrasts require univariate prior") +}) + + +# ============================================================================ # +# SECTION: spike_and_slab prior construction and helpers +# ============================================================================ # + +test_that("prior_spike_and_slab() works with factor priors", { + p_factor <- prior_factor("mnormal", list(mean = 0, sd = 1), contrast = "orthonormal") + p_factor$parameters[["K"]] <- 2 + + p_ss <- prior_spike_and_slab( + prior_parameter = p_factor, + prior_inclusion = prior("beta", list(1, 1)) + ) + + expect_true(is.prior.spike_and_slab(p_ss)) + expect_s3_class(p_ss, "prior.spike_and_slab") +}) + + +test_that(".set_spike_and_slab_variable_attr() sets attributes correctly", { + p_ss <- prior_spike_and_slab( + prior_parameter = prior("normal", list(0, 1)), + prior_inclusion = prior("beta", list(1, 1)) + ) + + p_ss2 <- BayesTools:::.set_spike_and_slab_variable_attr(p_ss, "test_attr", "test_value") + expect_true(is.prior.spike_and_slab(p_ss2)) + + # Error when not spike_and_slab + expect_error(BayesTools:::.set_spike_and_slab_variable_attr(prior("normal", list(0, 1)), "attr", "val"), + "only works with spike_and_slab priors") +}) + + +test_that(".get_spike_and_slab_variable() requires spike_and_slab prior", { + expect_error(BayesTools:::.get_spike_and_slab_variable(prior("normal", list(0, 1))), + "only works with spike_and_slab priors") +}) + + +test_that(".get_spike_and_slab_inclusion() requires spike_and_slab prior", { + expect_error(BayesTools:::.get_spike_and_slab_inclusion(prior("normal", list(0, 1))), + "only works with spike_and_slab priors") +}) + + +# ============================================================================ # +# SECTION: prior_mixture() construction +# ============================================================================ # + +test_that("prior_mixture() creates factor_mixture with spike component", { + p1 <- prior("spike", list(0)) + p2 <- prior_factor("mnormal", list(0, 1), contrast = "orthonormal") + p2$parameters[["K"]] <- 2 + + p_mix <- prior_mixture(list(p1, p2), components = c("null", "alt")) + + expect_s3_class(p_mix, "prior.factor_mixture") +}) + + +test_that("prior_mixture() handles prior_none in factor mixtures", { + p1 <- prior_none() + p2 <- prior_factor("mnormal", list(0, 1), contrast = "orthonormal") + p2$parameters[["K"]] <- 2 + + p_mix <- prior_mixture(list(p1, p2), components = c("null", "alt")) + + expect_s3_class(p_mix, "prior.factor_mixture") +}) + + +test_that("prior_mixture() creates bias_mixture for PET/PEESE/weightfunction", { + p_pet <- prior_PET("normal", list(0, 1)) + p_wf <- prior_weightfunction("one.sided", list(steps = c(0.05), alpha = c(1, 1))) + + p_mix <- prior_mixture(list(p_pet, p_wf), components = c("a", "b")) + + expect_s3_class(p_mix, "prior.bias_mixture") +}) + + +# ============================================================================ # +# SECTION: Distribution parameter validation +# ============================================================================ # + +test_that("uniform prior requires a < b", { + expect_error(prior("uniform", list(a = 5, b = 1)), + "lower than") +}) + + +# ============================================================================ # +# SECTION: rng() function with sample_components +# ============================================================================ # + +test_that("rng() spike_and_slab returns component indicators", { + p_ss <- prior_spike_and_slab( + prior_parameter = prior("normal", list(0, 1)), + prior_inclusion = prior("beta", list(1, 1)) + ) + + set.seed(1) + components <- rng(p_ss, 10, sample_components = TRUE) + + expect_true(all(components %in% c(0, 1))) + expect_length(components, 10) +}) + + +test_that("rng() mixture returns component indicators", { + p_mix <- prior_mixture( + list(prior("normal", list(0, 1)), prior("normal", list(3, 1))), + components = c("a", "b") + ) + + set.seed(1) + components <- rng(p_mix, 10, sample_components = TRUE) + + expect_true(all(components %in% 1:2)) + expect_length(components, 10) +}) + + +test_that("rng() factor_mixture with transform_factor_samples", { + p_mix <- prior_mixture( + list( + prior("spike", list(0)), + prior_factor("mnormal", list(0, 1), contrast = "orthonormal") + ), + components = c("null", "alt") + ) + for (i in seq_along(p_mix)) { + p_mix[[i]]$parameters[["K"]] <- 2 + } + + set.seed(1) + samples <- rng(p_mix, 10, transform_factor_samples = FALSE) + expect_true(is.matrix(samples)) + + samples2 <- rng(p_mix, 10, transform_factor_samples = TRUE) + expect_true(is.matrix(samples2)) + expect_equal(ncol(samples2), 3) # K+1 columns +}) + + +test_that("rng() orthonormal prior with transform_factor_samples", { + p <- prior_factor("mnormal", list(0, 1), contrast = "orthonormal") + p$parameters[["K"]] <- 2 + + set.seed(1) + samples <- rng(p, 10, transform_factor_samples = TRUE) + + expect_true(is.matrix(samples)) + expect_equal(ncol(samples), 3) # K+1 columns +}) + + +# ============================================================================ # +# SECTION: cdf() function edge cases +# ============================================================================ # + +test_that("cdf() not implemented for spike_and_slab", { + p_ss <- prior_spike_and_slab( + prior_parameter = prior("normal", list(0, 1)), + prior_inclusion = prior("beta", list(1, 1)) + ) + + expect_error(cdf(p_ss, 0), "No cdfs are implemented for spike and slab") +}) + + +test_that("cdf() handles truncated priors correctly", { + p <- prior("normal", list(0, 1), truncation = list(-2, 2)) + + expect_true(cdf(p, 0) > 0) + expect_true(cdf(p, -3) == 0) # Below truncation + expect_true(cdf(p, 3) >= 1 - 1e-6) # Above truncation +}) + + +# ============================================================================ # +# SECTION: ccdf() function edge cases +# ============================================================================ # + +test_that("ccdf() not implemented for spike_and_slab or mixture", { + p_ss <- prior_spike_and_slab( + prior_parameter = prior("normal", list(0, 1)), + prior_inclusion = prior("beta", list(1, 1)) + ) + expect_error(ccdf(p_ss, 0), "No ccdf are implemented for spike and slab") + + p_mix <- prior_mixture( + list(prior("normal", list(0, 1)), prior("normal", list(3, 1))), + components = c("a", "b") + ) + expect_error(ccdf(p_mix, 0), "No ccdf are implemented for prior mixtures") +}) + + +test_that("ccdf() handles truncated priors correctly", { + p <- prior("normal", list(0, 1), truncation = list(-2, 2)) + expect_true(ccdf(p, 0) > 0) + expect_true(ccdf(p, 3) == 0) # Above truncation +}) + + +# ============================================================================ # +# SECTION: lpdf() function edge cases +# ============================================================================ # + +test_that("lpdf() not implemented for spike_and_slab or mixture", { + p_ss <- prior_spike_and_slab( + prior_parameter = prior("normal", list(0, 1)), + prior_inclusion = prior("beta", list(1, 1)) + ) + expect_error(lpdf(p_ss, 0), "No lpdf are implemented for spike and slab") + + p_mix <- prior_mixture( + list(prior("normal", list(0, 1)), prior("normal", list(3, 1))), + components = c("a", "b") + ) + expect_error(lpdf(p_mix, 0), "No lpdf are implemented for prior mixtures") +}) + + +# ============================================================================ # +# SECTION: quant() function edge cases +# ============================================================================ # + +test_that("quant() not implemented for spike_and_slab or mixture", { + p_ss <- prior_spike_and_slab( + prior_parameter = prior("normal", list(0, 1)), + prior_inclusion = prior("beta", list(1, 1)) + ) + expect_error(quant(p_ss, 0.5), "No quant(ile)? functions? are implemented for spike and slab") + + p_mix <- prior_mixture( + list(prior("normal", list(0, 1)), prior("normal", list(3, 1))), + components = c("a", "b") + ) + expect_error(quant(p_mix, 0.5), "No quant(ile)? functions? are implemented for prior mixtures") +}) + + +test_that("quant() handles truncated priors with optimization", { + p <- prior("normal", list(0, 1), truncation = list(0.5, 2)) + + q <- quant(p, 0.5) + expect_true(q > 0.5 && q < 2) + + q_low <- quant(p, 0.01) + q_high <- quant(p, 0.99) + expect_true(q_low >= 0.5) + expect_true(q_high <= 2) +}) + + +# ============================================================================ # +# SECTION: Multivariate distribution functions (mcdf, mccdf, mlpdf, mquant) +# ============================================================================ # + +test_that("mcdf() works for orthonormal and meandif priors", { + p_orth <- prior_factor("mnormal", list(0, 1), contrast = "orthonormal") + p_orth$parameters[["K"]] <- 2 + + cdf_val <- mcdf(p_orth, 0) + expect_true(cdf_val >= 0 && cdf_val <= 1) + + p_md <- prior_factor("mnormal", list(0, 1), contrast = "meandif") + p_md$parameters[["K"]] <- 2 + + cdf_val2 <- mcdf(p_md, 0) + expect_true(cdf_val2 >= 0 && cdf_val2 <= 1) +}) + + +test_that("mccdf() works for orthonormal and meandif priors", { + p_orth <- prior_factor("mnormal", list(0, 1), contrast = "orthonormal") + p_orth$parameters[["K"]] <- 2 + + ccdf_val <- mccdf(p_orth, 0) + expect_true(ccdf_val >= 0 && ccdf_val <= 1) + + p_md <- prior_factor("mnormal", list(0, 1), contrast = "meandif") + p_md$parameters[["K"]] <- 2 + + ccdf_val2 <- mccdf(p_md, 0) + expect_true(ccdf_val2 >= 0 && ccdf_val2 <= 1) +}) + + +test_that("mlpdf() works for orthonormal and meandif priors", { + p_orth <- prior_factor("mnormal", list(0, 1), contrast = "orthonormal") + p_orth$parameters[["K"]] <- 2 + + lpdf_val <- mlpdf(p_orth, 0) + expect_true(is.finite(lpdf_val)) + + p_md <- prior_factor("mnormal", list(0, 1), contrast = "meandif") + p_md$parameters[["K"]] <- 2 + + lpdf_val2 <- mlpdf(p_md, 0) + expect_true(is.finite(lpdf_val2)) +}) + + +test_that("mquant() works for orthonormal and meandif priors", { + p_orth <- prior_factor("mnormal", list(0, 1), contrast = "orthonormal") + p_orth$parameters[["K"]] <- 2 + + q <- mquant(p_orth, 0.5) + expect_true(is.numeric(q)) + + p_md <- prior_factor("mnormal", list(0, 1), contrast = "meandif") + p_md$parameters[["K"]] <- 2 + + q2 <- mquant(p_md, 0.5) + expect_true(is.numeric(q2)) +}) + + +# ============================================================================ # +# SECTION: S3 dispatch and generic functions +# ============================================================================ # + +test_that("pdf() S3 dispatch works for prior objects", { + p <- prior("normal", list(0, 1)) + expect_true(is.numeric(pdf(p, 0))) +}) + + +# ============================================================================ # +# SECTION: mean() function edge cases +# ============================================================================ # + +test_that("mean() works for spike_and_slab priors", { + p_ss <- prior_spike_and_slab( + prior_parameter = prior("normal", list(1, 1)), + prior_inclusion = prior("point", list(0.5)) + ) + + m <- mean(p_ss) + expect_true(is.numeric(m)) + expect_equal(m, 0.5) # mean(normal(1,1)) * 0.5 +}) + + +test_that("mean() handles truncated distributions and undefined moments", { + # Truncated normal + p <- prior("normal", list(0, 1), truncation = list(0, Inf)) + m <- mean(p) + expect_true(m > 0) + + # Truncated t with df <= 1 returns NaN + p_t <- prior("t", list(0, 1, 1), truncation = list(-1, 1)) + m_t <- mean(p_t) + expect_true(is.nan(m_t)) +}) + + +test_that("mean() returns NaN for multivariate t with df <= 1", { + p_mt <- prior_factor("mt", list(0, 1, 1), contrast = "orthonormal") + p_mt$parameters[["K"]] <- 2 + + m <- mean(p_mt) + expect_true(is.nan(m)) +}) + + +# ============================================================================ # +# SECTION: var() function edge cases +# ============================================================================ # + +test_that("var() S3 dispatch works for numeric vectors", { + x <- c(1, 2, 3, 4, 5) + expect_equal(var(x), stats::var(x)) +}) + + +test_that("var() works for spike_and_slab priors", { + # spike_and_slab with beta inclusion + p_ss <- prior_spike_and_slab( + prior_parameter = prior("normal", list(0, 1)), + prior_inclusion = prior("beta", list(1, 1)) + ) + v <- var(p_ss) + expect_true(is.numeric(v)) + expect_true(v > 0) + + # spike_and_slab with point inclusion + p_ss2 <- prior_spike_and_slab( + prior_parameter = prior("normal", list(0, 1)), + prior_inclusion = prior("point", list(0.5)) + ) + v2 <- var(p_ss2) + expect_true(is.numeric(v2)) +}) + + +test_that("var() returns NaN for distributions with undefined variance", { + # t with df <= 2 returns NaN for variance + p_t <- prior("t", list(0, 1, 2), truncation = list(-1, 1)) + v <- var(p_t) + expect_true(is.nan(v)) + + # invgamma with shape <= 2 returns NaN + p_ig <- prior("invgamma", list(2, 1), truncation = list(0.1, 10)) + v_ig <- var(p_ig) + expect_true(is.nan(v_ig)) +}) + + +test_that("var() works for orthonormal and meandif priors", { + # orthonormal with mpoint returns 0 + p_mp <- prior_factor("mpoint", list(0), contrast = "orthonormal") + p_mp$parameters[["K"]] <- 2 + v <- var(p_mp) + expect_equal(v, 0) + + # orthonormal with mt and df <= 2 returns NaN + p_mt <- prior_factor("mt", list(0, 1, 2), contrast = "orthonormal") + p_mt$parameters[["K"]] <- 2 + v_mt <- var(p_mt) + expect_true(is.nan(v_mt)) + + # meandif with mnormal returns positive variance + p_md <- prior_factor("mnormal", list(0, 1), contrast = "meandif") + p_md$parameters[["K"]] <- 2 + v_md <- var(p_md) + expect_true(v_md > 0) +}) + + +test_that("var() not implemented for mixture priors", { + p_mix <- prior_mixture( + list(prior("normal", list(0, 1)), prior("normal", list(3, 1))), + components = c("a", "b") + ) + expect_error(var(p_mix), "No var is implemented for prior mixtures") +}) diff --git a/tests/testthat/test-priors-density.R b/tests/testthat/test-priors-density.R index 2550a4fa..a1b88858 100644 --- a/tests/testthat/test-priors-density.R +++ b/tests/testthat/test-priors-density.R @@ -1,4 +1,21 @@ -context("Prior density") +# ============================================================================ # +# TEST FILE: Prior Density Function +# ============================================================================ # +# +# PURPOSE: +# Visual regression tests for the density.prior S3 method including +# various transformation options. +# +# DEPENDENCIES: +# - vdiffr: Visual regression testing +# +# SKIP CONDITIONS: +# - skip_if_not_installed("vdiffr") +# +# TAGS: @evaluation, @visual, @priors, @density +# ============================================================================ # + +skip_if_not_installed("vdiffr") test_that("Prior density function density", { set.seed(1) @@ -30,6 +47,17 @@ test_that("Prior density function density", { vdiffr::expect_doppelganger("prior-density-4-2", function()plot(density(prior_factor("normal", list(0, 1), list(0, Inf), contrast = "treatment")))) vdiffr::expect_doppelganger("prior-density-4-3", function()suppressWarnings(plot(density(prior_factor("mnormal", list(0, 1), contrast = "orthonormal"))))) vdiffr::expect_doppelganger("prior-density-4-4", function()suppressWarnings(plot(density(prior_factor("mnormal", list(0, 1), contrast = "meandif"))))) + vdiffr::expect_doppelganger("prior-density-4-5", function()suppressWarnings(plot(density(prior_factor("mnormal", list(0, 1), contrast = "orthonormal"), force_samples = TRUE)))) + + # PET + vdiffr::expect_doppelganger("prior-density-5-1", function()plot(density(prior_PET("normal", list(0, 1))))) + vdiffr::expect_doppelganger("prior-density-5-2", function()plot(density(prior_PET("normal", list(0, 1)), force_samples = TRUE))) + vdiffr::expect_doppelganger("prior-density-5-3", function()plot(density(prior_PET("normal", list(0, 1)), force_samples = TRUE, transformation = "tanh"))) + + # no plotting etc implemented + xd <- density(prior_spike_and_slab(prior("normal", list(0, 1)))) + expect_s3_class(xd, "density.prior.spike_and_slab") + }) diff --git a/tests/testthat/test-priors-informed.R b/tests/testthat/test-priors-informed.R index d1ddbd22..0b27ff51 100644 --- a/tests/testthat/test-priors-informed.R +++ b/tests/testthat/test-priors-informed.R @@ -1,4 +1,19 @@ -context("Prior informed function") +# ============================================================================ # +# TEST FILE: Prior Informed Function +# ============================================================================ # +# +# PURPOSE: +# Tests for prior_informed function that creates priors based on +# published informed prior specifications (Oosterwijk, van Erp, medicine). +# +# DEPENDENCIES: +# - None (pure R) +# +# SKIP CONDITIONS: +# - None (can run on CRAN) +# +# TAGS: @evaluation, @priors, @informed +# ============================================================================ # test_that("Informed prior distributions match the specification", { diff --git a/tests/testthat/test-priors-plot.R b/tests/testthat/test-priors-plot.R index d8a27f03..7f6bf117 100644 --- a/tests/testthat/test-priors-plot.R +++ b/tests/testthat/test-priors-plot.R @@ -1,4 +1,21 @@ -context("Prior plot function") +# ============================================================================ # +# TEST FILE: Prior Plot Function +# ============================================================================ # +# +# PURPOSE: +# Visual regression tests for the plot.prior S3 method including +# base graphics and ggplot2 output. +# +# DEPENDENCIES: +# - vdiffr: Visual regression testing +# +# SKIP CONDITIONS: +# - skip_if_not_installed("vdiffr") +# +# TAGS: @evaluation, @visual, @priors, @plots +# ============================================================================ # + +skip_if_not_installed("vdiffr") test_that("Prior plot (simple) function works", { @@ -80,7 +97,7 @@ test_that("Prior plot (point) function works", { test_that("Prior plot (spike and slab) function works", { # check the default options - p1 <- prior("bernoulli", list(.33)) + p1 <- prior_spike_and_slab(prior("Normal", list(0, 1))) vdiffr::expect_doppelganger("priors-plot-15-1", function()plot(p1)) vdiffr::expect_doppelganger("priors-plot-15-2", function()plot(p1, short_name = TRUE)) @@ -93,7 +110,10 @@ test_that("Prior plot (spike and slab) function works", { vdiffr::expect_doppelganger("priors-plot-15-8", plot(p1, parameter_names = TRUE, plot_type = "ggplot")) vdiffr::expect_doppelganger("priors-plot-15-9", plot(p1, xlab = "xlab", ylab = "ylab", main = "main", plot_type = "ggplot")) vdiffr::expect_doppelganger("priors-plot-15-10", plot(p1, lwd = 3, lty = 3, col = "blue", plot_type = "ggplot")) - vdiffr::expect_doppelganger("priors-plot-15-11", plot(p1, par_name = bquote(mu), plot_type = "ggplot")) + vdiffr::expect_doppelganger("priors-plot-15-11", plot(p1, par_name = "mu", plot_type = "ggplot")) + vdiffr::expect_doppelganger("priors-plot-15-12", plot(p1, par_name = "mu", plot_type = "ggplot", transformation = "exp")) + vdiffr::expect_doppelganger("priors-plot-15-13", plot(p1, par_name = "mu", plot_type = "ggplot", transformation = "exp", transformation_settings = T, xlim = c(1, 5))) + }) @@ -151,9 +171,11 @@ test_that("Prior plot (orthonormal) function works", { vdiffr::expect_doppelganger("priors-plot-11-2", function()plot(p11.2)) vdiffr::expect_doppelganger("priors-plot-11-3", plot(p11.3, plot_type = "ggplot")) vdiffr::expect_doppelganger("priors-plot-11-5", function()plot(p11.5)) + vdiffr::expect_doppelganger("priors-plot-11-5-1", function()suppressMessages(plot(p11.5, transformation = "exp"))) vdiffr::expect_doppelganger("priors-plot-12-9", function()plot(p12.9)) vdiffr::expect_doppelganger("priors-plot-20-3", function()plot(p20.3)) vdiffr::expect_doppelganger("priors-plot-20-5", plot(p20.5, plot_type = "ggplot")) + vdiffr::expect_doppelganger("priors-plot-20-5-1", plot(p20.5, plot_type = "ggplot", transformation = "exp")) }) @@ -166,6 +188,7 @@ test_that("Prior plot (treatment) function works", { vdiffr::expect_doppelganger("priors-plot-13-1", function()plot(p13)) vdiffr::expect_doppelganger("priors-plot-13-2", plot(p13, plot_type = "ggplot")) vdiffr::expect_doppelganger("priors-plot-14", function()plot(p14)) + vdiffr::expect_doppelganger("priors-plot-14-1", function()plot(p14, transformation = "tanh")) vdiffr::expect_doppelganger("priors-plot-21", function()plot(p21)) }) @@ -179,6 +202,7 @@ test_that("Prior plot (independent) function works", { vdiffr::expect_doppelganger("priors-plot-16-1", function()plot(p15)) vdiffr::expect_doppelganger("priors-plot-16-2", plot(p15, plot_type = "ggplot")) vdiffr::expect_doppelganger("priors-plot-17", function()plot(p16)) + vdiffr::expect_doppelganger("priors-plot-17-1", function()plot(p16, transformation = "exp")) vdiffr::expect_doppelganger("priors-plot-22", function()plot(p22)) }) diff --git a/tests/testthat/test-priors-print.R b/tests/testthat/test-priors-print.R index 33bbd1ac..707d0acc 100644 --- a/tests/testthat/test-priors-print.R +++ b/tests/testthat/test-priors-print.R @@ -1,4 +1,34 @@ -context("Prior print function") +# ============================================================================ # +# TEST FILE: Prior Print Function +# ============================================================================ # +# +# PURPOSE: +# Tests for the print.prior S3 method including input validation, +# formatting options, and output correctness. +# +# DEPENDENCIES: +# - None (pure R) +# +# SKIP CONDITIONS: +# - None (can run on CRAN) +# +# TAGS: @evaluation, @priors, @print +# ============================================================================ # + + +test_that("Prior print function input validation", { + + p <- prior("normal", list(0, 1)) + + # Check invalid inputs + expect_error(print(p, short_name = "no"), "'short_name'") + expect_error(print(p, parameter_names = "no"), "'parameter_names'") + expect_error(print(p, digits_estimates = "two"), "'digits_estimates'") + expect_error(print(p, plot = "yes"), "'plot'") + expect_error(print(p, silent = "shh"), "'silent'") + expect_error(print(p, inline = "no"), "'inline'") + +}) test_that("Prior print function works", { @@ -153,7 +183,7 @@ test_that("Prior print function works", { "alternative:", " (1/3) * Normal(mean = 0, sd = 1)", " (1/3) * Normal(mean = -3, sd = 1)", " (1/3) * Gamma(shape = 5, rate = 10)" )) expect_equal(utils::capture.output(print(p23, short_name = TRUE)), c( - "alternative:", " (1/7) * N(0, 1)", "null:", " (5/7) * N(-3, 1)", " (1/7) * G(5, 10)" + "alternative:", " (1/7) * N(-3, 1)", "null:", " (5/7) * N(0, 1)", " (1/7) * G(5, 10)" )) expect_equal(utils::capture.output(print(p24)), c( "b:", " (1/6) * Normal(0, 1)", "a:", " (5/6) * Normal(-3, 1)" @@ -175,3 +205,78 @@ test_that("Prior print function works", { text(0.5, 1, print(pe1, plot = TRUE)) }) }) + + +test_that("Prior print for prior_none", { + + p_none <- prior_none() + output <- utils::capture.output(print(p_none)) + expect_type(output, "character") + + # Silent output + expect_equal(utils::capture.output(print(p_none, silent = TRUE)), character()) + +}) + + +test_that("Prior print with inline option for mixtures", { + + p_mix <- prior_mixture( + list( + prior("normal", list(0, 1)), + prior("normal", list(0, 2)) + ) + ) + + # Test inline option + output_inline <- print(p_mix, silent = TRUE, inline = TRUE) + expect_type(output_inline, "character") + +}) + + +test_that("Prior print for additional distributions", { + + # Beta distribution with different parameters + p_beta <- prior("beta", list(alpha = 2, beta = 5)) + expect_equal(utils::capture.output(print(p_beta)), "Beta(2, 5)") + expect_equal(utils::capture.output(print(p_beta, short_name = TRUE)), "B(2, 5)") + + # Exponential distribution + p_exp <- prior("exp", list(rate = 2)) + expect_equal(utils::capture.output(print(p_exp)), "Exponential(2)") + expect_equal(utils::capture.output(print(p_exp, short_name = TRUE)), "E(2)") + + # Uniform distribution + p_unif <- prior("uniform", list(a = -1, b = 1)) + expect_equal(utils::capture.output(print(p_unif)), "Uniform(-1, 1)") + expect_equal(utils::capture.output(print(p_unif, short_name = TRUE)), "U(-1, 1)") + + # Lognormal distribution + p_ln <- prior("lognormal", list(meanlog = 0, sdlog = 1)) + expect_equal(utils::capture.output(print(p_ln)), "Lognormal(0, 1)") + expect_equal(utils::capture.output(print(p_ln, short_name = TRUE)), "Ln(0, 1)") + + # Inverse gamma distribution + p_ig <- prior("invgamma", list(shape = 1, scale = 1)) + expect_equal(utils::capture.output(print(p_ig)), "InvGamma(1, 1)") + expect_equal(utils::capture.output(print(p_ig, short_name = TRUE)), "Ig(1, 1)") + +}) + + +test_that("Prior print digits_estimates parameter", { + + p <- prior("normal", list(mean = 1.2345678, sd = 0.9876543)) + + # Default (2 digits) + expect_match(utils::capture.output(print(p)), "Normal\\(1\\.23, 0\\.99\\)") + + # 4 digits + expect_match(utils::capture.output(print(p, digits_estimates = 4)), "Normal\\(1\\.2346, 0\\.9877\\)") + + # 0 digits + expect_match(utils::capture.output(print(p, digits_estimates = 0)), "Normal\\(1, 1\\)") + +}) + diff --git a/tests/testthat/test-priors-tools.R b/tests/testthat/test-priors-tools.R index 601d3641..680854bf 100644 --- a/tests/testthat/test-priors-tools.R +++ b/tests/testthat/test-priors-tools.R @@ -1,4 +1,19 @@ -context("Prior distribution tool functions") +# ============================================================================ # +# TEST FILE: Prior Distribution Tool Functions +# ============================================================================ # +# +# PURPOSE: +# Tests for prior handling utilities, parameter checks, and prior type +# detection functions. +# +# DEPENDENCIES: +# - None (pure R) +# +# SKIP CONDITIONS: +# - None (can run on CRAN) +# +# TAGS: @evaluation, @priors, @tools +# ============================================================================ # test_that("Prior handling works", { @@ -43,3 +58,306 @@ test_that("Prior handling works", { expect_error(prior_weightfunction("one-sided", list(c(.05, 0.55, .40), c(1, 1), c(1, 1))), "Parameters 'steps' must be monotonically increasing.") }) + + +test_that("is.prior functions work correctly", { + + # Create priors for testing + p_normal <- prior("normal", list(0, 1)) + p_point <- prior("point", list(0)) + p_discrete <- prior("bernoulli", list(0.5)) + p_vector <- prior("mnormal", list(0, 1, 3)) + p_pet <- prior_PET("normal", list(0, 1)) + p_peese <- prior_PEESE("normal", list(0, 1)) + p_wf <- prior_weightfunction("one.sided", list(c(0.05), c(1, 1))) + p_factor_t <- prior_factor("normal", contrast = "treatment", list(0, 1)) + p_factor_o <- prior_factor("mnormal", contrast = "orthonormal", list(0, 1)) + p_factor_m <- prior_factor("mnormal", contrast = "meandif", list(0, 1)) + p_factor_i <- prior_factor("beta", contrast = "independent", list(1, 1)) + p_spike_slab <- prior_spike_and_slab(prior("normal", list(0, 1)), prior_inclusion = prior("spike", list(0.5))) + p_none <- prior_none() + + # Test is.prior + expect_true(is.prior(p_normal)) + expect_true(is.prior(p_point)) + expect_false(is.prior("not a prior")) + expect_false(is.prior(list(a = 1))) + + # Test is.prior.simple + expect_true(is.prior.simple(p_normal)) + expect_true(is.prior.simple(p_point)) + expect_false(is.prior.simple(p_wf)) + expect_false(is.prior.simple(p_vector)) + + # Test is.prior.point + expect_true(is.prior.point(p_point)) + expect_false(is.prior.point(p_normal)) + + # Test is.prior.none + expect_true(is.prior.none(p_none)) + expect_false(is.prior.none(p_normal)) + + # Test is.prior.discrete + expect_true(is.prior.discrete(p_discrete)) + expect_false(is.prior.discrete(p_normal)) + + # Test is.prior.vector + expect_true(is.prior.vector(p_vector)) + expect_false(is.prior.vector(p_normal)) + + # Test is.prior.PET + expect_true(is.prior.PET(p_pet)) + expect_false(is.prior.PET(p_normal)) + + # Test is.prior.PEESE + expect_true(is.prior.PEESE(p_peese)) + expect_false(is.prior.PEESE(p_normal)) + + # Test is.prior.weightfunction + expect_true(is.prior.weightfunction(p_wf)) + expect_false(is.prior.weightfunction(p_normal)) + + # Test is.prior.factor + expect_true(is.prior.factor(p_factor_t)) + expect_true(is.prior.factor(p_factor_o)) + expect_true(is.prior.factor(p_factor_m)) + expect_true(is.prior.factor(p_factor_i)) + expect_false(is.prior.factor(p_normal)) + + # Test is.prior.treatment + expect_true(is.prior.treatment(p_factor_t)) + expect_false(is.prior.treatment(p_factor_o)) + + # Test is.prior.orthonormal + expect_true(is.prior.orthonormal(p_factor_o)) + expect_false(is.prior.orthonormal(p_factor_t)) + + # Test is.prior.meandif + expect_true(is.prior.meandif(p_factor_m)) + expect_false(is.prior.meandif(p_factor_o)) + + # Test is.prior.independent + expect_true(is.prior.independent(p_factor_i)) + expect_false(is.prior.independent(p_factor_t)) + + # Test is.prior.spike_and_slab + expect_true(is.prior.spike_and_slab(p_spike_slab)) + expect_false(is.prior.spike_and_slab(p_normal)) + +}) + + +test_that(".check_prior works correctly", { + + p_normal <- prior("normal", list(0, 1)) + + # Valid prior should pass + expect_null(BayesTools:::.check_prior(p_normal)) + + # Non-prior should fail + expect_error(BayesTools:::.check_prior("not a prior"), "must be a valid prior object") + expect_error(BayesTools:::.check_prior(list(a = 1)), "must be a valid prior object") + +}) + + +test_that(".check_prior_list works correctly", { + + p_normal <- prior("normal", list(0, 1)) + p_point <- prior("point", list(0)) + p_wf <- prior_weightfunction("one.sided", list(c(0.05), c(1, 1))) + + # Valid prior list should pass + expect_null(BayesTools:::.check_prior_list(list(p_normal, p_point))) + + # Empty list with allow_NULL + expect_null(BayesTools:::.check_prior_list(NULL, allow_NULL = TRUE)) + expect_null(BayesTools:::.check_prior_list(list(), allow_NULL = TRUE)) + + # Non-list should fail + expect_error(BayesTools:::.check_prior_list("not a list"), "must be a list") + + # List with non-prior should fail + expect_error(BayesTools:::.check_prior_list(list("not a prior")), "must be a prior distribution") + + # Disallowing specific types + expect_error( + BayesTools:::.check_prior_list(list(p_point), allow_prior.point = FALSE), + "must not contain point priors" + ) + expect_error( + BayesTools:::.check_prior_list(list(p_wf), allow_prior.weightfunction = FALSE), + "must not contain weightfunction priors" + ) + +}) + + +test_that(".check_and_name_parameters works correctly", { + + # Valid parameters + params <- list(0, 1) + result <- BayesTools:::.check_and_name_parameters(params, c("mean", "sd"), "normal") + expect_equal(names(result), c("mean", "sd")) + + # Named parameters in different order + params2 <- list(sd = 1, mean = 0) + result2 <- BayesTools:::.check_and_name_parameters(params2, c("mean", "sd"), "normal") + expect_equal(result2$mean, 0) + expect_equal(result2$sd, 1) + + # Wrong number of parameters + expect_error( + BayesTools:::.check_and_name_parameters(list(0), c("mean", "sd"), "normal"), + "requires 2 parameters" + ) + + # Invalid parameter names + expect_error( + BayesTools:::.check_and_name_parameters(list(location = 0, sd = 1), c("mean", "sd"), "normal"), + "Parameters 'location' are not supported" + ) + +}) + + +test_that(".check_and_set_truncation works correctly", { + + # Default truncation + result <- BayesTools:::.check_and_set_truncation(list()) + expect_equal(result$lower, -Inf) + expect_equal(result$upper, Inf) + + # Named truncation + result2 <- BayesTools:::.check_and_set_truncation(list(lower = 0)) + expect_equal(result2$lower, 0) + expect_equal(result2$upper, Inf) + + result3 <- BayesTools:::.check_and_set_truncation(list(upper = 1)) + expect_equal(result3$lower, -Inf) + expect_equal(result3$upper, 1) + + # Positional truncation + result4 <- BayesTools:::.check_and_set_truncation(list(0, 1)) + expect_equal(result4$lower, 0) + expect_equal(result4$upper, 1) + + # Single positional (becomes lower) + result5 <- BayesTools:::.check_and_set_truncation(list(0)) + expect_equal(result5$lower, 0) + + # Distribution-specific defaults + result6 <- BayesTools:::.check_and_set_truncation(list(), lower = 0) + expect_equal(result6$lower, 0) + + # Error conditions + expect_error( + BayesTools:::.check_and_set_truncation(list(1, 2, 3)), + "More than two truncation points" + ) + + expect_error( + BayesTools:::.check_and_set_truncation(list(bad_name = 0)), + "must be named 'lower' and 'upper'" + ) + + expect_error( + BayesTools:::.check_and_set_truncation(list(1, 0)), + "lower truncation point must be lower" + ) + + expect_error( + BayesTools:::.check_and_set_truncation(list(-1, Inf), lower = 0), + "Lower truncation point must be larger or equal to 0" + ) + + expect_error( + BayesTools:::.check_and_set_truncation(list(-Inf, 2), upper = 1), + "Upper truncation point must be smaller or equal to 1" + ) + +}) + + +test_that(".check_parameter works correctly", { + + # Valid parameters + expect_null(BayesTools:::.check_parameter(1, "param")) + expect_null(BayesTools:::.check_parameter(c(1, 2, 3), "param", length = 3)) + expect_null(BayesTools:::.check_parameter(c(1, 2), "param", length = 0)) + + # Expressions should pass through + expect_null(BayesTools:::.check_parameter(expression(x + 1), "param")) + + # Invalid parameters + expect_error( + BayesTools:::.check_parameter("a", "param"), + "must be a numeric vector" + ) + + expect_error( + BayesTools:::.check_parameter(c(1, 2), "param", length = 3), + "must be a numeric vector of length 3" + ) + +}) + + +test_that(".check_parameter_dimensions works correctly", { + + # Valid dimensions + expect_null(BayesTools:::.check_parameter_dimensions(3, "K")) + expect_null(BayesTools:::.check_parameter_dimensions(NA, "K", allow_NA = TRUE)) + + # Expressions should pass through + expect_null(BayesTools:::.check_parameter_dimensions(expression(K), "K")) + + # Invalid dimensions + expect_error( + BayesTools:::.check_parameter_dimensions(NA, "K", allow_NA = FALSE), + "must be defined" + ) + + # Note: The function has some implementation quirks with vector input, + # so we just test that invalid inputs throw some error + expect_error(BayesTools:::.check_parameter_dimensions(c(1, 2), "K")) + expect_error(BayesTools:::.check_parameter_dimensions(1.5, "K")) + +}) + + +test_that(".get_prior_factor_levels works correctly", { + + # Treatment contrast - levels - 1 + p_treatment <- prior_factor("normal", contrast = "treatment", list(0, 1)) + attr(p_treatment, "levels") <- 3 + expect_equal(BayesTools:::.get_prior_factor_levels(p_treatment), 2) + + # Independent contrast - all levels + p_independent <- prior_factor("beta", contrast = "independent", list(1, 1)) + attr(p_independent, "levels") <- 3 + expect_equal(BayesTools:::.get_prior_factor_levels(p_independent), 3) + + # Orthonormal contrast - levels - 1 + p_orthonormal <- prior_factor("mnormal", contrast = "orthonormal", list(0, 1)) + attr(p_orthonormal, "levels") <- 4 + expect_equal(BayesTools:::.get_prior_factor_levels(p_orthonormal), 3) + + # Meandif contrast - levels - 1 + p_meandif <- prior_factor("mnormal", contrast = "meandif", list(0, 1)) + attr(p_meandif, "levels") <- 3 + expect_equal(BayesTools:::.get_prior_factor_levels(p_meandif), 2) + +}) + + +test_that(".prior_clean_input_name works correctly", { + + expect_equal(BayesTools:::.prior_clean_input_name("Normal"), "normal") + expect_equal(BayesTools:::.prior_clean_input_name("Log-Normal"), "lognormal") + expect_equal(BayesTools:::.prior_clean_input_name("Student_t"), "studentt") + expect_equal(BayesTools:::.prior_clean_input_name("one.sided"), "onesided") + expect_equal(BayesTools:::.prior_clean_input_name("two-sided"), "twosided") + expect_equal(BayesTools:::.prior_clean_input_name(" Sp ace "), "space") + +}) diff --git a/tests/testthat/test-priors.R b/tests/testthat/test-priors.R index ac60ffed..36529dd9 100644 --- a/tests/testthat/test-priors.R +++ b/tests/testthat/test-priors.R @@ -1,119 +1,33 @@ -context("Prior distribution functions") - -# each test checks that a corresponding prior distribution can be created and the following functions work: -# - random number generator -# - quantile function -# - density function -# - distribution function -# - print function -# - mean and sd functions -test_prior <- function(prior, skip_moments = FALSE){ - set.seed(1) - # tests rng and print function (for plot) - samples <- rng(prior, 100000) - if(is.prior.discrete(prior)){ - barplot(table(samples)/length(samples), main = print(prior, plot = T), width = 1/(max(samples)+1), space = 0, xlim = c(-0.25, max(samples)+0.25)) - }else if(is.prior.spike_and_slab(prior)){ - xh <- hist(samples[samples != 0], breaks = 50, plot = FALSE) - xh$density <- xh$density * mean(samples != 0) - plot(xh, main = print(prior, plot = T), freq = FALSE) - }else{ - hist(samples, main = print(prior, plot = T), breaks = 50, freq = FALSE) - } - # tests density function - lines(prior, individual = TRUE) - - # tests quantile function - if(!is.prior.spike_and_slab(prior) && !is.prior.mixture(prior)){ - abline(v = quant(prior, 0.5), col = "blue", lwd = 2) - } - # tests that pdf(q(x)) == x - if(!is.prior.point(prior) && !is.prior.discrete(prior) && !is.prior.spike_and_slab(prior) && !is.prior.mixture(prior)){ - expect_equal(.25, cdf(prior, quant(prior, 0.25)), tolerance = 1e-4) - expect_equal(.25, ccdf(prior, quant(prior, 0.75)), tolerance = 1e-4) - } - # test mean and sd functions - if(!skip_moments){ - expect_equal(mean(samples), mean(prior), tolerance = 1e-2) - expect_equal(sd(samples), sd(prior), tolerance = 1e-2) - } - return(invisible()) -} -test_weightfunction <- function(prior, skip_moments = FALSE){ - set.seed(1) - # tests rng and print function (for plot) - samples <- rng(prior, 10000) - densities <- density(prior, individual = TRUE) - - if(!all(names(prior$parameters) %in% c("steps", "alpha1", "alpha2"))){ - quantiles <- mquant(prior, 0.5) - } +# ============================================================================ # +# TEST FILE: Prior Distribution Evaluation Tests +# ============================================================================ # +# +# PURPOSE: +# Visual regression tests for prior distribution functions (rng, pdf, cdf, +# quant, mean, sd). Each test validates a distribution type works correctly. +# +# DEPENDENCIES: +# - vdiffr: Visual regression testing +# - common-functions.R: test_prior, test_weightfunction, test_orthonormal, +# test_meandif helper functions +# +# SKIP CONDITIONS: +# - skip_on_os(c("mac", "linux", "solaris")): Multivariate sampling tests +# (orthonormal, meandif priors only) +# - Note: Pure R tests - can run on CRAN +# +# MODELS/FIXTURES: +# - None required (pure prior testing) +# +# TAGS: @evaluation, @visual, @priors +# ============================================================================ # + +# Load test helper functions +source(testthat::test_path("common-functions.R")) + +# File-level skips for visual regression +skip_if_not_installed("vdiffr") - oldpar <- graphics::par(no.readonly = TRUE) - on.exit(graphics::par(mfcol = oldpar[["mfcol"]])) - par(mfcol = c(1, ncol(samples)-1)) - - for(i in 1:(ncol(samples)-1)){ - hist(samples[,i], main = print(prior, plot = T), breaks = 50, freq = FALSE) - lines(densities[[i]]) - if(!all(names(prior$parameters) %in% c("steps", "alpha1", "alpha2"))){ - abline(v = quantiles[i], col = "blue", lwd = 2) - } - if(!grepl("fixed", prior$distribution) & !all(names(prior$parameters) %in% c("steps", "alpha1", "alpha2"))){ - expect_equal(.25, mcdf(prior, mquant(prior, 0.25)[,i])[,i], tolerance = 1e-5) - expect_equal(.25, mccdf.prior(prior, mquant(prior, 0.75)[,i])[,i], tolerance = 1e-5) - } - if(!skip_moments){ - expect_equal(apply(samples, 2, mean), mean(prior), tolerance = 1e-2) - expect_equal(apply(samples, 2, sd), sd(prior) , tolerance = 1e-2) - } - } - return(invisible()) -} -test_orthonormal <- function(prior, skip_moments = FALSE){ - set.seed(1) - # tests rng and print function (for plot) - samples <- rng(prior, 100000) - samples <- samples[abs(samples) < 10] - hist(samples, main = print(prior, plot = T), breaks = 50, freq = FALSE) - # tests density function - lines(prior, individual = TRUE) - # tests quantile function - abline(v = mquant(prior, 0.5), col = "blue", lwd = 2) - # tests that pdf(q(x)) == x - if(!is.prior.point(prior)){ - expect_equal(.25, mcdf(prior, mquant(prior, 0.25)), tolerance = 1e-5) - expect_equal(.25, mccdf(prior, mquant(prior, 0.75)), tolerance = 1e-5) - } - # test mean and sd functions - if(!skip_moments){ - expect_equal(mean(samples), mean(prior), tolerance = 1e-2) - expect_equal(sd(samples), sd(prior), tolerance = 1e-2) - } - return(invisible()) -} -test_meandif <- function(prior, skip_moments = FALSE){ - set.seed(1) - # tests rng and print function (for plot) - samples <- rng(prior, 100000) - samples <- samples[abs(samples) < 10] - hist(samples, main = print(prior, plot = T), breaks = 50, freq = FALSE) - # tests density function - lines(prior, individual = TRUE) - # tests quantile function - abline(v = mquant(prior, 0.5), col = "blue", lwd = 2) - # tests that pdf(q(x)) == x - if(!is.prior.point(prior)){ - expect_equal(.25, mcdf(prior, mquant(prior, 0.25)), tolerance = 1e-5) - expect_equal(.25, mccdf(prior, mquant(prior, 0.75)), tolerance = 1e-5) - } - # test mean and sd functions - if(!skip_moments){ - expect_equal(mean(samples), mean(prior), tolerance = 1e-2) - expect_equal(sd(samples), sd(prior), tolerance = 1e-2) - } - return(invisible()) -} test_that("Normal prior distribution works", { @@ -377,6 +291,39 @@ test_that("Prior mixture distributions work", { vdiffr::expect_doppelganger("prior-mixture-4", function()hist(rng(p4, 10000, transform_factor_samples = FALSE), main = print(p4, plot = T), breaks = 50, freq = FALSE)) vdiffr::expect_doppelganger("prior-mixture-5", function()hist(rng(p4, 10000, transform_factor_samples = TRUE), main = print(p4, plot = T), breaks = 50, freq = FALSE)) + # mixture with none and spikes + p5 <- prior_mixture( + list( + prior_none(), + prior("spike", list(1)), + prior("gamma", list(5, 10)) + ) + ) + p6 <- prior_mixture( + list( + prior_none(), + prior("spike", list(1)), + prior_factor("mnormal", list(0, 1), contrast = "orthonormal") + ), components = c("a", "b", "c") + ) + p7 <- prior_mixture( + list( + prior_none(), + prior("spike", list(1)), + prior_factor("beta", list(3, 1), contrast = "treatment") + ) + ) + for(i in seq_along(p6)){ + p6[[i]]$parameters[["K"]] <- 2 + } + for(i in seq_along(p7)){ + p7[[i]]$parameters[["K"]] <- 2 + } + + vdiffr::expect_doppelganger("prior-mixture-6", function()hist(rng(p5, 10000, transform_factor_samples = FALSE), main = print(p5, plot = T), breaks = 50, freq = FALSE)) + vdiffr::expect_doppelganger("prior-mixture-7", function()hist(rng(p6, 10000, transform_factor_samples = FALSE), main = print(p6, plot = T), breaks = 50, freq = FALSE)) + vdiffr::expect_doppelganger("prior-mixture-8", function()hist(rng(p7, 10000, transform_factor_samples = FALSE), main = print(p7, plot = T), breaks = 50, freq = FALSE)) + }) test_that("Priors with expressions work", { diff --git a/tests/testthat/test-summary-tables-helpers.R b/tests/testthat/test-summary-tables-helpers.R new file mode 100644 index 00000000..9cdc8370 --- /dev/null +++ b/tests/testthat/test-summary-tables-helpers.R @@ -0,0 +1,248 @@ +# ============================================================================ # +# TEST FILE: Summary Tables Helper Functions +# ============================================================================ # +# +# PURPOSE: +# Tests for format_BF, format_estimates, and other summary table +# formatting utilities. +# +# DEPENDENCIES: +# - common-functions.R: test_reference_table, REFERENCE_DIR +# +# SKIP CONDITIONS: +# - None (can run on CRAN - pure R with reference file testing) +# +# TAGS: @evaluation, @summary-tables, @formatting +# ============================================================================ # + +REFERENCE_DIR <<- testthat::test_path("..", "results", "summary-tables-helpers") +source(testthat::test_path("common-functions.R")) + + +test_that("format_BF works correctly", { + + # Basic usage + BF <- format_BF(3.5) + expect_equal(as.numeric(BF), 3.5) + expect_equal(attr(BF, "name"), "BF") + expect_false(attr(BF, "logBF")) + expect_false(attr(BF, "BF01")) + + # With BF01 = TRUE (inverted) + BF_01 <- format_BF(2, BF01 = TRUE) + expect_equal(as.numeric(BF_01), 0.5) + expect_equal(attr(BF_01, "name"), "1/BF") + expect_true(attr(BF_01, "BF01")) + + # With logBF = TRUE + BF_log <- format_BF(exp(2), logBF = TRUE) + expect_equal(as.numeric(BF_log), 2, tolerance = 1e-10) + expect_match(attr(BF_log, "name"), "log\\(BF\\)") + expect_true(attr(BF_log, "logBF")) + + # With inclusion = TRUE + BF_incl <- format_BF(5, inclusion = TRUE) + expect_equal(attr(BF_incl, "name"), "Inclusion BF") + + # With BF01 = TRUE and inclusion = TRUE + BF_excl <- format_BF(5, BF01 = TRUE, inclusion = TRUE) + expect_equal(attr(BF_excl, "name"), "Exclusion BF") + + # Combined logBF and BF01 + BF_both <- format_BF(10, logBF = TRUE, BF01 = TRUE) + expect_equal(as.numeric(BF_both), log(0.1), tolerance = 1e-10) + expect_match(attr(BF_both, "name"), "log\\(1/BF\\)") + + # Vector input with NA - NA_real_ must be part of numeric vector + BF_vec_na <- format_BF(c(1, 2, NA_real_)) + expect_equal(as.numeric(BF_vec_na)[1:2], c(1, 2)) + expect_true(is.na(as.numeric(BF_vec_na)[3])) + + # Vector input + BF_vec <- format_BF(c(1, 2, 3)) + expect_equal(as.numeric(BF_vec), c(1, 2, 3)) + +}) + + +test_that("format_BF input validation works", { + + expect_error(format_BF(-1), "must be equal or higher than 0") + expect_error(format_BF("3"), "must be a numeric") + expect_error(format_BF(3, logBF = "TRUE"), "must be a logical") + expect_error(format_BF(3, BF01 = "TRUE"), "must be a logical") + +}) + + +test_that("add_column works correctly", { + + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + # Create a proper BayesTools_table with 3 columns (needed for middle position test) + test_data <- data.frame( + Mean = c(0.5, 1.2), + Median = c(0.4, 1.1), + SD = c(0.1, 0.2) + ) + rownames(test_data) <- c("mu", "sigma") + class(test_data) <- c("BayesTools_table", "data.frame") + attr(test_data, "type") <- c("estimate", "estimate", "estimate") + attr(test_data, "rownames") <- TRUE + + # Add column at end (default) + result1 <- add_column(test_data, "CI_lower", c(-0.5, 0.8)) + expect_equal(ncol(result1), 4) + expect_true("CI_lower" %in% names(result1)) + expect_s3_class(result1, "BayesTools_table") + expect_equal(attr(result1, "type"), c("estimate", "estimate", "estimate", "estimate")) + test_reference_table(result1, "add_column_end.txt") + + # Add column at specific position + result2 <- add_column(test_data, "CI_lower", c(-0.5, 0.8), column_position = 2) + expect_equal(ncol(result2), 4) + expect_equal(names(result2)[2], "CI_lower") + test_reference_table(result2, "add_column_position2.txt") + + # Add column at position 1 + result3 <- add_column(test_data, "ID", c(1, 2), column_position = 1) + expect_equal(ncol(result3), 4) + expect_equal(names(result3)[1], "ID") + expect_equal(attr(result3, "type")[1], "integer") + test_reference_table(result3, "add_column_position1.txt") + + # With specified column type + result4 <- add_column(test_data, "Prob", c(0.5, 0.8), column_type = "probability") + expect_equal(ncol(result4), 4) + expect_equal(attr(result4, "type")[4], "probability") + test_reference_table(result4, "add_column_probability.txt") + + # Add column with string values (must specify column_type) + result5 <- add_column(test_data, "Category", c("A", "B"), column_type = "string") + expect_equal(ncol(result5), 4) + expect_true("Category" %in% names(result5)) + expect_equal(attr(result5, "type")[4], "string") + test_reference_table(result5, "add_column_string.txt") + +}) + + +test_that("add_column input validation works", { + + # Create a proper BayesTools_table with data (3 columns) + test_data <- data.frame( + Mean = c(0.5, 1.2), + Median = c(0.4, 1.1), + SD = c(0.1, 0.2) + ) + rownames(test_data) <- c("mu", "sigma") + class(test_data) <- c("BayesTools_table", "data.frame") + attr(test_data, "type") <- c("estimate", "estimate", "estimate") + attr(test_data, "rownames") <- TRUE + + # Wrong table class + expect_error(add_column(data.frame(a = 1), "b", 2), "must be of class 'BayesTools_table'") + + # Wrong column_values length + expect_error(add_column(test_data, "new", c(1, 2, 3)), "must be a vector of the same length") + +}) + + +test_that("remove_column works correctly", { + + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + # Create a proper BayesTools_table with data + test_data <- data.frame( + Mean = c(0.5, 1.2), + Median = c(0.4, 1.1), + SD = c(0.1, 0.2) + ) + rownames(test_data) <- c("mu", "sigma") + class(test_data) <- c("BayesTools_table", "data.frame") + attr(test_data, "type") <- c("estimate", "estimate", "estimate") + attr(test_data, "rownames") <- TRUE + + # Remove last column (default) + result0 <- remove_column(test_data) + expect_equal(ncol(result0), 2) + expect_false("SD" %in% names(result0)) + expect_s3_class(result0, "BayesTools_table") + expect_equal(attr(result0, "type"), c("estimate", "estimate")) + test_reference_table(result0, "remove_column_last.txt") + + # Remove by position + result2 <- remove_column(test_data, column_position = 2) + expect_equal(ncol(result2), 2) + expect_false("Median" %in% names(result2)) + test_reference_table(result2, "remove_column_position2.txt") + +}) + + +test_that("remove_column input validation works", { + + # Create a proper BayesTools_table with data + test_data <- data.frame( + Mean = c(0.5, 1.2), + Median = c(0.4, 1.1) + ) + rownames(test_data) <- c("mu", "sigma") + class(test_data) <- c("BayesTools_table", "data.frame") + attr(test_data, "type") <- c("estimate", "estimate") + attr(test_data, "rownames") <- TRUE + + # Wrong table class + expect_error(remove_column(data.frame(a = 1)), "must be of class 'BayesTools_table'") + + # Invalid column position + expect_error(remove_column(test_data, column_position = 10), "'column_position'") + +}) + + +test_that("ensemble_estimates_empty_table works correctly", { + + empty_table <- ensemble_estimates_empty_table() + expect_s3_class(empty_table, "BayesTools_table") + expect_equal(nrow(empty_table), 0) + expect_true(ncol(empty_table) > 0) + test_reference_table(empty_table, "ensemble_estimates_empty.txt") + +}) + + +test_that("ensemble_inference_empty_table works correctly", { + + empty_table <- ensemble_inference_empty_table() + expect_s3_class(empty_table, "BayesTools_table") + expect_equal(nrow(empty_table), 0) + expect_true(ncol(empty_table) > 0) + test_reference_table(empty_table, "ensemble_inference_empty.txt") + +}) + + +test_that("ensemble_summary_empty_table works correctly", { + + empty_table <- ensemble_summary_empty_table() + expect_s3_class(empty_table, "BayesTools_table") + expect_equal(nrow(empty_table), 0) + expect_true(ncol(empty_table) > 0) + test_reference_table(empty_table, "ensemble_summary_empty.txt") + +}) + + +test_that("ensemble_diagnostics_empty_table works correctly", { + + empty_table <- ensemble_diagnostics_empty_table() + expect_s3_class(empty_table, "BayesTools_table") + expect_equal(nrow(empty_table), 0) + expect_true(ncol(empty_table) > 0) + test_reference_table(empty_table, "ensemble_diagnostics_empty.txt") + +}) diff --git a/tests/testthat/test-summary-tables.R b/tests/testthat/test-summary-tables.R index 9c6657ca..7f0ab1d0 100644 --- a/tests/testthat/test-summary-tables.R +++ b/tests/testthat/test-summary-tables.R @@ -1,1463 +1,463 @@ -context("Summary tables functions") +# ============================================================================ # +# TEST FILE: Summary Tables +# ============================================================================ # +# +# PURPOSE: +# Tests for summary table functions including ensemble_estimates_table, +# ensemble_inference_table, ensemble_summary_table, ensemble_diagnostics_table, +# model_summary_table, and print methods. +# +# DEPENDENCIES: +# - rjags, bridgesampling: For tests using pre-fitted models +# - common-functions.R: temp_fits_dir, test_reference_table +# +# SKIP CONDITIONS: +# - skip_if_no_fits(): Pre-fitted models required for most tests +# - skip_if_not_installed("rjags"), skip_if_not_installed("bridgesampling") +# +# MODELS/FIXTURES: +# - fit_summary*, fit_simple_normal, fit_simple_spike, fit_orthonormal_* +# +# TAGS: @evaluation, @summary-tables +# ============================================================================ # + +REFERENCE_DIR <<- testthat::test_path("..", "results", "summary-tables") +source(testthat::test_path("common-functions.R")) + + +# ============================================================================ # +# SECTION 1: ensemble_estimates_table tests +# ============================================================================ # +test_that("ensemble_estimates_table handles matrix posteriors", { + + skip_if_no_fits() + skip_if_not_installed("rjags") + skip_if_not_installed("bridgesampling") + + # Load fits with margliks for creating mixed posteriors + fit_summary0 <- readRDS(file.path(temp_fits_dir, "fit_summary0.RDS")) + marglik_summary0 <- readRDS(file.path(temp_marglik_dir, "fit_summary0.RDS")) + + fit_summary1 <- readRDS(file.path(temp_fits_dir, "fit_summary1.RDS")) + marglik_summary1 <- readRDS(file.path(temp_marglik_dir, "fit_summary1.RDS")) -test_that("Summary tables functions work",{ - - runjags::runjags.options(silent.jags = T, silent.runjags = T) - set.seed(1) - data <- list( - x = rnorm(20, 0, 1), - N = 20 - ) - priors_list0 <- list( - m =prior("normal", list(0, 1)), - omega = prior_none() + models <- list( + list(fit = fit_summary0, marglik = marglik_summary0, prior_weights = 1), + list(fit = fit_summary1, marglik = marglik_summary1, prior_weights = 1) ) - priors_list1 <- list( - m = prior("normal", list(0, .5)), - omega = prior_weightfunction("one.sided", list(c(0.05), c(1, 1))) + + mixed_posteriors <- mix_posteriors( + model_list = models, + parameters = c("m", "omega"), + is_null_list = list("m" = c(FALSE, FALSE), "omega" = c(TRUE, FALSE)), + seed = 1, + n_samples = 1000 ) - priors_list2 <- list( - m = prior("normal", list(0, .3)), - omega = prior_weightfunction("one.sided", list(c(0.05, 0.50), c(1, 1, 1))) + + # Test basic table creation + estimates_table <- ensemble_estimates_table( + mixed_posteriors, + parameters = c("m", "omega") ) - model_syntax <- - "model - { - for(i in 1:N){ - x[i] ~ dnorm(m, 1) - } - }" - log_posterior <- function(parameters, data){ - return(0) - } - fit0 <- JAGS_fit(model_syntax, data, priors_list0, chains = 1, adapt = 100, burnin = 150, sample = 500, seed = 0) - fit1 <- JAGS_fit(model_syntax, data, priors_list1, chains = 1, adapt = 100, burnin = 150, sample = 500, seed = 1) - fit2 <- JAGS_fit(model_syntax, data, priors_list2, chains = 1, adapt = 100, burnin = 150, sample = 500, seed = 1) - marglik0 <- JAGS_bridgesampling(fit0, log_posterior = log_posterior, data = data, prior_list = priors_list0) - marglik1 <- JAGS_bridgesampling(fit1, log_posterior = log_posterior, data = data, prior_list = priors_list1) - marglik2 <- JAGS_bridgesampling(fit2, log_posterior = log_posterior, data = data, prior_list = priors_list2) - models <- list( - list(fit = fit0, marglik = marglik0, prior_weights = 1, fit_summary = runjags_estimates_table(fit0)), - list(fit = fit1, marglik = marglik1, prior_weights = 1, fit_summary = runjags_estimates_table(fit1)), - list(fit = fit2, marglik = marglik2, prior_weights = 1, fit_summary = runjags_estimates_table(fit2)) + + test_reference_table(estimates_table, "ensemble_estimates_basic.txt") + + # Test with custom probs + estimates_table_probs <- ensemble_estimates_table( + mixed_posteriors, + parameters = c("m", "omega"), + probs = c(0.10, 0.50, 0.90) ) - models <- models_inference(models) - inference <- ensemble_inference(model_list = models, parameters = c("m", "omega"), is_null_list = list("m" = 0, "omega" = 1), conditional = FALSE) - mixed_posteriors <- mix_posteriors(model_list = models, parameters = c("m", "omega"), is_null_list = list("m" = 0, "omega" = 1), seed = 1) - - ### checking summary functions - # model summary - model_summary <- model_summary_table(models[[2]]) - expect_equal(model_summary[,1], c("Model ", "Prior prob. ", "log(marglik) ", "Post. prob. ", "Inclusion BF ")) - expect_equal(model_summary[,2], c("2", "0.333", "-0.61", "0.325", "0.964")) - expect_equal(model_summary[,4], c("Parameter prior distributions", "m ~ Normal(0, 0.5)", "omega[one-sided: .05] ~ CumDirichlet(1, 1)", "", "")) - - # runjags summary - runjags_summary <- models[[2]]$fit_summary - expect_equal(colnames(runjags_summary), c("Mean", "SD", "lCI", "Median", "uCI", "MCMC_error", "MCMC_SD_error", "ESS", "R_hat")) - expect_equal(rownames(runjags_summary), c("m", "omega[0,0.05]", "omega[0.05,1]")) - expect_equal(unname(unlist(runjags_summary[1,])), c(0.155080816, 0.197817354, -0.247495448, 0.167295089, 0.496803251, 0.009208408, 0.0466 , 461.4872, NA), tolerance = 1e-4) - - # ensemble estimates - estimates_table <- ensemble_estimates_table(mixed_posteriors, parameters = c("m", "omega"), probs = c(.025, 0.95)) - expect_equal(colnames(estimates_table), c("Mean", "Median", "0.025", "0.95")) - expect_equal(rownames(estimates_table), c("m", "omega[0,0.05]", "omega[0.05,0.5]", "omega[0.5,1]")) - expect_equal(unname(unlist(estimates_table[1,])), c(0.1522389, 0.1519897, -0.2204951, 0.4610624), tolerance = 1e-4) - expect_equal(unname(unlist(estimates_table[3,])), c(0.6794735, 0.7447313, 0.0643561, 1.0000000), tolerance = 1e-4) - - # ensemble inference - inference_table <- ensemble_inference_table(inference, names(inference)) - expect_equal(colnames(inference_table), c("models", "prior_prob", "post_prob", "inclusion_BF")) - expect_equal(rownames(inference_table), c("m", "omega")) - expect_equal(unname(unlist(inference_table[1,])), c(3, 1, 1, Inf)) - expect_equal(unname(unlist(inference_table[2,])), c(2.0000000, 0.6666667, 0.8001882, 2.0023549), tolerance = 1e-4) - - # ensemble summary - summary_table <- ensemble_summary_table(models, c("m", "omega")) - expect_equal(colnames(summary_table), c("Model", "m", "omega", "prior_prob", "marglik", "post_prob", "inclusion_BF")) - expect_equal(unname(as.vector(summary_table[,1])), c(1, 2, 3)) - expect_equal(unname(as.vector(summary_table[,2])), c("Normal(0, 1)", "Normal(0, 0.5)", "Normal(0, 0.3)")) - expect_equal(unname(as.vector(summary_table[,3])), c("", "omega[one-sided: .05] ~ CumDirichlet(1, 1)", "omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1)")) - expect_equal(unname(as.vector(summary_table[,4])), c(0.3333333, 0.3333333, 0.3333333), tolerance = 1e-4) - expect_equal(unname(as.vector(summary_table[,5])), c(-1.1023042, -0.6149897, -0.2365613), tolerance = 1e-4) - expect_equal(unname(as.vector(summary_table[,6])), c(0.1998118, 0.3252813, 0.4749069), tolerance = 1e-4) - expect_equal(unname(as.vector(summary_table[,7])), c(0.4994120, 0.9641984, 1.8088483), tolerance = 1e-4) - - # ensemble diagnostics - diagnostics_table <- ensemble_diagnostics_table(models, c("m", "omega")) - expect_equal(colnames(diagnostics_table), c("Model", "m", "omega", "max_MCMC_error", "max_MCMC_SD_error", "min_ESS", "max_R_hat")) - - expect_equal(unname(as.vector(diagnostics_table[,1])), c(1, 2, 3)) - expect_equal(unname(as.vector(diagnostics_table[,2])), c("Normal(0, 1)", "Normal(0, 0.5)", "Normal(0, 0.3)")) - expect_equal(unname(as.vector(diagnostics_table[,3])), c("", "omega[one-sided: .05] ~ CumDirichlet(1, 1)", "omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1)")) - expect_equal(unname(as.vector(diagnostics_table[,4])), c(0.01019039, 0.01348211, 0.01061287), tolerance = 1e-4) - expect_equal(unname(as.vector(diagnostics_table[,5])), c(0.048, 0.047, 0.045), tolerance = 1e-3) - expect_equal(unname(as.vector(diagnostics_table[,6])), c(434, 461, 500), tolerance = 1e-3) - expect_equal(unname(as.vector(diagnostics_table[,7])), c(NA, NA, NA)) - - - ### test additional settings - # transformations - runjags_summary2t <- runjags_estimates_table(fit2, transformations = list("m" = list(fun = exp))) - expect_equal(exp(as.data.frame(models[[3]]$fit_summary[1,c("lCI","Median","uCI")])), as.data.frame(runjags_summary2t[1,c("lCI","Median","uCI")]), tolerance = 1e-5) - expect_equal(colnames(models[[3]]$fit_summary), colnames(runjags_summary2t)) - expect_equal(rownames(models[[3]]$fit_summary), rownames(runjags_summary2t)) - - ### test an empty tables - runjags_summary_empty <- runjags_estimates_empty_table() - expect_equivalent(nrow(runjags_summary_empty), 0) - expect_equal(colnames(runjags_summary_empty), colnames(runjags_summary)) - expect_equal(capture_output_lines(runjags_summary_empty, width = 150)[1], capture_output_lines(runjags_summary, width = 150)[1]) - - ensemble_estimates_empty <- ensemble_estimates_empty_table() - expect_equivalent(nrow(ensemble_estimates_empty), 0) - expect_equal(colnames(ensemble_estimates_empty), colnames(estimates_table)) - expect_equal(capture_output_lines(ensemble_estimates_empty, width = 150)[1], capture_output_lines(estimates_table, width = 150)[1]) - - ensemble_inference_empty <- ensemble_inference_empty_table() - expect_equivalent(nrow(ensemble_inference_empty), 0) - expect_equal(colnames(ensemble_inference_empty), colnames(inference_table)) - expect_equal(capture_output_lines(ensemble_inference_empty, width = 150)[1], capture_output_lines(inference_table, width = 150)[1]) - - ensemble_summary_table <- ensemble_summary_empty_table() - expect_equivalent(nrow(ensemble_summary_table), 0) - summary_table.trimmed <- remove_column(summary_table, 2) - summary_table.trimmed <- remove_column(summary_table.trimmed, 2) - expect_equal(colnames(ensemble_summary_table), colnames(summary_table.trimmed)) - expect_equal(capture_output_lines(ensemble_summary_table, width = 150)[1], capture_output_lines(summary_table.trimmed, width = 150)[1]) - - ensemble_diagnostics_empty <- ensemble_diagnostics_empty_table() - expect_equivalent(nrow(ensemble_diagnostics_empty), 0) - diagnostics_table.trimmed <- remove_column(diagnostics_table, 2) - diagnostics_table.trimmed <- remove_column(diagnostics_table.trimmed, 2) - expect_equal(colnames(ensemble_diagnostics_empty), colnames(diagnostics_table.trimmed)) - expect_equal(capture_output_lines(ensemble_diagnostics_empty, width = 150)[1], capture_output_lines(diagnostics_table.trimmed, width = 150)[1]) - - model_summary_empty <- model_summary_empty_table() - expect_equivalent(nrow(model_summary_empty), 5) - expect_equal(model_summary_empty[,1], model_summary[,1]) - expect_equal(model_summary_empty[1,4], model_summary[1,4]) - - ### test print functions - expect_equal(capture_output_lines(model_summary, print = TRUE, width = 150), - c(" ", - " Model 2 Parameter prior distributions", - " Prior prob. 0.333 m ~ Normal(0, 0.5) ", - " log(marglik) -0.61 omega[one-sided: .05] ~ CumDirichlet(1, 1)", - " Post. prob. 0.325 ", - " Inclusion BF 0.964 " - )) - expect_equal(capture_output_lines(runjags_summary, print = TRUE, width = 150), - c(" Mean SD lCI Median uCI error(MCMC) error(MCMC)/SD ESS R-hat", - "m 0.155 0.198 -0.247 0.167 0.497 0.00921 0.047 461 NA", - "omega[0,0.05] 1.000 0.000 1.000 1.000 1.000 NA NA NA NA", - "omega[0.05,1] 0.509 0.301 0.028 0.508 0.983 0.01348 0.045 500 NA" - - )) - expect_equal(capture_output_lines(estimates_table, print = TRUE, width = 150), - c(" Mean Median 0.025 0.95", - "m 0.152 0.152 -0.220 0.461", - "omega[0,0.05] 1.000 1.000 1.000 1.000", - "omega[0.05,0.5] 0.679 0.745 0.064 1.000", - "omega[0.5,1] 0.529 0.483 0.023 1.000" - - )) - expect_equal(capture_output_lines(inference_table, print = TRUE, width = 150), - c(" Models Prior prob. Post. prob. Inclusion BF", - "m 3/3 1.000 1.000 Inf", - "omega 2/3 0.667 0.800 2.002" - - )) - expect_equal(capture_output_lines(summary_table, print = TRUE, width = 150), - c(" Model Prior m Prior omega Prior prob. log(marglik) Post. prob. Inclusion BF", - " 1 Normal(0, 1) 0.333 -1.10 0.200 0.499", - " 2 Normal(0, 0.5) omega[one-sided: .05] ~ CumDirichlet(1, 1) 0.333 -0.61 0.325 0.964", - " 3 Normal(0, 0.3) omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1) 0.333 -0.24 0.475 1.809" - )) - expect_equal(capture_output_lines(diagnostics_table, print = TRUE, width = 150), - c(" Model Prior m Prior omega max[error(MCMC)] max[error(MCMC)/SD] min(ESS) max(R-hat)", - " 1 Normal(0, 1) 0.01019 0.048 434 NA", - " 2 Normal(0, 0.5) omega[one-sided: .05] ~ CumDirichlet(1, 1) 0.01348 0.047 461 NA", - " 3 Normal(0, 0.3) omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1) 0.01061 0.045 500 NA" - )) - - - ### test adding columns - expect_error(add_column(runjags_summary, column_title = "New Title", column_values = c(0.2, 0.3, 0.4, 0.5)), - "The 'column_values' must be a vector of the same length as has the table rows.") - expect_error(add_column(runjags_summary, column_title = "New Title", column_values = c(0.2, 0.3, 0.4), column_type = "random text"), - "The 'random text' values are not recognized by the 'column_type' argument.") - expect_error(add_column(runjags_summary, column_title = "New Title", column_values = c(0.2, 0.3, 0.4), column_position = 55), - "The 'column_position' must be equal or lower than ") - expect_error(add_column(data.frame(a = 1:3, b = c("A", "B", "C")), column_title = "New Title", column_values = c(0.2, 0.3, 0.4)), - "The 'table' must be of class 'BayesTools_table'.") - - expect_equal(capture_output_lines( - add_column(runjags_summary, column_title = "New Title", column_values = c(0.2, 0.3, 0.4)), print = TRUE, width = 150), - c(" Mean SD lCI Median uCI error(MCMC) error(MCMC)/SD ESS R-hat New Title", - "m 0.155 0.198 -0.247 0.167 0.497 0.00921 0.047 461 NA 0.200", - "omega[0,0.05] 1.000 0.000 1.000 1.000 1.000 NA NA NA NA 0.300", - "omega[0.05,1] 0.509 0.301 0.028 0.508 0.983 0.01348 0.045 500 NA 0.400" - )) - expect_equal(capture_output_lines( - add_column(estimates_table, column_title = "Models", column_values = c(1:4), column_position = 1), print = TRUE, width = 150), - c(" Models Mean Median 0.025 0.95", - "m 1 0.152 0.152 -0.220 0.461", - "omega[0,0.05] 2 1.000 1.000 1.000 1.000", - "omega[0.05,0.5] 3 0.679 0.745 0.064 1.000", - "omega[0.5,1] 4 0.529 0.483 0.023 1.000" - )) - expect_equal(capture_output_lines( - add_column(inference_table, column_title = "BF2", column_values = inference_table[,4], column_position = 5, column_type = "BF"), print = TRUE, width = 150), - c(" Models Prior prob. Post. prob. Inclusion BF Inclusion BF", - "m 3/3 1.000 1.000 Inf Inf", - "omega 2/3 0.667 0.800 2.002 2.002" - )) - expect_equal(capture_output_lines( - add_column(summary_table, column_title = "Distribution", column_values = c("A", "B", "C"), column_position = 2, column_type = "string"), print = TRUE, width = 150), - c(" Model Distribution Prior m Prior omega Prior prob. log(marglik) Post. prob. Inclusion BF", - " 1 A Normal(0, 1) 0.333 -1.10 0.200 0.499", - " 2 B Normal(0, 0.5) omega[one-sided: .05] ~ CumDirichlet(1, 1) 0.333 -0.61 0.325 0.964", - " 3 C Normal(0, 0.3) omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1) 0.333 -0.24 0.475 1.809" - )) - - - ### test removing columns - expect_error(remove_column(runjags_summary, column_position = 10), - "The 'column_position' must be equal or lower than 9.") - - expect_equal(capture_output_lines( - remove_column(inference_table, column_position = 1), print = TRUE, width = 150), - c(" Prior prob. Post. prob. Inclusion BF", - "m 1.000 1.000 Inf", - "omega 0.667 0.800 2.002" - )) - - - ### test explanatory texts - inference <- ensemble_inference(model_list = models, parameters = c("m", "omega"), is_null_list = list("m" = 0, "omega" = 1), conditional = FALSE) - mixed_posteriors <- mix_posteriors(model_list = models, parameters = c("m", "omega"), is_null_list = list("m" = 0, "omega" = 1), seed = 1) - - expect_equal(interpret(inference, mixed_posteriors, list( - list( - inference = "m", - samples = "m", - inference_name = "effect", - inference_BF_name = "BF_10", - samples_name = "y", - samples_units = NULL - ) - ), "Test"), "Test found strong evidence in favor of the effect, BF_10 = Inf, with mean model-averaged estimate y = 0.152, 95% CI [-0.220, 0.525].") - - inference[["m"]][["BF"]] <- 1/5 - expect_equal(interpret(inference, mixed_posteriors, list( - list( - inference = "m", - samples = "m", - inference_name = "effect", - inference_BF_name = "BF_10", - samples_name = "y", - samples_units = "mm", - samples_conditional = TRUE - ), - list( - inference = "omega", - inference_name = "bias", - inference_BF_name = "BF_pb" - ) - ), "Test2"), "Test2 found moderate evidence against the effect, BF_10 = 0.200, with mean conditional estimate y = 0.152 mm, 95% CI [-0.220, 0.525]. Test2 found weak evidence in favor of the bias, BF_pb = 2.00.") + + test_reference_table(estimates_table_probs, "ensemble_estimates_custom_probs.txt") }) -# skip the rest as it takes too long -skip_on_cran() -test_that("Summary tables functions work (formulas + factors)",{ +test_that("ensemble_estimates_table handles transform_factors", { - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes + skip_if_not_installed("rjags") skip_on_cran() + skip_if_no_fits() - set.seed(1) + # Load orthonormal models with marginal likelihoods + fit_orthonormal_0 <- readRDS(file.path(temp_fits_dir, "fit_orthonormal_0.RDS")) + marglik_orthonormal_0 <- readRDS(file.path(temp_marglik_dir, "fit_orthonormal_0.RDS")) - data_formula <- data.frame( - x_cont1 = rnorm(60), - x_fac2t = factor(rep(c("A", "B"), 30), levels = c("A", "B")), - x_fac3o = factor(rep(c("A", "B", "C"), 20), levels = c("A", "B", "C")), - x_fac3t = factor(rep(c("A", "B", "C"), 20), levels = c("A", "B", "C")) - ) - data <- list( - y = rnorm(60, .4 * data_formula$x_cont1 + ifelse(data_formula$x_fac3t == "A", 0.0, ifelse(data_formula$x_fac3t == "B", -0.2, 0.4)), 1), - N = 60 - ) + fit_orthonormal_1 <- readRDS(file.path(temp_fits_dir, "fit_orthonormal_1.RDS")) + marglik_orthonormal_1 <- readRDS(file.path(temp_marglik_dir, "fit_orthonormal_1.RDS")) - # create model with mix of a formula and free parameters --- - formula_list0 <- list(mu = ~ x_fac2t) - formula_list1 <- list(mu = ~ x_cont1 + x_fac3t) - formula_list2 <- list(mu = ~ x_fac3o) - formula_list3 <- list(mu = ~ x_cont1 * x_fac3o) - - formula_prior_list0 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_fac2t" = prior_factor("normal", contrast = "treatment", list(0, 1)) - ) - ) - formula_prior_list1 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior("normal", list(0, 1)), - "x_fac3t" = prior_factor("normal", contrast = "treatment", list(0, 1)) - ) - ) - formula_prior_list2 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_fac3o" = prior_factor("mnormal", contrast = "orthonormal", list(0, 1)) - ) - ) - formula_prior_list3 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior("normal", list(0, 1)), - "x_fac3o" = prior_factor("mnormal", contrast = "orthonormal", list(0, 1)), - "x_cont1:x_fac3o" = prior_factor("mnormal", contrast = "orthonormal", list(0, 1)) - ) + models <- list( + list(fit = fit_orthonormal_0, marglik = marglik_orthonormal_0, prior_weights = 1), + list(fit = fit_orthonormal_1, marglik = marglik_orthonormal_1, prior_weights = 1) ) - prior_list <- list(sigma = prior("lognormal", list(0, 1))) - formula_data_list <- list(mu = data_formula) + # Get factor parameter names from the model + prior_list <- attr(fit_orthonormal_1, "prior_list") + factor_params <- names(prior_list)[sapply(prior_list, is.prior.factor)] - model_syntax <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" + mixed_posteriors <- mix_posteriors( + model_list = models, + parameters = factor_params, + is_null_list = setNames(list(c(TRUE, FALSE)), factor_params), + seed = 1, + n_samples = 1000 ) - log_posterior <- function(parameters, data){ - sum(stats::dnorm(data$y, parameters[["mu"]], parameters[["sigma"]], log = TRUE)) - } - - fit0 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list0, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0, seed = 1) - fit1 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list1, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1, seed = 2) - fit2 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list2, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list2, seed = 3) - fit3 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list3, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list3, seed = 4) - - marglik0 <- JAGS_bridgesampling( - fit0, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list0, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0) - marglik1 <- JAGS_bridgesampling( - fit1, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list1, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1) - marglik2 <- JAGS_bridgesampling( - fit2, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list2, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list2) - marglik3 <- JAGS_bridgesampling( - fit3, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list3, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list3) - - - # mix posteriors - models <- list( - list(fit = fit0, marglik = marglik0, fit_summary = runjags_estimates_table(fit0), prior_weights = 1), - list(fit = fit1, marglik = marglik1, fit_summary = runjags_estimates_table(fit1), prior_weights = 1), - list(fit = fit2, marglik = marglik2, fit_summary = runjags_estimates_table(fit2), prior_weights = 1), - list(fit = fit3, marglik = marglik3, fit_summary = runjags_estimates_table(fit3), prior_weights = 1) + # Test with transform_factors = TRUE + estimates_table_transform <- ensemble_estimates_table( + mixed_posteriors, + parameters = factor_params, + transform_factors = TRUE ) - models <- models_inference(models) - - inference <- ensemble_inference( - model_list = models, - parameters = c("mu_x_cont1", "mu_x_fac2t", "mu_x_fac3t", "mu_x_fac3o", "mu_x_cont1__xXx__x_fac3o"), - is_null_list = list( - "mu_x_cont1" = c(TRUE, FALSE, TRUE, FALSE), - "mu_x_fac2t" = c(FALSE, TRUE, TRUE, TRUE), - "mu_x_fac2t" = c(TRUE, FALSE, TRUE, TRUE), - "mu_x_fac2t" = c(TRUE, TRUE, FALSE, FALSE), - "mu_x_cont1:x_fac3o" = c(TRUE, TRUE, TRUE, FALSE) - ), - conditional = FALSE) - - mixed_posteriors <- mix_posteriors( - model_list = models, - parameters = c("mu_x_cont1", "mu_x_fac2t", "mu_x_fac3t", "mu_x_fac3o", "mu_x_cont1__xXx__x_fac3o"), - is_null_list = list( - "mu_x_cont1" = c(TRUE, FALSE, TRUE, FALSE), - "mu_x_fac2t" = c(FALSE, TRUE, TRUE, TRUE), - "mu_x_fac2t" = c(TRUE, FALSE, TRUE, TRUE), - "mu_x_fac2t" = c(TRUE, TRUE, FALSE, FALSE), - "mu_x_cont1:x_fac3o" = c(TRUE, TRUE, TRUE, FALSE) - ), - seed = 1, n_samples = 10000) - - - - ### checking summary functions - # model summary - model_summary <- model_summary_table(models[[4]]) - expect_equal(model_summary[,1], c("Model ", "Prior prob. ", "log(marglik) ", "Post. prob. ", "Inclusion BF ", " ")) - expect_equal(model_summary[,4], c("Parameter prior distributions","(mu) intercept ~ Normal(0, 5)","(mu) x_cont1 ~ Normal(0, 1)","(mu) x_fac3o ~ orthonormal contrast: mNormal(0, 1)","(mu) x_cont1:x_fac3o ~ orthonormal contrast: mNormal(0, 1)","sigma ~ Lognormal(0, 1)")) - - model_summary2 <- model_summary_table(models[[4]], formula_prefix = FALSE, remove_parameters = "sigma") - expect_equal(model_summary2[,1], c("Model ", "Prior prob. ", "log(marglik) ", "Post. prob. ", "Inclusion BF ")) - expect_equal(model_summary2[,4], c("Parameter prior distributions","intercept ~ Normal(0, 5)","x_cont1 ~ Normal(0, 1)","x_fac3o ~ orthonormal contrast: mNormal(0, 1)","x_cont1:x_fac3o ~ orthonormal contrast: mNormal(0, 1)")) - - - # runjags summary - runjags_summary <- models[[2]]$fit_summary - expect_equal(colnames(runjags_summary), c("Mean", "SD", "lCI", "Median", "uCI", "MCMC_error", "MCMC_SD_error", "ESS", "R_hat")) - expect_equal(rownames(runjags_summary), c("(mu) intercept", "(mu) x_cont1", "(mu) x_fac3t[B]", "(mu) x_fac3t[C]", "sigma")) - expect_equal(unname(unlist(runjags_summary[3,])), c(5.746362e-03, 2.808364e-01, -5.496105e-01, 1.058318e-02, 5.504860e-01, 4.142589e-03, 1.500000e-02, 4.596000e+03, 1.000580e+00), tolerance = 1e-3) - - runjags_summary2 <- runjags_estimates_table(fit1, formula_prefix = FALSE) - expect_equal(colnames(runjags_summary2), c("Mean", "SD", "lCI", "Median", "uCI", "MCMC_error", "MCMC_SD_error", "ESS", "R_hat")) - expect_equal(rownames(runjags_summary2), c("intercept", "x_cont1", "x_fac3t[B]", "x_fac3t[C]", "sigma")) - expect_equal(unname(unlist(runjags_summary2[3,])), c(5.746362e-03, 2.808364e-01, -5.496105e-01, 1.058318e-02, 5.504860e-01, 4.142589e-03, 1.500000e-02, 4.596000e+03, 1.000580e+00), tolerance = 1e-3) - - runjags_summary <- models[[4]]$fit_summary - expect_equal(colnames(runjags_summary), c("Mean", "SD", "lCI", "Median", "uCI", "MCMC_error", "MCMC_SD_error", "ESS", "R_hat")) - expect_equal(rownames(runjags_summary), c("(mu) intercept", "(mu) x_cont1", "(mu) x_fac3o[1]", "(mu) x_fac3o[2]", "(mu) x_cont1:x_fac3o[1]", "(mu) x_cont1:x_fac3o[2]", "sigma" )) - expect_equal(unname(unlist(runjags_summary[1,])), c(1.876569e-01, 1.210763e-01, -5.091384e-02, 1.878474e-01, 4.285015e-01, 9.894116e-04, 8.000000e-03, 1.497500e+04, 1.000068e+00), tolerance = 1e-3) - - - # ensemble estimates - estimates_table <- ensemble_estimates_table(mixed_posteriors, parameters = c("mu_x_cont1", "mu_x_fac3t", "mu_x_fac3o", "mu_x_cont1__xXx__x_fac3o"), probs = c(.025, 0.95)) - expect_equal(colnames(estimates_table), c("Mean", "Median", "0.025", "0.95")) - expect_equal(rownames(estimates_table), c("(mu) x_cont1", "(mu) x_fac3t[B]", "(mu) x_fac3t[C]", "(mu) x_fac3o[1]", "(mu) x_fac3o[2]", "(mu) x_cont1:x_fac3o[1]", "(mu) x_cont1:x_fac3o[2]")) - expect_equal(unname(unlist(estimates_table[1,])), c(0.1224567, 0.0000000, 0.0000000, 0.4794182), tolerance = 1e-3) - expect_equal(unname(unlist(estimates_table[3,])), c( 0.0397569, 0.0000000, -0.2895047, 0.4087159), tolerance = 1e-3) - expect_equal(unname(unlist(estimates_table[5,])), c(-0.004121766, 0.000000000, -0.215131954, 0.036829714), tolerance = 1e-3) - - estimates_table <- ensemble_estimates_table(mixed_posteriors, parameters = c("mu_x_cont1", "mu_x_fac3o", "mu_x_cont1__xXx__x_fac3o"), probs = c(.025, 0.95)) - expect_equal(colnames(estimates_table), c("Mean", "Median", "0.025", "0.95")) - expect_equal(rownames(estimates_table), c("(mu) x_cont1", "(mu) x_fac3o[1]", "(mu) x_fac3o[2]", "(mu) x_cont1:x_fac3o[1]", "(mu) x_cont1:x_fac3o[2]")) - expect_equal(unname(unlist(estimates_table[1,])), c(0.1224567, 0.0000000, 0.0000000, 0.4794182), tolerance = 1e-3) - expect_equal(unname(unlist(estimates_table[3,])), c(-0.004121766, 0.000000000, -0.215131954, 0.036829714), tolerance = 1e-3) - - # ensemble inference - inference_table <- ensemble_inference_table(inference, names(inference)) - expect_equal(colnames(inference_table), c("models", "prior_prob", "post_prob", "inclusion_BF")) - expect_equal(rownames(inference_table), c("(mu) x_cont1", "(mu) x_fac2t", "(mu) x_fac3t", "(mu) x_fac3o", "(mu) x_cont1:x_fac3o")) - expect_equal(unname(unlist(inference_table[,1])), c(2, 1, 1, 2, 1)) - expect_equal(unname(unlist(inference_table[,2])), c(0.50, 0.25, 0.25, 0.50, 0.25)) - expect_equal(unname(unlist(inference_table[,3])), c(0.37435772, 0.52598137, 0.33962193, 0.13439670, 0.03473579), tolerance = 1e-3) - expect_equal(unname(as.vector(inference_table[,4])), c(0.5983575, 3.3288651, 1.5428523, 0.1552636, 0.1079573), tolerance = 1e-3) - - # ensemble summary - summary_table <- ensemble_summary_table(models, c("mu_x_cont1", "mu_x_fac3o", "mu_x_cont1__xXx__x_fac3o")) - expect_equal(colnames(summary_table), c("Model", "(mu) x_cont1", "(mu) x_fac3o", "(mu) x_cont1:x_fac3o", "prior_prob", "marglik", "post_prob", "inclusion_BF")) - expect_equal(unname(as.vector(summary_table[,1])), c(1, 2, 3, 4)) - expect_equal(unname(as.vector(summary_table[,2])), c("", "Normal(0, 1)", "", "Normal(0, 1)")) - expect_equal(unname(as.vector(summary_table[,3])), c("", "", "orthonormal contrast: mNormal(0, 1)", "orthonormal contrast: mNormal(0, 1)")) - expect_equal(unname(as.vector(summary_table[,4])), c("", "", "", "orthonormal contrast: mNormal(0, 1)")) - expect_equal(unname(as.vector(summary_table[,5])), c(0.25, 0.25, 0.25, 0.25), tolerance = 1e-4) - expect_equal(unname(as.vector(summary_table[,6])), c(-88.22395, -88.66138, -89.88744, -90.94144), tolerance = 1e-3) - expect_equal(unname(as.vector(summary_table[,7])), c(0.52598137, 0.33962193, 0.09966091, 0.03473579), tolerance = 1e-3) - expect_equal(unname(as.vector(summary_table[,8])), c(3.3288651, 1.5428523, 0.3320779, 0.1079573), tolerance = 1e-3) - - # ensemble diagnostics - diagnostics_table <- ensemble_diagnostics_table(models, c("mu_x_cont1", "mu_x_fac3o", "mu_x_cont1__xXx__x_fac3o")) - expect_equal(colnames(diagnostics_table), c("Model", "(mu) x_cont1", "(mu) x_fac3o", "(mu) x_cont1:x_fac3o", "max_MCMC_error", "max_MCMC_SD_error", "min_ESS", "max_R_hat")) - - expect_equal(unname(as.vector(diagnostics_table[,1])), c(1, 2, 3, 4)) - expect_equal(unname(as.vector(diagnostics_table[,2])), c("", "Normal(0, 1)", "", "Normal(0, 1)")) - expect_equal(unname(as.vector(diagnostics_table[,3])), c("", "", "orthonormal contrast: mNormal(0, 1)", "orthonormal contrast: mNormal(0, 1)")) - expect_equal(unname(as.vector(diagnostics_table[,4])), c("", "", "", "orthonormal contrast: mNormal(0, 1)")) - expect_equal(unname(as.vector(diagnostics_table[,5])), c(0.003223670, 0.004142589, 0.001676136, 0.001959310), tolerance = 1e-3) - expect_equal(unname(as.vector(diagnostics_table[,6])), c(0.013, 0.017, 0.011, 0.011), tolerance = 1e-3) - expect_equal(unname(as.vector(diagnostics_table[,7])), c(5559, 3526, 8660, 7969), tolerance = 1e-3) - expect_equal(unname(as.vector(diagnostics_table[,8])), c(1.001154, 1.000955, 1.000125, 1.000658), tolerance = 1e-3) - - - ### test additional settings - # transformations of orthonormal contrast to differences from the mean - runjags_summary_t <- suppressMessages(runjags_estimates_table(fit3, transform_factors = TRUE)) - expect_equal(colnames(runjags_summary_t), c("Mean", "SD", "lCI", "Median", "uCI", "MCMC_error", "MCMC_SD_error", "ESS", "R_hat")) - expect_equal(rownames(runjags_summary_t), c("(mu) intercept","(mu) x_cont1","(mu) x_fac3o [dif: A]","(mu) x_fac3o [dif: B]","(mu) x_fac3o [dif: C]", "(mu) x_cont1:x_fac3o [dif: A]", "(mu) x_cont1:x_fac3o [dif: B]", "(mu) x_cont1:x_fac3o [dif: C]", "sigma" )) - expect_equal(capture_output_lines(runjags_summary_t, print = TRUE, width = 150), - c(" Mean SD lCI Median uCI error(MCMC) error(MCMC)/SD ESS R-hat", - "(mu) intercept 0.188 0.121 -0.051 0.188 0.429 0.00099 0.008 14975 1.000", - "(mu) x_cont1 0.324 0.140 0.047 0.324 0.597 0.00112 0.008 15680 1.000", - "(mu) x_fac3o [dif: A] -0.010 0.168 -0.337 -0.011 0.321 0.00134 0.008 15720 1.000", - "(mu) x_fac3o [dif: B] -0.064 0.170 -0.397 -0.064 0.270 0.00139 0.008 14958 1.000", - "(mu) x_fac3o [dif: C] 0.074 0.167 -0.251 0.072 0.404 0.00133 0.008 15737 1.000", - "(mu) x_cont1:x_fac3o [dif: A] -0.283 0.197 -0.668 -0.283 0.105 0.00158 0.008 15659 1.000", - "(mu) x_cont1:x_fac3o [dif: B] 0.164 0.194 -0.221 0.164 0.539 0.00160 0.008 14777 1.000", - "(mu) x_cont1:x_fac3o [dif: C] 0.119 0.202 -0.275 0.118 0.521 0.00161 0.008 15778 1.000", - "sigma 0.925 0.090 0.770 0.918 1.119 0.00101 0.011 7969 1.001" - )) - - - estimates_table_t <- ensemble_estimates_table(mixed_posteriors, parameters = c("mu_x_cont1", "mu_x_fac3o", "mu_x_cont1__xXx__x_fac3o"), probs = c(.025, 0.95), transform_factors = TRUE) - expect_equal(colnames(estimates_table_t), c("Mean", "Median", "0.025", "0.95")) - expect_equal(rownames(estimates_table_t), c("(mu) x_cont1","(mu) x_fac3o [dif: A]", "(mu) x_fac3o [dif: B]", "(mu) x_fac3o [dif: C]", "(mu) x_cont1:x_fac3o [dif: A]", "(mu) x_cont1:x_fac3o [dif: B]", "(mu) x_cont1:x_fac3o [dif: C]")) - expect_equal(capture_output_lines(estimates_table_t, print = TRUE, width = 150), - c(" Mean Median 0.025 0.95", - "(mu) x_cont1 0.122 0.000 0.000 0.479", - "(mu) x_fac3o [dif: A] -0.003 0.000 -0.176 0.030", - "(mu) x_fac3o [dif: B] -0.003 0.000 -0.181 0.039", - "(mu) x_fac3o [dif: C] 0.007 0.000 -0.105 0.100", - "(mu) x_cont1:x_fac3o [dif: A] -0.010 0.000 -0.183 0.000", - "(mu) x_cont1:x_fac3o [dif: B] 0.006 0.000 0.000 0.000", - "(mu) x_cont1:x_fac3o [dif: C] 0.005 0.000 0.000 0.000" - )) - # transform estimates - runjags_summary_t2 <- suppressMessages(runjags_estimates_table(fit1, transform_factors = FALSE, transformations = list("mu_x_fac2t" = list(fun = exp)))) - expect_equal(capture_output_lines(runjags_summary_t2, print = TRUE, width = 150), - c(" Mean SD lCI Median uCI error(MCMC) error(MCMC)/SD ESS R-hat", - "(mu) intercept 0.145 0.200 -0.245 0.144 0.541 0.00338 0.017 3526 1.001", - "(mu) x_cont1 0.327 0.139 0.052 0.327 0.602 0.00111 0.008 15725 1.000", - "(mu) x_fac3t[B] 0.006 0.281 -0.550 0.011 0.550 0.00415 0.015 4596 1.001", - "(mu) x_fac3t[C] 0.118 0.277 -0.433 0.120 0.656 0.00407 0.015 4630 1.001", - "sigma 0.926 0.089 0.774 0.918 1.117 0.00099 0.011 8016 1.000" - )) - - - ### test print functions - expect_equal(capture_output_lines(model_summary, print = TRUE, width = 150), - c(" ", - " Model 4 Parameter prior distributions", - " Prior prob. 0.250 (mu) intercept ~ Normal(0, 5) ", - " log(marglik) -90.94 (mu) x_cont1 ~ Normal(0, 1) ", - " Post. prob. 0.035 (mu) x_fac3o ~ orthonormal contrast: mNormal(0, 1)", - " Inclusion BF 0.108 (mu) x_cont1:x_fac3o ~ orthonormal contrast: mNormal(0, 1)", - " sigma ~ Lognormal(0, 1) " - )) - expect_equal(capture_output_lines(runjags_summary, print = TRUE, width = 150), - c(" Mean SD lCI Median uCI error(MCMC) error(MCMC)/SD ESS R-hat", - "(mu) intercept 0.188 0.121 -0.051 0.188 0.429 0.00099 0.008 14975 1.000", - "(mu) x_cont1 0.324 0.140 0.047 0.324 0.597 0.00112 0.008 15680 1.000", - "(mu) x_fac3o[1] 0.097 0.207 -0.314 0.096 0.508 0.00166 0.008 15450 1.000", - "(mu) x_fac3o[2] -0.012 0.205 -0.412 -0.013 0.393 0.00164 0.008 15720 1.000", - "(mu) x_cont1:x_fac3o[1] -0.032 0.243 -0.507 -0.033 0.448 0.00196 0.008 15383 1.000", - "(mu) x_cont1:x_fac3o[2] -0.347 0.242 -0.818 -0.347 0.128 0.00193 0.008 15659 1.000", - "sigma 0.925 0.090 0.770 0.918 1.119 0.00101 0.011 7969 1.001" - - )) - expect_equal(capture_output_lines(estimates_table, print = TRUE, width = 150), - c(" Mean Median 0.025 0.95", - "(mu) x_cont1 0.122 0.000 0.000 0.479", - "(mu) x_fac3o[1] 0.007 0.000 -0.145 0.125", - "(mu) x_fac3o[2] -0.004 0.000 -0.215 0.037", - "(mu) x_cont1:x_fac3o[1] -0.001 0.000 0.000 0.000", - "(mu) x_cont1:x_fac3o[2] -0.013 0.000 -0.224 0.000" - - )) - expect_equal(capture_output_lines(inference_table, print = TRUE, width = 150), - c(" Models Prior prob. Post. prob. Inclusion BF", - "(mu) x_cont1 2/4 0.500 0.374 0.598", - "(mu) x_fac2t 1/4 0.250 0.526 3.329", - "(mu) x_fac3t 1/4 0.250 0.340 1.543", - "(mu) x_fac3o 2/4 0.500 0.134 0.155", - "(mu) x_cont1:x_fac3o 1/4 0.250 0.035 0.108" - - )) - expect_equal(capture_output_lines(summary_table, print = TRUE, width = 150), - c(" Model Prior (mu) x_cont1 Prior (mu) x_fac3o Prior (mu) x_cont1:x_fac3o Prior prob. log(marglik) Post. prob. Inclusion BF", - " 1 0.250 -88.22 0.526 3.329", - " 2 Normal(0, 1) 0.250 -88.66 0.340 1.543", - " 3 orthonormal contrast: mNormal(0, 1) 0.250 -89.89 0.100 0.332", - " 4 Normal(0, 1) orthonormal contrast: mNormal(0, 1) orthonormal contrast: mNormal(0, 1) 0.250 -90.94 0.035 0.108" - )) - expect_equal(capture_output_lines(diagnostics_table, print = TRUE, width = 180), - c(" Model Prior (mu) x_cont1 Prior (mu) x_fac3o Prior (mu) x_cont1:x_fac3o max[error(MCMC)] max[error(MCMC)/SD] min(ESS) max(R-hat)", - " 1 0.00323 0.013 5559 1.001", - " 2 Normal(0, 1) 0.00415 0.017 3526 1.001", - " 3 orthonormal contrast: mNormal(0, 1) 0.00168 0.011 8660 1.000", - " 4 Normal(0, 1) orthonormal contrast: mNormal(0, 1) orthonormal contrast: mNormal(0, 1) 0.00196 0.011 7969 1.001" - )) + test_reference_table(estimates_table_transform, "ensemble_estimates_transform_factors.txt") }) -test_that("Summary tables functions work (indepdent factors)",{ - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes +test_that("ensemble_estimates_table handles formula posteriors", { + + skip_if_not_installed("rjags") skip_on_cran() + skip_if_no_fits() - set.seed(1) + # Use orthonormal models (have formulas and marginal likelihoods) + fit_formula <- readRDS(file.path(temp_fits_dir, "fit_orthonormal_0.RDS")) + marglik_formula <- readRDS(file.path(temp_marglik_dir, "fit_orthonormal_0.RDS")) - data_formula <- data.frame( - x_fac2i = factor(rep(c("A", "B"), 30), levels = c("A", "B")) - ) - data <- list( - y = rnorm(60, ifelse(data_formula$x_fac2i == "A", 0.0, -0.2), 1), - N = 60 - ) - - # create model with mix of a formula and free parameters --- - formula_list0 <- list(mu = ~ x_fac2i - 1) - formula_list1 <- list(mu = ~ x_fac2i - 1) + fit_formula2 <- readRDS(file.path(temp_fits_dir, "fit_orthonormal_1.RDS")) + marglik_formula2 <- readRDS(file.path(temp_marglik_dir, "fit_orthonormal_1.RDS")) - formula_prior_list0 <- list( - mu = list( - "x_fac2i" = prior_factor("spike", contrast = "independent", list(0)) - ) - ) - formula_prior_list1 <- list( - mu = list( - "x_fac2i" = prior_factor("normal", contrast = "independent", list(0, 1/4)) - ) + models <- list( + list(fit = fit_formula, marglik = marglik_formula, prior_weights = 1), + list(fit = fit_formula2, marglik = marglik_formula2, prior_weights = 1) ) - prior_list <- list(sigma = prior("lognormal", list(0, 1))) - formula_data_list <- list(mu = data_formula) + prior_list <- attr(fit_formula, "prior_list") + params <- names(prior_list)[!sapply(prior_list, is.null)] - model_syntax <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" + is_null_list <- setNames( + lapply(params, function(p) c(FALSE, FALSE)), + params ) - log_posterior <- function(parameters, data){ - sum(stats::dnorm(data$y, parameters[["mu"]], parameters[["sigma"]], log = TRUE)) - } - - fit0 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list0, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0, seed = 1) - fit1 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list1, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1, seed = 2) - - marglik0 <- JAGS_bridgesampling( - fit0, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list0, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0) - marglik1 <- JAGS_bridgesampling( - fit1, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list1, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1) - - # mix posteriors - models <- list( - list(fit = fit0, marglik = marglik0, fit_summary = runjags_estimates_table(fit0), prior_weights = 1), - list(fit = fit1, marglik = marglik1, fit_summary = runjags_estimates_table(fit1), prior_weights = 1) + mixed_posteriors <- mix_posteriors( + model_list = models, + parameters = params, + is_null_list = is_null_list, + seed = 1, + n_samples = 1000 ) - models <- models_inference(models) + # Test with formula_prefix = TRUE + estimates_prefix_true <- ensemble_estimates_table( + mixed_posteriors, + parameters = params, + formula_prefix = TRUE + ) - inference <- ensemble_inference( - model_list = models, - parameters = c("mu_x_fac2i"), - is_null_list = list( - "mu_x_fac2i" = c(TRUE, FALSE) - ), - conditional = FALSE) + # Test with formula_prefix = FALSE + estimates_prefix_false <- ensemble_estimates_table( + mixed_posteriors, + parameters = params, + formula_prefix = FALSE + ) - mixed_posteriors <- mix_posteriors( - model_list = models, - parameters = c("mu_x_fac2i"), - is_null_list = list( - "mu_x_fac2i" = c(TRUE, FALSE) - ), - seed = 1, n_samples = 10000) - - - - ### checking summary functions - # model summary - model_summary <- model_summary_table(models[[2]]) - expect_equal(model_summary[,1], c("Model ", "Prior prob. ", "log(marglik) ", "Post. prob. ", "Inclusion BF ")) - expect_equal(model_summary[,4], c("Parameter prior distributions", "(mu) x_fac2i ~ independent contrast: Normal(0, 0.25)","sigma ~ Lognormal(0, 1)", "", "")) - - # runjags summary - runjags_summary <- models[[2]]$fit_summary - expect_equal(colnames(runjags_summary), c("Mean", "SD", "lCI", "Median", "uCI", "MCMC_error", "MCMC_SD_error", "ESS", "R_hat")) - expect_equal(rownames(runjags_summary), c("(mu) x_fac2i[A]", "(mu) x_fac2i[B]", "sigma" )) - expect_equal(unname(unlist(runjags_summary[1,])), c(1.734095e-01, 1.340447e-01, -9.293281e-02, 1.747751e-01, 4.347246e-01, 1.067352e-03, 8.000000e-03, 1.577200e+04, 1.000033e+00), tolerance = 1e-3) - - # ensemble estimates - estimates_table <- ensemble_estimates_table(mixed_posteriors, parameters = c("mu_x_fac2i"), probs = c(.025, 0.95)) - expect_equal(colnames(estimates_table), c("Mean", "Median", "0.025", "0.95")) - expect_equal(rownames(estimates_table), c("(mu) x_fac2i[A]", "(mu) x_fac2i[B]")) - expect_equal(unname(unlist(estimates_table[1,])), c(0.10208451, 0.03621004, -0.06041045, 0.35346681), tolerance = 1e-3) - expect_equal(unname(unlist(estimates_table[2,])), c(-0.09355933, -0.01700284, -0.38746858, 0.02836426), tolerance = 1e-3) - - # ensemble inference - inference_table <- ensemble_inference_table(inference, names(inference)) - expect_equal(colnames(inference_table), c("models", "prior_prob", "post_prob", "inclusion_BF")) - expect_equal(rownames(inference_table), c("(mu) x_fac2i")) - expect_equal(unname(unlist(inference_table[,1])), 1) - expect_equal(unname(unlist(inference_table[,2])), 0.5) - expect_equal(unname(unlist(inference_table[,3])), 0.5876797, tolerance = 1e-3) - expect_equal(unname(as.vector(inference_table[,4])), 1.425299, tolerance = 1e-3) - - # ensemble summary - summary_table <- ensemble_summary_table(models, c("mu_x_fac2i")) - expect_equal(colnames(summary_table), c("Model", "(mu) x_fac2i", "prior_prob", "marglik", "post_prob", "inclusion_BF")) - expect_equal(unname(as.vector(summary_table[,1])), c(1, 2)) - expect_equal(unname(as.vector(summary_table[,2])), c("","independent contrast: Normal(0, 0.25)")) - expect_equal(unname(as.vector(summary_table[,3])), c(0.5, 0.5), tolerance = 1e-4) - expect_equal(unname(as.vector(summary_table[,4])), c(-79.15494, -78.80056), tolerance = 1e-3) - expect_equal(unname(as.vector(summary_table[,5])), c(0.4123203, 0.5876797), tolerance = 1e-3) - expect_equal(unname(as.vector(summary_table[,6])), c(0.7016071, 1.4252991), tolerance = 1e-3) - - # ensemble diagnostics - diagnostics_table <- ensemble_diagnostics_table(models, c("mu_x_fac2i"), remove_spike_0 = FALSE) - expect_equal(colnames(diagnostics_table), c("Model", "(mu) x_fac2i", "max_MCMC_error", "max_MCMC_SD_error", "min_ESS", "max_R_hat")) - - expect_equal(unname(as.vector(diagnostics_table[,1])), c(1, 2)) - expect_equal(unname(as.vector(diagnostics_table[,2])), c("independent contrast: Spike(0)","independent contrast: Normal(0, 0.25)")) - expect_equal(unname(as.vector(diagnostics_table[,3])), c(0.0008277888, 0.0010673515), tolerance = 1e-3) - expect_equal(unname(as.vector(diagnostics_table[,4])), c(0.010, 0.011), tolerance = 1e-3) - expect_equal(unname(as.vector(diagnostics_table[,5])), c(9564, 8145), tolerance = 1e-3) + test_reference_table(estimates_prefix_true, "ensemble_estimates_formula_prefix_true.txt") + test_reference_table(estimates_prefix_false, "ensemble_estimates_formula_prefix_false.txt") }) -test_that("Summary tables functions work (meandif factors)",{ - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes - skip_on_cran() +# ============================================================================ # +# SECTION 2: ensemble_inference_table tests +# ============================================================================ # +test_that("ensemble_inference_table handles multiple parameters", { - set.seed(2) + skip_if_not_installed("rjags") + skip_on_cran() + skip_if_no_fits() - data_formula <- data.frame( - x_fac3 = factor(rep(c("A", "B", "C"), 60), levels = c("A", "B", "C")) - ) - data <- list( - y = rnorm(180, ifelse(data_formula$x_fac3 == "A", -0.2, ifelse(data_formula$x_fac3 == "B", 0.0, 0.2)), 1), - N = 180 - ) + fit_summary0 <- readRDS(file.path(temp_fits_dir, "fit_summary0.RDS")) + marglik_summary0 <- readRDS(file.path(temp_marglik_dir, "fit_summary0.RDS")) - # create model with mix of a formula and free parameters --- - formula_list0 <- list(mu = ~ 1) - formula_list1 <- list(mu = ~ x_fac3) + fit_summary1 <- readRDS(file.path(temp_fits_dir, "fit_summary1.RDS")) + marglik_summary1 <- readRDS(file.path(temp_marglik_dir, "fit_summary1.RDS")) - formula_prior_list0 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)) - ) - ) - formula_prior_list1 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_fac3" = prior_factor("mnormal", contrast = "meandif", list(0, 1/5)) - ) + models <- list( + list(fit = fit_summary0, marglik = marglik_summary0, prior_weights = 1), + list(fit = fit_summary1, marglik = marglik_summary1, prior_weights = 1) ) - prior_list <- list(sigma = prior("lognormal", list(0, 1))) - formula_data_list <- list(mu = data_formula) - - model_syntax <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" + inference <- ensemble_inference( + model_list = models, + parameters = c("m", "omega"), + is_null_list = list("m" = c(FALSE, FALSE), "omega" = c(TRUE, FALSE)) ) - log_posterior <- function(parameters, data){ - sum(stats::dnorm(data$y, parameters[["mu"]], parameters[["sigma"]], log = TRUE)) - } - - fit0 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list0, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0, seed = 1) - fit1 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list1, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1, seed = 2) - - marglik0 <- JAGS_bridgesampling( - fit0, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list0, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0) - marglik1 <- JAGS_bridgesampling( - fit1, log_posterior = log_posterior, data = data, prior_list = prior_list, - formula_list = formula_list1, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1) - - # mix posteriors - models <- list( - list(fit = fit0, marglik = marglik0, fit_summary = runjags_estimates_table(fit0), prior_weights = 1), - list(fit = fit1, marglik = marglik1, fit_summary = runjags_estimates_table(fit1), prior_weights = 1) - ) - models <- models_inference(models) + # Basic table + inference_table <- ensemble_inference_table(inference, names(inference)) + test_reference_table(inference_table, "ensemble_inference_basic.txt") + # With logBF + inference_table_log <- ensemble_inference_table(inference, names(inference), logBF = TRUE) + test_reference_table(inference_table_log, "ensemble_inference_logBF.txt") - inference <- ensemble_inference( - model_list = models, - parameters = c("mu_x_fac3"), - is_null_list = list( - "mu_x_fac3" = c(TRUE, FALSE) - ), - conditional = FALSE) + # With BF01 + inference_table_bf01 <- ensemble_inference_table(inference, names(inference), BF01 = TRUE) + test_reference_table(inference_table_bf01, "ensemble_inference_BF01.txt") - mixed_posteriors <- mix_posteriors( - model_list = models, - parameters = c("mu_x_fac3"), - is_null_list = list( - "mu_x_fac3" = c(TRUE, FALSE) - ), - seed = 1, n_samples = 10000) - - - - ### checking summary functions - # model summary - model_summary <- model_summary_table(models[[2]]) - expect_equal(model_summary[,1], c("Model ", "Prior prob. ", "log(marglik) ", "Post. prob. ", "Inclusion BF ")) - expect_equal(model_summary[,4], c("Parameter prior distributions", "(mu) intercept ~ Normal(0, 5)", "(mu) x_fac3 ~ mean difference contrast: mNormal(0, 0.2)","sigma ~ Lognormal(0, 1)", "")) - - # runjags summary - runjags_summary <- models[[2]]$fit_summary - expect_equal(colnames(runjags_summary), c("Mean", "SD", "lCI", "Median", "uCI", "MCMC_error", "MCMC_SD_error", "ESS", "R_hat")) - expect_equal(rownames(runjags_summary), c("(mu) intercept", "(mu) x_fac3[1]", "(mu) x_fac3[2]", "sigma")) - expect_equal(unname(unlist(runjags_summary[1,])), c(2.616574e-02,8.256672e-02,-1.369357e-01,2.621934e-02,1.851191e-01,6.471943e-04,8.000000e-03,1.627600e+04,9.999001e-01), tolerance = 1e-3) - - # ensemble estimates - estimates_table <- suppressMessages(ensemble_estimates_table(mixed_posteriors, parameters = c("mu_x_fac3"), probs = c(.025, 0.95), transform_factors = TRUE)) - expect_equal(colnames(estimates_table), c("Mean", "Median", "0.025", "0.95")) - expect_equal(rownames(estimates_table), c("(mu) x_fac3 [dif: A]", "(mu) x_fac3 [dif: B]", "(mu) x_fac3 [dif: C]")) - expect_equal(unname(unlist(estimates_table[1,])), c(-0.2074503, -0.2206674, -0.4204564, 0.0000000), tolerance = 1e-3) - expect_equal(unname(unlist(estimates_table[2,])), c(0.023169431, 0.008606852, -0.163666847, 0.185934433), tolerance = 1e-3) - expect_equal(unname(unlist(estimates_table[3,])), c(0.1842808, 0.1938991, 0.0000000, 0.3678031), tolerance = 1e-3) - - # ensemble inference - inference_table <- ensemble_inference_table(inference, names(inference)) - expect_equal(colnames(inference_table), c("models", "prior_prob", "post_prob", "inclusion_BF")) - expect_equal(rownames(inference_table), c("(mu) x_fac3")) - expect_equal(unname(unlist(inference_table[,1])), 1) - expect_equal(unname(unlist(inference_table[,2])), 0.5) - expect_equal(unname(unlist(inference_table[,3])), 0.8737537, tolerance = 1e-3) - expect_equal(unname(as.vector(inference_table[,4])), 6.921025, tolerance = 1e-3) - - # ensemble summary - summary_table <- ensemble_summary_table(models, c("mu_x_fac3")) - expect_equal(colnames(summary_table), c("Model", "(mu) x_fac3", "prior_prob", "marglik", "post_prob", "inclusion_BF")) - expect_equal(unname(as.vector(summary_table[,1])), c(1, 2)) - expect_equal(unname(as.vector(summary_table[,2])), c("","mean difference contrast: mNormal(0, 0.2)")) - expect_equal(unname(as.vector(summary_table[,3])), c(0.5, 0.5), tolerance = 1e-4) - expect_equal(unname(as.vector(summary_table[,4])), c(-282.5467, -280.6121), tolerance = 1e-3) - expect_equal(unname(as.vector(summary_table[,5])), c(0.1262463, 0.8737537), tolerance = 1e-3) - expect_equal(unname(as.vector(summary_table[,6])), c(0.1444873, 6.9210254), tolerance = 1e-3) - - # ensemble diagnostics - diagnostics_table <- ensemble_diagnostics_table(models, c("mu_x_fac3"), remove_spike_0 = FALSE) - expect_equal(colnames(diagnostics_table), c("Model", "(mu) x_fac3", "max_MCMC_error", "max_MCMC_SD_error", "min_ESS", "max_R_hat")) - expect_equal(unname(as.vector(diagnostics_table[,1])), c(1, 2)) - expect_equal(unname(as.vector(diagnostics_table[,2])), c("", "mean difference contrast: mNormal(0, 0.2)")) - expect_equal(unname(as.vector(diagnostics_table[,3])), c(0.0006707336, 0.0007978420), tolerance = 1e-3) - expect_equal(unname(as.vector(diagnostics_table[,4])), c(0.01, 0.01), tolerance = 1e-3) - expect_equal(unname(as.vector(diagnostics_table[,5])), c(9676, 9871), tolerance = 1e-3) + # With both + inference_table_both <- ensemble_inference_table(inference, names(inference), logBF = TRUE, BF01 = TRUE) + test_reference_table(inference_table_both, "ensemble_inference_both.txt") }) -test_that("Summary tables functions work (spike and slab priors)",{ - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes +# ============================================================================ # +# SECTION 3: ensemble_summary_table and ensemble_diagnostics_table tests +# ============================================================================ # +test_that("ensemble_summary_table handles different model configurations", { + + skip_if_not_installed("rjags") skip_on_cran() + skip_if_no_fits() - set.seed(1) + # Use models with and without spike-at-zero to test remove_spike_0 + fit_simple_normal <- readRDS(file.path(temp_fits_dir, "fit_simple_normal.RDS")) + marglik_simple_normal <- readRDS(file.path(temp_marglik_dir, "fit_simple_normal.RDS")) - data_formula <- data.frame( - x_cont1 = rnorm(60), - x_fac2t = factor(rep(c("A", "B"), 30), levels = c("A", "B")), - x_fac3o = factor(rep(c("A", "B", "C"), 20), levels = c("A", "B", "C")), - x_fac3t = factor(rep(c("A", "B", "C"), 20), levels = c("A", "B", "C")) - ) - data <- list( - y = rnorm(60, .4 * data_formula$x_cont1 + ifelse(data_formula$x_fac3t == "A", 0.0, ifelse(data_formula$x_fac3t == "B", -0.2, 0.4)), 1), - N = 60 - ) + fit_simple_spike <- readRDS(file.path(temp_fits_dir, "fit_simple_spike.RDS")) + marglik_simple_spike <- readRDS(file.path(temp_marglik_dir, "fit_simple_spike.RDS")) - # create model with mix of a formula and free parameters --- - formula_list0 <- list(mu = ~ x_cont1 + x_fac2t + x_fac3o) - - formula_prior_list0 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_cont1" = prior_spike_and_slab( - prior_parameter = prior("normal", list(0, 0.5)), - prior_inclusion = prior("beta", list(1, 1)) - ), - "x_fac2t" = prior_spike_and_slab( - prior_parameter = prior_factor("normal", contrast = "treatment", list(0, 1)), - prior_inclusion = prior("beta", list(1, 1)) - ), - "x_fac3o" = prior_spike_and_slab( - prior_parameter = prior_factor("mnormal", contrast = "orthonormal", list(0, 1)), - prior_inclusion = prior("spike", list(.5)) - ) - ) + models <- list( + list(fit = fit_simple_normal, marglik = marglik_simple_normal, prior_weights = 1, fit_summary = runjags_estimates_table(fit_simple_normal)), + list(fit = fit_simple_spike, marglik = marglik_simple_spike, prior_weights = 1, fit_summary = runjags_estimates_table(fit_simple_spike)) ) + models <- models_inference(models) + # Test summary table + summary_table <- ensemble_summary_table(models, c("m", "s")) + test_reference_table(summary_table, "ensemble_summary_basic.txt") - prior_list <- list(sigma = prior("lognormal", list(0, 1))) - formula_data_list <- list(mu = data_formula) + # Test with short_name + summary_table_short <- ensemble_summary_table(models, c("m", "s"), short_name = TRUE) + test_reference_table(summary_table_short, "ensemble_summary_short_name.txt") - model_syntax <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) + # Test with logBF and BF01 + summary_table_bf <- ensemble_summary_table(models, c("m", "s"), logBF = TRUE, BF01 = TRUE) + test_reference_table(summary_table_bf, "ensemble_summary_bf_options.txt") - fit0 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list0, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0, seed = 1) + # Test with remove_spike_0 + summary_table_no_spike <- ensemble_summary_table(models, c("m", "s"), remove_spike_0 = FALSE) + test_reference_table(summary_table_no_spike, "ensemble_summary_no_spike.txt") + +}) - # bridge sampling cannot be computer for spike and slab priors - using a dummy value for marglik - marglik0 <- list(logml = 0) - class(marglik0) <- "bridge" - # mix posteriors +test_that("ensemble_summary_table handles parameters as list", { + + skip_if_not_installed("rjags") + skip_on_cran() + skip_if_no_fits() + + fit_simple_normal <- readRDS(file.path(temp_fits_dir, "fit_simple_normal.RDS")) + marglik_simple_normal <- readRDS(file.path(temp_marglik_dir, "fit_simple_normal.RDS")) + + fit_simple_spike <- readRDS(file.path(temp_fits_dir, "fit_simple_spike.RDS")) + marglik_simple_spike <- readRDS(file.path(temp_marglik_dir, "fit_simple_spike.RDS")) + models <- list( - list(fit = fit0, marglik = marglik0, fit_summary = runjags_estimates_table(fit0), prior_weights = 1) + list(fit = fit_simple_normal, marglik = marglik_simple_normal, prior_weights = 1, fit_summary = runjags_estimates_table(fit_simple_normal)), + list(fit = fit_simple_spike, marglik = marglik_simple_spike, prior_weights = 1, fit_summary = runjags_estimates_table(fit_simple_spike)) ) models <- models_inference(models) - ### checking summary functions - # model summary - model_summary <- model_summary_table(models[[1]]) - expect_equal(model_summary[,1], c("Model ", "Prior prob. ", "log(marglik) ", "Post. prob. ", "Inclusion BF ", " ")) - expect_equal(model_summary[,4], c("Parameter prior distributions", "(mu) intercept ~ Normal(0, 5)", "(mu) x_cont1 ~ Normal(0, 0.5) * Beta(1, 1)", "(mu) x_fac2t ~ treatment contrast: Normal(0, 1) * Beta(1, 1)", "(mu) x_fac3o ~ orthonormal contrast: mNormal(0, 1) * Spike(0.5)", "sigma ~ Lognormal(0, 1)")) - - model_estimates <- runjags_estimates_table(fit0) - testthat::expect_equal(capture_output_lines(print(model_estimates), width = 150), c( - " Mean SD lCI Median uCI error(MCMC) error(MCMC)/SD ESS R-hat", - "(mu) intercept 0.194 0.132 -0.072 0.195 0.456 0.00132 0.010 10084 1.000", - "(mu) x_cont1 (inclusion) 0.780 NA NA NA NA NA NA NA NA", - "(mu) x_cont1 0.237 0.174 0.000 0.256 0.555 0.00230 0.013 5720 1.000", - "(mu) x_fac2t (inclusion) 0.186 NA NA NA NA NA NA NA NA", - "(mu) x_fac2t[B] 0.006 0.105 -0.233 0.000 0.299 0.00123 0.012 7310 1.002", - "(mu) x_fac3o (inclusion) 0.040 NA NA NA NA NA NA NA NA", - "(mu) x_fac3o[1] 0.003 0.043 0.000 0.000 0.003 0.00034 0.008 15764 1.001", - "(mu) x_fac3o[2] -0.002 0.043 0.000 0.000 0.000 0.00035 0.008 15506 1.001", - "sigma 0.922 0.088 0.772 0.915 1.113 0.00095 0.011 8458 1.000" - )) - - model_estimates <- suppressMessages(runjags_estimates_table(fit0, transform_factors = TRUE, conditional = TRUE)) - testthat::expect_equal(capture_output_lines(print(model_estimates), width = 150), c( - " Mean SD lCI Median uCI", - "(mu) intercept 0.194 0.132 -0.072 0.195 0.456", - "(mu) x_cont1 (inclusion) 0.780 NA NA NA NA", - "(mu) x_cont1 0.304 0.136 0.033 0.306 0.568", - "(mu) x_fac2t (inclusion) 0.186 NA NA NA NA", - "(mu) x_fac2t[B] 0.033 0.241 -0.435 0.031 0.507", - "(mu) x_fac3o (inclusion) 0.040 NA NA NA NA", - "(mu) x_fac3o [dif: A] -0.036 0.171 -0.359 -0.043 0.316", - "(mu) x_fac3o [dif: B] -0.026 0.173 -0.367 -0.026 0.296", - "(mu) x_fac3o [dif: C] 0.063 0.166 -0.262 0.059 0.395", - "sigma 0.922 0.088 0.772 0.915 1.113" - )) - - model_estimates <- suppressMessages(runjags_estimates_table(fit0, transform_factors = TRUE, conditional = TRUE, remove_inclusion = TRUE)) - testthat::expect_equal(capture_output_lines(print(model_estimates), width = 150), c( - " Mean SD lCI Median uCI", - "(mu) intercept 0.194 0.132 -0.072 0.195 0.456", - "(mu) x_cont1 0.304 0.136 0.033 0.306 0.568", - "(mu) x_fac2t[B] 0.033 0.241 -0.435 0.031 0.507", - "(mu) x_fac3o [dif: A] -0.036 0.171 -0.359 -0.043 0.316", - "(mu) x_fac3o [dif: B] -0.026 0.173 -0.367 -0.026 0.296", - "(mu) x_fac3o [dif: C] 0.063 0.166 -0.262 0.059 0.395", - "sigma 0.922 0.088 0.772 0.915 1.113" - )) - - model_inference <- runjags_inference_table(fit0) - expect_equal(colnames(model_inference), c("prior_prob", "post_prob", "inclusion_BF")) - expect_equal(rownames(model_inference), c("(mu) x_cont1", "(mu) x_fac2t", "(mu) x_fac3o")) - expect_equal(model_inference[,1], c(0.5, 0.5, 0.5)) - expect_equal(model_inference[,2], c(0.7798125, 0.1864375, 0.0399375), tolerance = 1e-3) - expect_equal(model_inference[,3], c(3.54158388, 0.22916187, 0.04159885), tolerance = 1e-3) - - runjags_inference_empty <- runjags_inference_empty_table() - expect_equivalent(nrow(runjags_inference_empty), 0) - expect_equal(colnames(runjags_inference_empty), colnames(model_inference)) - expect_equal(capture_output_lines(runjags_inference_empty, width = 150)[1], capture_output_lines(model_inference, width = 150)[1]) + # Test with parameters supplied as a list + pars <- list("m" = "m", "renamed 2" = "s") + summary_table_list <- ensemble_summary_table(models, pars) + test_reference_table(summary_table_list, "ensemble_summary_params_list.txt") }) -test_that("Summary tables functions work (stan)",{ +test_that("ensemble_diagnostics_table handles different configurations", { + + skip_if_not_installed("rjags") skip_on_cran() - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes + skip_if_no_fits() - # prefitted model with RoBTT - if(!file.exists(file.path("../results/fits", "fit_RoBTT.RDS"))) - skip(message = "Only runs locally") + # Use models with and without spike-at-zero to test remove_spike_0 + fit_simple_normal <- readRDS(file.path(temp_fits_dir, "fit_simple_normal.RDS")) + marglik_simple_normal <- readRDS(file.path(temp_marglik_dir, "fit_simple_normal.RDS")) - fit <- readRDS(file = file.path("../results/fits", "fit_RoBTT.RDS")) + fit_simple_spike <- readRDS(file.path(temp_fits_dir, "fit_simple_spike.RDS")) + marglik_simple_spike <- readRDS(file.path(temp_marglik_dir, "fit_simple_spike.RDS")) - set.seed(1) + models <- list( + list(fit = fit_simple_normal, marglik = marglik_simple_normal, prior_weights = 1, fit_summary = runjags_estimates_table(fit_simple_normal)), + list(fit = fit_simple_spike, marglik = marglik_simple_spike, prior_weights = 1, fit_summary = runjags_estimates_table(fit_simple_spike)) + ) + models <- models_inference(models) + + # Test diagnostics table + diagnostics_table <- ensemble_diagnostics_table(models, c("m", "s")) + test_reference_table(diagnostics_table, "ensemble_diagnostics_basic.txt") - ### checking summary functions - model_estimates <- stan_estimates_table(fit) - expect_equal(colnames(model_estimates), c("Mean", "SD", "lCI", "Median", "uCI", "MCMC_error", "MCMC_SD_error", "ESS", "R_hat")) - expect_equal(rownames(model_estimates), c("mu", "sigma2", "pooled_sigma", "sigma_i[1]", "sigma_i[2]", "mu_i[1]", "mu_i[2]" )) - expect_equal(unname(unlist(model_estimates[1,])), c(1.43876353, 0.37708461, 0.81080656, 1.42486330, 2.15911838, 0.06223762, 0.16504949, 36.70892380, 1.01241771), tolerance = 1e-3) + # Test with short_name + diagnostics_short <- ensemble_diagnostics_table(models, c("m", "s"), short_name = TRUE) + test_reference_table(diagnostics_short, "ensemble_diagnostics_short_name.txt") + + # Test with remove_spike_0 + diagnostics_no_spike <- ensemble_diagnostics_table(models, c("m", "s"), remove_spike_0 = FALSE) + test_reference_table(diagnostics_no_spike, "ensemble_diagnostics_no_spike.txt") }) -test_that("Summary tables functions work (spike factors)",{ - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes +# ============================================================================ # +# SECTION 4: marginal_estimates_table tests +# ============================================================================ # +test_that("marginal_estimates_table handles various inputs", { + + skip_if_not_installed("rjags") skip_on_cran() + # Create sample data for marginal inference testing set.seed(1) - - data_formula <- data.frame( - x_fac3o = factor(rep(c("A", "B", "C"), 100), levels = c("A", "B", "C")) - ) - data <- list( - y = rnorm(300, ifelse(data_formula$x_fac3o == "A", 0.0, ifelse(data_formula$x_fac3o == "B", -0.2, 0.4))), - N = 300 + samples <- list( + mu = rnorm(1000, 0, 1) ) - - formula_list <- list( - mu = ~ x_fac3o - ) - formula_data_list <- list( - mu = data_formula - ) - formula_prior_list0 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_fac3o" = prior_factor("spike", contrast = "meandif", list(0)) - ) - ) - formula_prior_list1 <- list( - mu = list( - "intercept" = prior("normal", list(0, 5)), - "x_fac3o" = prior_factor("mnormal", contrast = "meandif", list(0, 0.25)) - ) + inference <- list( + mu = structure(list( + BF = 2.5, + prior_probs = c(0.5, 0.5), + post_probs = c(0.4, 0.6) + ), class = c("list", "marginal_inference")) ) - prior_list <- list( - sigma = prior("lognormal", list(0, 1)) - ) - model_syntax <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" + attr(inference$mu, "is_null") <- c(TRUE, FALSE) + attr(inference$mu, "prior_list") <- list( + prior("spike", list(0)), + prior("normal", list(0, 1)) ) - fit0 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0) - fit1 <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list, - formula_list = formula_list, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list1) - - log_posterior <- function(parameters, data){ - return(sum(stats::dnorm(data$y, mean = parameters[["mu"]], sd = parameters[["sigma"]], log = TRUE))) - } - - marglik0 <- JAGS_bridgesampling( - fit = fit0, - log_posterior = log_posterior, - data = data, - prior_list = prior_list, - formula_list = formula_list, - formula_data_list = formula_data_list, - formula_prior_list = formula_prior_list0) - marglik1 <- JAGS_bridgesampling( - fit = fit1, - log_posterior = log_posterior, - data = data, - prior_list = prior_list, - formula_list = formula_list, - formula_data_list = formula_data_list, - formula_prior_list = formula_prior_list1) - - - # mix posteriors - models <- list( - list(fit = fit0, marglik = marglik0, fit_summary = suppressMessages(runjags_estimates_table(fit0, remove_spike_0 = FALSE, transform_factors = TRUE)), prior_weights = 1), - list(fit = fit1, marglik = marglik1, fit_summary = suppressMessages(runjags_estimates_table(fit1, remove_spike_0 = FALSE, transform_factors = TRUE)), prior_weights = 1) + marginal_table <- marginal_estimates_table( + samples = samples, + inference = inference, + parameters = "mu" ) - models <- models_inference(models) - inference <- ensemble_inference( - model_list = models, - parameters = c("mu_x_fac3o"), - is_null_list = list( - "mu_x_fac3o" = c(TRUE, FALSE) - ), - conditional = FALSE) + test_reference_table(marginal_table, "marginal_estimates_basic.txt") - mixed_posteriors <- mix_posteriors( - model_list = models, - parameters = c("mu_x_fac3o"), - is_null_list = list( - "mu_x_fac3o" = c(TRUE, FALSE) - ), - seed = 1, n_samples = 10000) - - - ### checking summary functions - # model summary - model_summary <- model_summary_table(models[[1]], remove_spike_0 = FALSE) - expect_equal(model_summary[,1], c("Model ", "Prior prob. ", "log(marglik) ", "Post. prob. ", "Inclusion BF ")) - expect_equal(model_summary[,4], c("Parameter prior distributions", "(mu) intercept ~ Normal(0, 5)", "(mu) x_fac3o ~ mean difference contrast: mSpike(0)", "sigma ~ Lognormal(0, 1)", "")) - - # runjags summary - runjags_summary <- models[[1]]$fit_summary - expect_equal(colnames(runjags_summary), c("Mean", "SD", "lCI", "Median", "uCI", "MCMC_error", "MCMC_SD_error", "ESS", "R_hat")) - expect_equal(rownames(runjags_summary), c("(mu) intercept", "(mu) x_fac3o [dif: A]", "(mu) x_fac3o [dif: B]", "(mu) x_fac3o [dif: C]", "sigma")) - expect_equal(unname(unlist(runjags_summary[,1])), c(0.09974883, 0.00000000, 0.00000000, 0.00000000, 0.97359248), tolerance = 1e-3) - - # ensemble estimates - estimates_table <- ensemble_estimates_table(mixed_posteriors, parameters = c("mu_x_fac3o"), probs = c(.025, 0.95), transform_factors = TRUE) - expect_equal(colnames(estimates_table), c("Mean", "Median", "0.025", "0.95")) - expect_equal(rownames(estimates_table), c("(mu) x_fac3o [dif: A]", "(mu) x_fac3o [dif: B]", "(mu) x_fac3o [dif: C]")) - expect_equal(unname(unlist(estimates_table[1,])), c(-0.00919489, 0.00000000, -0.15024720, 0.09922589), tolerance = 1e-3) - expect_equal(unname(unlist(estimates_table[2,])), c(-0.1246629, -0.1329689, -0.3041710, 0.0000000), tolerance = 1e-3) - expect_equal(unname(unlist(estimates_table[3,])), c(0.1338578, 0.1465136, 0.0000000, 0.2895046), tolerance = 1e-3) - - # ensemble inference - inference_table <- ensemble_inference_table(inference, names(inference)) - expect_equal(colnames(inference_table), c("models", "prior_prob", "post_prob", "inclusion_BF")) - expect_equal(rownames(inference_table), c("(mu) x_fac3o")) - expect_equal(unname(unlist(inference_table[,1])), 1) - expect_equal(unname(unlist(inference_table[,2])), 0.5) - expect_equal(unname(unlist(inference_table[,3])), 0.751, tolerance = 1e-3) - expect_equal(unname(as.vector(inference_table[,4])), 3.020, tolerance = 1e-3) - - # ensemble summary - summary_table <- ensemble_summary_table(models, c("mu_x_fac3o"), remove_spike_0 = FALSE) - expect_equal(colnames(summary_table), c("Model", "(mu) x_fac3o", "prior_prob", "marglik", "post_prob", "inclusion_BF")) - expect_equal(unname(as.vector(summary_table[,1])), c(1, 2)) - expect_equal(unname(as.vector(summary_table[,2])), c("mean difference contrast: mSpike(0)","mean difference contrast: mNormal(0, 0.25)")) - expect_equal(unname(as.vector(summary_table[,3])), c(0.5, 0.5), tolerance = 1e-4) - expect_equal(unname(as.vector(summary_table[,4])), c(-424.1119, -423.0067), tolerance = 1e-3) - expect_equal(unname(as.vector(summary_table[,5])), c(0.2487827, 0.7512173), tolerance = 1e-3) - expect_equal(unname(as.vector(summary_table[,6])), c(0.3311728, 3.0195717), tolerance = 1e-3) - - # ensemble diagnostics - diagnostics_table <- ensemble_diagnostics_table(models, c("mu_x_fac3o"), remove_spike_0 = FALSE) - expect_equal(colnames(diagnostics_table), c("Model", "(mu) x_fac3o", "max_MCMC_error", "max_MCMC_SD_error", "min_ESS", "max_R_hat")) - expect_equal(unname(as.vector(diagnostics_table[,1])), c(1, 2)) - expect_equal(unname(as.vector(diagnostics_table[,2])), c("mean difference contrast: mSpike(0)", "mean difference contrast: mNormal(0, 0.25)")) - expect_equal(unname(as.vector(diagnostics_table[,3])), c(0.0004365069, 0.0006020573), tolerance = 1e-3) - expect_equal(unname(as.vector(diagnostics_table[,4])), c(0.01, 0.01), tolerance = 1e-3) - expect_equal(unname(as.vector(diagnostics_table[,5])), c(0, 10554), tolerance = 1e-3) + # With logBF + marginal_table_log <- marginal_estimates_table( + samples = samples, + inference = inference, + parameters = "mu", + logBF = TRUE + ) + test_reference_table(marginal_table_log, "marginal_estimates_logBF.txt") + + # With BF01 + marginal_table_bf01 <- marginal_estimates_table( + samples = samples, + inference = inference, + parameters = "mu", + BF01 = TRUE + ) + test_reference_table(marginal_table_bf01, "marginal_estimates_BF01.txt") }) -test_that("Summary tables functions work (mixture priors)",{ - skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes +# ============================================================================ # +# SECTION 5: model_summary_table tests +# ============================================================================ # +test_that("model_summary_table handles various configurations", { + + skip_if_not_installed("rjags") skip_on_cran() + skip_if_no_fits() - set.seed(1) + # Use model with spike-at-zero to test remove_spike_0 + fit_simple_spike <- readRDS(file.path(temp_fits_dir, "fit_simple_spike.RDS")) + marglik_simple_spike <- readRDS(file.path(temp_marglik_dir, "fit_simple_spike.RDS")) - data_formula <- data.frame( - x_cont1 = rnorm(300), - x_fac2t = factor(rep(c("A", "B"), 150), levels = c("A", "B")), - x_fac3t = factor(rep(c("A", "B", "C"), 100), levels = c("A", "B", "C")) - ) - data <- list( - y = rnorm(300, -0.15 + 0.20 * data_formula$x_cont1 + ifelse(data_formula$x_fac3t == "A", 0.0, ifelse(data_formula$x_fac3t == "B", -0.2, 0.2)), ifelse(data_formula$x_fac2t == "A", 0.5, 1)), - N = 300 + model <- list( + fit = fit_simple_spike, + marglik = marglik_simple_spike, + prior_weights = 1, + fit_summary = runjags_estimates_table(fit_simple_spike) ) + model_list <- list(model) + model_list <- models_inference(model_list) - # create model with mix of a formula and free parameters --- - formula_list1 <- list( - mu = ~ x_cont1 + x_fac3t - ) - formula_data_list1 <- list( - mu = data_formula - ) - formula_prior_list1 <- list( - mu = list( - "intercept" = prior_mixture( - list( - prior("spike", list(0), prior_weights = 2), - prior("normal", list(-1, 0.5), prior_weights = 1), - prior("normal", list( 1, 0.5), prior_weights = 1) - ), - is_null = c(T, F, F) - ), - "x_cont1" = prior_mixture( - list( - prior("spike", list(0), prior_weights = 1), - prior("normal", list(0, 1), prior_weights = 1) - ), - is_null = c(T, F) - ), - "x_fac3t" = prior_spike_and_slab(prior_factor("mnormal", list(0, 1), contrast = "orthonormal"), - prior_inclusion = prior("spike", list(0.5))) - ) - ) - attr(formula_prior_list1$mu$x_cont1, "multiply_by") <- "sigma" - prior_list1 <- list( - "sigma" = prior_mixture( - list( - prior("normal", list(0, 1), truncation = list(0, Inf)), - prior("lognormal", list(0, 1)) - ), - components = c("normal", "lognormal") - ), - "bias" = prior_mixture(list( - prior_none(prior_weights = 1), - prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/3), - prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/3), - prior_PET("normal", list(0, 1), prior_weights = 1/3) - ), is_null = c(T, F, F, F)) - ) - model_syntax1 <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n", - "}\n", - "}" - ) + # Basic model summary + summary_table <- model_summary_table(model_list[[1]]) + test_reference_table(summary_table, "model_summary_basic.txt") - if("RoBMA" %in% rownames(installed.packages())){ - require("RoBMA") - }else{ - skip() - } + # With short_name + summary_short <- model_summary_table(model_list[[1]], short_name = TRUE) + test_reference_table(summary_short, "model_summary_short_name.txt") - fit1 <- JAGS_fit( - model_syntax = model_syntax1, data = data, prior_list = prior_list1, - formula_list = formula_list1, formula_data_list = formula_data_list1, formula_prior_list = formula_prior_list1) + # With remove_spike_0 (should remove 'm' which has spike at zero) + summary_no_spike <- model_summary_table(model_list[[1]], remove_spike_0 = TRUE) + test_reference_table(summary_no_spike, "model_summary_no_spike.txt") - # bridge sampling cannot be computer for spike and slab priors - using a dummy value for marglik - marglik1 <- list(logml = 0) - class(marglik1) <- "bridge" +}) - # mix posteriors - models <- list( - list(fit = fit1, marglik = marglik1, fit_summary = runjags_estimates_table(fit1), prior_weights = 1) - ) - models <- models_inference(models) - ### checking summary functions - # model summary - model_summary <- model_summary_table(models[[1]], short_name = TRUE) - expect_equal(model_summary[,1], c("Model ", "Prior prob. ", "log(marglik) ", "Post. prob. ", "Inclusion BF ", " ")) - expect_equal(model_summary[,4], c( - "Parameter prior distributions", - "(mu) intercept ~ (2/4) * S(0) + (1/4) * N(-1, 0.5) + (1/4) * N(1, 0.5)", - "(mu) x_cont1 ~ (1/2) * S(0) + (1/2) * N(0, 1)", - "(mu) x_fac3t ~ orthonormal contrast: mN(0, 1) * S(0.5)", - "sigma ~ (1/2) * N(0, 1)[0, Inf] + (1/2) * Ln(0, 1)", - "bias ~ (1/2) * None + (0.33/2) * omega[2s: .05] ~ CumD(1, 1) + (0.33/2) * omega[1s: .05, .025] ~ CumD(1, 1, 1) + (0.33/2) * PET ~ N(0, 1)[0, Inf]" - )) - - model_estimates <- runjags_estimates_table(fit1) - expect_equal(capture_output_lines(print(model_estimates), width = 150), c( - " Mean SD lCI Median uCI error(MCMC) error(MCMC)/SD ESS R-hat", - "(mu) intercept (inclusion) 0.596 NA NA NA NA NA NA NA NA", - "(mu) intercept -0.087 0.080 -0.226 -0.098 0.000 0.00246 0.031 1067 1.002", - "(mu) x_cont1 (inclusion) 0.999 NA NA NA NA NA NA NA NA", - "(mu) x_cont1 0.279 0.063 0.154 0.280 0.401 0.00063 0.010 11015 1.000", - "(mu) x_fac3t (inclusion) 0.855 NA NA NA NA NA NA NA NA", - "(mu) x_fac3t[1] 0.252 0.128 0.000 0.277 0.448 0.00467 0.037 939 1.016", - "(mu) x_fac3t[2] -0.012 0.074 -0.167 0.000 0.137 0.00057 0.008 17039 1.001", - "sigma (inclusion: normal) 0.510 NA NA NA NA NA NA NA NA", - "sigma (inclusion: lognormal) 0.490 NA NA NA NA NA NA NA NA", - "sigma 0.803 0.034 0.740 0.802 0.874 0.00039 0.011 7753 1.000", - "bias (inclusion) 0.497 NA NA NA NA NA NA NA NA", - "PET 0.130 0.377 0.000 0.000 1.410 0.00292 0.008 16826 1.000", - "omega[0,0.025] 1.000 0.000 1.000 1.000 1.000 NA NA NA NA", - "omega[0.025,0.05] 0.865 0.248 0.139 1.000 1.000 0.00196 0.008 16000 1.000", - "omega[0.05,0.975] 0.809 0.316 0.053 1.000 1.000 0.00247 0.008 16361 1.000", - "omega[0.975,1] 0.889 0.267 0.076 1.000 1.000 0.00211 0.008 16128 1.000" - )) - - model_estimates <- suppressMessages(runjags_estimates_table(fit1, transform_factors = TRUE, conditional = TRUE)) - expect_equal(capture_output_lines(print(model_estimates), width = 150), c( - " Mean SD lCI Median uCI", - "(mu) intercept (inclusion) 0.596 NA NA NA NA", - "(mu) intercept -0.145 0.047 -0.238 -0.146 -0.052", - "(mu) x_cont1 (inclusion) 0.999 NA NA NA NA", - "(mu) x_cont1 0.279 0.062 0.155 0.280 0.401", - "(mu) x_fac3t (inclusion) 0.855 NA NA NA NA", - "(mu) x_fac3t [dif: A] -0.012 0.066 -0.141 -0.012 0.116", - "(mu) x_fac3t [dif: B] -0.203 0.066 -0.333 -0.202 -0.074", - "(mu) x_fac3t [dif: C] 0.214 0.065 0.088 0.214 0.341", - "sigma (inclusion: normal) 0.510 NA NA NA NA", - "sigma (inclusion: lognormal) 0.490 NA NA NA NA", - "sigma[normal] 0.804 0.034 0.740 0.802 0.872", - "sigma[lognormal] 0.803 0.034 0.740 0.802 0.875", - "bias (inclusion) 0.497 NA NA NA NA", - "PET 0.780 0.589 0.031 0.656 2.113", - "omega[0,0.025] 1.000 0.000 1.000 1.000 1.000", - "omega[0.025,0.05] 0.592 0.275 0.048 0.627 0.984", - "omega[0.05,0.975] 0.421 0.279 0.017 0.386 0.953", - "omega[0.975,1] 0.663 0.374 0.027 0.916 1.000" - )) - - model_estimates <- runjags_estimates_table(fit1, transform_factors = TRUE, conditional = TRUE, remove_inclusion = TRUE) - expect_equal(capture_output_lines(print(model_estimates), width = 150), c( - " Mean SD lCI Median uCI", - "(mu) intercept -0.145 0.047 -0.238 -0.146 -0.052", - "(mu) x_cont1 0.279 0.062 0.155 0.280 0.401", - "(mu) x_fac3t [dif: A] -0.012 0.066 -0.141 -0.012 0.116", - "(mu) x_fac3t [dif: B] -0.203 0.066 -0.333 -0.202 -0.074", - "(mu) x_fac3t [dif: C] 0.214 0.065 0.088 0.214 0.341", - "sigma[normal] 0.804 0.034 0.740 0.802 0.872", - "sigma[lognormal] 0.803 0.034 0.740 0.802 0.875", - "PET 0.780 0.589 0.031 0.656 2.113", - "omega[0,0.025] 1.000 0.000 1.000 1.000 1.000", - "omega[0.025,0.05] 0.592 0.275 0.048 0.627 0.984", - "omega[0.05,0.975] 0.421 0.279 0.017 0.386 0.953", - "omega[0.975,1] 0.663 0.374 0.027 0.916 1.000" - )) - - model_estimates <- runjags_estimates_table(fit1, transformations = list( - "mu_intercept" = list(fun = exp), - "mu_x_cont1" = list(fun = exp), - "sigma" = list(fun = exp), - "PET" = list(fun = exp) - )) - expect_equal(capture_output_lines(print(model_estimates), width = 150), c( - " Mean SD lCI Median uCI error(MCMC) error(MCMC)/SD ESS R-hat", - "(mu) intercept (inclusion) 0.596 NA NA NA NA NA NA NA NA", - "(mu) intercept 0.920 0.073 0.798 0.907 1.000 0.00225 0.031 1061 1.002", - "(mu) x_cont1 (inclusion) 0.999 NA NA NA NA NA NA NA NA", - "(mu) x_cont1 1.324 0.083 1.166 1.323 1.494 0.00081 0.010 11242 1.000", - "(mu) x_fac3t (inclusion) 0.855 NA NA NA NA NA NA NA NA", - "(mu) x_fac3t[1] 0.252 0.128 0.000 0.277 0.448 0.00467 0.037 939 1.016", - "(mu) x_fac3t[2] -0.012 0.074 -0.167 0.000 0.137 0.00057 0.008 17039 1.001", - "sigma (inclusion: normal) 0.510 NA NA NA NA NA NA NA NA", - "sigma (inclusion: lognormal) 0.490 NA NA NA NA NA NA NA NA", - "sigma 2.235 0.077 2.097 2.231 2.395 0.00088 0.011 7722 1.000", - "bias (inclusion) 0.497 NA NA NA NA NA NA NA NA", - "PET 1.288 1.525 1.000 1.000 4.095 0.01205 0.008 16030 1.093", - "omega[0,0.025] 1.000 0.000 1.000 1.000 1.000 NA NA NA NA", - "omega[0.025,0.05] 0.865 0.248 0.139 1.000 1.000 0.00196 0.008 16000 1.000", - "omega[0.05,0.975] 0.809 0.316 0.053 1.000 1.000 0.00247 0.008 16361 1.000", - "omega[0.975,1] 0.889 0.267 0.076 1.000 1.000 0.00211 0.008 16128 1.000" - )) - - model_estimates <- runjags_estimates_table(fit1, conditional = TRUE, remove_inclusion = TRUE, transformations = list( - "mu_intercept" = list(fun = exp), - "mu_x_cont1" = list(fun = exp), - "sigma" = list(fun = exp), - "PET" = list(fun = exp) - )) - expect_equal(capture_output_lines(print(model_estimates), width = 150), c( - " Mean SD lCI Median uCI", - "(mu) intercept 0.866 0.041 0.788 0.864 0.949", - "(mu) x_cont1 1.325 0.083 1.168 1.323 1.494", - "(mu) x_fac3t[1] 0.295 0.081 0.138 0.295 0.454", - "(mu) x_fac3t[2] -0.014 0.080 -0.173 -0.015 0.142", - "sigma[normal] 2.235 0.077 2.097 2.231 2.392", - "sigma[lognormal] 2.234 0.077 2.097 2.231 2.400", - "PET 2.726 3.384 1.032 1.927 8.272", - "omega[0,0.025] 1.000 0.000 1.000 1.000 1.000", - "omega[0.025,0.05] 0.592 0.275 0.048 0.627 0.984", - "omega[0.05,0.975] 0.421 0.279 0.017 0.386 0.953", - "omega[0.975,1] 0.663 0.374 0.027 0.916 1.000" - )) - - model_inference <- runjags_inference_table(fit1) - expect_equal(capture_output_lines(print(model_inference), width = 150), c( - " Prior prob. Post. prob. Inclusion BF", - "(mu) intercept 0.500 0.596 1.478", - "(mu) x_cont1 0.500 0.999 841.105", - "(mu) x_fac3t 0.500 0.855 5.894", - "sigma [normal] 0.500 0.510 1.041", - "sigma [lognormal] 0.500 0.490 0.961", - "bias 0.500 0.497 0.989" - )) - - model_inference <- update(model_inference, title = "Table 1", footnotes = c("Footnote 1", "Footnote 2"), logBF = TRUE) - expect_equal(capture_output_lines(print(model_inference), width = 150), c( - "Table 1" , - " Prior prob. Post. prob. log(Inclusion BF)", - "(mu) intercept 0.500 0.596 0.391", - "(mu) x_cont1 0.500 0.999 6.735", - "(mu) x_fac3t 0.500 0.855 1.774", - "sigma [normal] 0.500 0.510 0.040", - "sigma [lognormal] 0.500 0.490 -0.040", - "bias 0.500 0.497 -0.011", - "Footnote 1" , - "Footnote 2" )) -}) +# ============================================================================ # +# SECTION 6: update.BayesTools_table tests +# ============================================================================ # +test_that("update.BayesTools_table works correctly", { -test_that("Summary tables odd cases",{ + skip_if_not_installed("rjags") + skip_on_cran() + skip_if_no_fits() - set.seed(1) + fit_summary0 <- readRDS(file.path(temp_fits_dir, "fit_summary0.RDS")) + marglik_summary0 <- readRDS(file.path(temp_marglik_dir, "fit_summary0.RDS")) - data <- list( - y = rnorm(10), - N = 10 - ) + fit_summary1 <- readRDS(file.path(temp_fits_dir, "fit_summary1.RDS")) + marglik_summary1 <- readRDS(file.path(temp_marglik_dir, "fit_summary1.RDS")) - prior_list <- list( - "mu" = prior_mixture( - list(prior("spike", list(0))), - is_null = c(FALSE) - ), - "sigma" = prior_mixture( - list(prior("spike", list(1))), - is_null = c(TRUE) - ), - "beta" = prior("normal", list(0, 1)) - ) - model_syntax <- paste0( - "model{\n", - "for(i in 1:N){\n", - " y[i] ~ dnorm(mu, 1/pow(sigma, 2))\n", - "}\n", - "}" + models <- list( + list(fit = fit_summary0, marglik = marglik_summary0, prior_weights = 1, fit_summary = runjags_estimates_table(fit_summary0)), + list(fit = fit_summary1, marglik = marglik_summary1, prior_weights = 1, fit_summary = runjags_estimates_table(fit_summary1)) ) + models <- models_inference(models) - fit <- JAGS_fit( - model_syntax = model_syntax, data = data, prior_list = prior_list + inference <- ensemble_inference( + model_list = models, + parameters = c("m", "omega"), + is_null_list = list("m" = c(FALSE, FALSE), "omega" = c(TRUE, FALSE)) ) - expect_equal(capture_output_lines(print(runjags_estimates_table(fit)), width = 150), c( - " Mean SD lCI Median uCI error(MCMC) error(MCMC)/SD ESS R-hat", - "mu (inclusion) 1.000 NA NA NA NA NA NA NA NA", - "mu 0.000 0.000 0.000 0.000 0.000 0.00000 NA 0 NA", - "sigma (inclusion) 0.000 NA NA NA NA NA NA NA NA", - "sigma 1.000 0.000 1.000 1.000 1.000 0.00000 NA 0 NA", - "beta -0.014 0.999 -1.985 -0.003 1.930 0.00805 0.008 15412 1.000" - )) - - expect_equal(capture_output_lines(print(runjags_estimates_table(fit, conditional = TRUE)), width = 150), c( - " Mean SD lCI Median uCI", - "mu (inclusion) 1.000 NA NA NA NA", - "mu 0.000 0.000 0.000 0.000 0.000", - "sigma (inclusion) 0.000 NA NA NA NA", - "sigma NaN NA NA NA NA", - "beta -0.014 0.999 -1.985 -0.003 1.930", - "\033[0;31mConditional summary for sigma parameter could not be computed due to no posterior samples.\033[0m" - )) - - expect_equal(capture_output_lines(print(runjags_inference_table(fit)), width = 150), c( - " Prior prob. Post. prob. Inclusion BF", - "mu 1.000 1.000 Inf", - "sigma 0.000 0.000 0.000" - )) + # Create inference table + inference_table <- ensemble_inference_table(inference, names(inference)) -}) + # Update with new title + updated_table <- update(inference_table, title = "Updated Title") + test_reference_table(updated_table, "update_table_new_title.txt") -test_that("Simplified interpret2 function", { + # Update with footnotes + updated_footnotes <- update(inference_table, footnotes = "This is a footnote") + test_reference_table(updated_footnotes, "update_table_footnotes.txt") - set.seed(1) - information <- list( - list( - inference_name = "Effect", - inference_BF_name = "BF10", - inference_BF = 3.5, - estimate_name = "mu", - estimate_samples = rnorm(1000, 0.3, 0.15), - estimate_units = "kg", - estimate_conditional = FALSE - ) - ) + # Update with warnings + updated_warnings <- update(inference_table, warnings = "This is a warning") + test_reference_table(updated_warnings, "update_table_warnings.txt") - expect_equal( - interpret2(information, "RoBMA"), - "RoBMA found moderate evidence in favor of the Effect, BF10 = 3.50, with mean model-averaged estimate mu = 0.298 kg, 95% CI [-0.020, 0.601]." - ) + # Update with logBF + updated_logbf <- update(inference_table, logBF = TRUE) + test_reference_table(updated_logbf, "update_table_logBF.txt") + + # Update with BF01 + updated_bf01 <- update(inference_table, BF01 = TRUE) + test_reference_table(updated_bf01, "update_table_BF01.txt") }) diff --git a/tests/testthat/test-tools-evaluation.R b/tests/testthat/test-tools-evaluation.R new file mode 100644 index 00000000..906c83db --- /dev/null +++ b/tests/testthat/test-tools-evaluation.R @@ -0,0 +1,76 @@ +# ============================================================================ # +# TEST FILE: Utility Functions Evaluation Tests +# ============================================================================ # +# +# PURPOSE: +# Tests for utility functions behavior (not input validation). +# Includes .is.wholenumber, transformation checks, stan extraction, etc. +# +# DEPENDENCIES: +# - No external packages required beyond testthat +# +# SKIP CONDITIONS: +# - None (fast, pure R tests) +# +# MODELS/FIXTURES: +# - None required +# +# TAGS: @evaluation, @fast +# ============================================================================ # + + +test_that(".is.wholenumber works correctly", { + + # Positive cases + + expect_true(BayesTools:::.is.wholenumber(0)) + expect_true(BayesTools:::.is.wholenumber(5)) + expect_true(BayesTools:::.is.wholenumber(-3)) + expect_true(BayesTools:::.is.wholenumber(1e10)) + + # Negative cases + expect_false(BayesTools:::.is.wholenumber(0.5)) + expect_false(BayesTools:::.is.wholenumber(1.1)) + expect_false(BayesTools:::.is.wholenumber(-3.5)) + + # NA handling + expect_true(is.na(BayesTools:::.is.wholenumber(NA))) + expect_equal(BayesTools:::.is.wholenumber(NA, na.rm = TRUE), logical(0)) + + # Vector input + expect_equal(BayesTools:::.is.wholenumber(c(1, 2, 3.5)), c(TRUE, TRUE, FALSE)) + expect_equal(BayesTools:::.is.wholenumber(c(1, NA, 3.5)), c(TRUE, NA, FALSE)) +}) + + +test_that("transformation input validation works", { + + # Valid transformation + expect_null(.check_transformation_input(transformation = list( + "fun" = function(x) exp(x), + "inv" = function(x) log(x), + "jac" = function(x) exp(x) + ), NULL, FALSE)) + + # Missing 'jac' component + expect_error(.check_transformation_input(transformation = list( + "fun" = function(x) exp(x), + "inv" = function(x) log(x), + "err" = function(x) exp(x) + ), NULL, FALSE), "The 'jac' objects are missing in the 'transformation' argument.") + + # Invalid format + expect_error(.check_transformation_input(transformation = 1, NULL, FALSE), + "Uknown format of the 'transformation' argument.") +}) + + +test_that("stan extraction requires rstan fit", { + expect_error(.extract_stan(NULL), "'fit' must be an rstan fit") +}) + + +test_that("depreciation warnings work", { + expect_warning(.depreciate.transform_orthonormal(TRUE, FALSE), + "'transform_orthonormal' argument will be depreciated in favor of 'transform_factors' argument.") +}) diff --git a/tests/testthat/test-tools.R b/tests/testthat/test-tools-input.R similarity index 69% rename from tests/testthat/test-tools.R rename to tests/testthat/test-tools-input.R index 0d9762ee..287fb615 100644 --- a/tests/testthat/test-tools.R +++ b/tests/testthat/test-tools-input.R @@ -1,9 +1,30 @@ -context("Tools") +# ============================================================================ # +# TEST FILE: Input Validation Tests for Tools +# ============================================================================ # +# +# PURPOSE: +# Tests for input validation functions (check_bool, check_char, check_real, +# check_int, check_list) in R/tools.R +# +# DEPENDENCIES: +# - No external packages required beyond testthat +# - Tests the check_* functions exported from BayesTools +# +# SKIP CONDITIONS: +# - None (fast, pure R tests) +# +# MODELS/FIXTURES: +# - None required +# +# TAGS: @input-validation, @fast +# ============================================================================ # -test_that("Check booleans", { +test_that("check_bool validates logical inputs", { + + + # Valid inputs - # these should be allowed expect_null(check_bool(NULL, "", allow_NULL = TRUE)) expect_null(check_bool(TRUE, "")) expect_null(check_bool(FALSE, "")) @@ -11,40 +32,47 @@ test_that("Check booleans", { expect_null(check_bool(c(FALSE, FALSE), "", check_length = 2)) expect_null(check_bool(NA, "")) - # these should fail + # Invalid type: matrix expect_error( check_bool(as.matrix(as.logical(rbinom(5, 1, .5))), "test object"), "The 'test object' argument must be a logical vector." ) + # Invalid type: string expect_error( check_bool("string", "test object"), "The 'test object' argument must be a logical vector." ) + # Invalid type: numeric expect_error( check_bool(1, "test object"), "The 'test object' argument must be a logical vector." ) + # Invalid type: list expect_error( check_bool(list(TRUE), "test object"), "The 'test object' argument must be a logical vector." ) + # Invalid length expect_error( check_bool(TRUE, "test object", check_length = 2), "The 'test object' argument must have length '2'." ) + # NULL not allowed expect_error( check_bool(NULL, "test object"), "The 'test object' argument cannot be NULL." ) + # NA not allowed expect_error( check_bool(NA, "test object", allow_NA = FALSE), "The 'test object' argument cannot contain NA/NaN values." ) }) -test_that("Check strings", { - # these should be allowed +test_that("check_char validates character inputs", { + + # Valid inputs expect_null(check_char(NULL, "", allow_NULL = TRUE)) expect_null(check_char("string", "")) expect_null(check_char(c("string", "string1"), "", check_length = 0)) @@ -52,44 +80,52 @@ test_that("Check strings", { expect_null(check_char(c("string", "string1"), "", check_length = 0, allow_values = c("string", "string1"))) expect_null(check_char(c(NA, ""), "", check_length = 2)) - # these should fail + # Invalid type: matrix expect_error( check_char(as.matrix(as.logical(as.character(5, 1, .5))), "test object"), "The 'test object' argument must be a character vector." ) + # Invalid type: logical expect_error( check_char(TRUE, "test object"), "The 'test object' argument must be a character vector." ) + # Invalid type: numeric expect_error( check_char(1, "test object"), "The 'test object' argument must be a character vector." ) + # Invalid type: list expect_error( check_char(list("string"), "test object"), "The 'test object' argument must be a character vector." ) + # Invalid length expect_error( check_char("string", "test object", check_length = 2), "The 'test object' argument must have length '2'." ) + # Invalid allowed values expect_error( check_char(c("string", "string1"), "test object", check_length = 0, allow_values = c("string")), "The 'string1' values are not recognized by the 'test object' argument." ) + # NULL not allowed expect_error( check_char(NULL, "test object"), "The 'test object' argument cannot be NULL." ) + # NA not allowed expect_error( check_char(c("a", NA), "test object", allow_NA = FALSE, check_length = FALSE), "The 'test object' argument cannot contain NA/NaN values." ) }) -test_that("Check reals", { - # these should be allowed +test_that("check_real validates numeric inputs", { + + # Valid inputs expect_null(check_real(NULL, "", allow_NULL = TRUE)) expect_null(check_real(pi, "")) expect_null(check_real(c(pi, 2), "", check_length = 0)) @@ -99,56 +135,67 @@ test_that("Check reals", { expect_null(check_real(c(0, 1), "", lower = 0, upper = 1, check_length = 2)) expect_null(check_real(c(NA, NaN), "", check_length = 2)) - # these should fail + # Invalid type: matrix expect_error( check_real(as.matrix(stats::rnorm(4, 1, .5)), "test object", check_length = FALSE), "The 'test object' argument must be a numeric vector." ) + # Invalid type: logical expect_error( check_real(TRUE, "test object"), "The 'test object' argument must be a numeric vector." ) + # Invalid type: string expect_error( check_real("string", "test object"), "The 'test object' argument must be a numeric vector." ) + # Invalid type: list expect_error( check_real(list(3.2), "test object"), "The 'test object' argument must be a numeric vector." ) + # Invalid length expect_error( check_real(1, "test object", check_length = 2), "The 'test object' argument must have length '2'." ) + # Upper bound violation expect_error( check_real(stats::rgamma(1, 1, 1), "test object", upper = 0), "The 'test object' must be equal or lower than 0." ) + # Lower bound violation expect_error( check_real(stats::rbeta(1, 1, 1), "test object", lower = 1), "The 'test object' must be equal or higher than 1." ) + # Boundary not allowed (lower) expect_error( check_real(0, "test object", lower = 0, upper = 1, allow_bound = FALSE), "The 'test object' must be higher than 0." ) + # Boundary not allowed (upper) expect_error( check_real(1, "test object", lower = 0, upper = 1, allow_bound = FALSE), "The 'test object' must be lower than 1." ) + # NULL not allowed expect_error( check_real(NULL, "test object"), "The 'test object' argument cannot be NULL." ) + # NA not allowed expect_error( check_real(NaN, "test object", allow_NA = FALSE), "The 'test object' argument cannot contain NA/NaN values." ) }) -test_that("Check integers", { - # these should be allowed +test_that("check_int validates integer inputs", { + + # Valid inputs expect_null(check_int(NULL, "", allow_NULL = TRUE)) expect_null(check_int(0, "")) expect_null(check_int(c(-1, 2), "", check_length = 0)) @@ -158,56 +205,70 @@ test_that("Check integers", { expect_null(check_int(c(-3, -1), "", lower = -3, upper = -1, check_length = 2)) expect_null(check_int(c(NA, NaN), "", check_length = 2)) - # these should fail + # Invalid type: matrix expect_error( check_int(as.matrix(stats::rpois(4, 1)), "test object", check_length = FALSE), "The 'test object' argument must be a numeric vector." ) + # Invalid type: logical expect_error( check_int(TRUE, "test object"), "The 'test object' argument must be a numeric vector." ) + # Invalid type: string expect_error( check_int("string", "test object"), "The 'test object' argument must be a numeric vector." ) + # Invalid type: list expect_error( check_int(list(3.2), "test object"), "The 'test object' argument must be a numeric vector." ) + # Invalid length expect_error( check_int(1, "test object", check_length = 2), "The 'test object' argument must have length '2'." ) + # Upper bound violation expect_error( check_int(1, "test object", upper = 0), "The 'test object' must be equal or lower than 0." ) + # Lower bound violation expect_error( check_int(0, "test object", lower = 1), "The 'test object' must be equal or higher than 1." ) + # Boundary not allowed (lower) expect_error( check_int(0, "test object", lower = 0, upper = 1, allow_bound = FALSE), "The 'test object' must be higher than 0." ) + # Boundary not allowed (upper) expect_error( check_int(1, "test object", lower = 0, upper = 1, allow_bound = FALSE), "The 'test object' must be lower than 1." ) + # NULL not allowed expect_error( check_int(NULL, "test object"), "The 'test object' argument cannot be NULL." ) + # NA not allowed expect_error( check_int(c(1, NA), "test object", allow_NA = FALSE, check_length = FALSE), "The 'test object' argument cannot contain NA/NaN values." ) + # Non-integer values rejected + expect_error(check_int(1.5, "test"), "must be an integer") + expect_error(check_int(c(1, 2.5, 3), "test", check_length = 3), "must be an integer") }) -test_that("Check lists", { - # these should be allowed +test_that("check_list validates list inputs", { + + # Valid inputs expect_null(check_list(NULL, "", allow_NULL = TRUE)) expect_null(check_list(list(), "", allow_NULL = TRUE)) expect_null(check_list(list("a" = c("a", "b"), "b" = 1), "")) @@ -217,58 +278,80 @@ test_that("Check lists", { expect_null(check_list(list("a" = c("a", "b"), "b" = 1), "", check_length = 2)) expect_null(check_list(list("a" = c("a", "b"), "b" = 1, "c" = c("a", "b")), "", check_names = c("a", "b"), all_objects = TRUE, allow_other = TRUE)) - # these should fail + # Invalid type: string expect_error( check_list("string", "test object"), "The 'test object' argument must be a list." ) + # Invalid type: numeric expect_error( check_list(1, "test object"), "The 'test object' argument must be a list." ) + # Invalid type: logical expect_error( check_list(TRUE, "test object"), "The 'test object' argument must be a list." ) + # Empty list with length requirement expect_error( check_list(list(), "test object", check_length = 2), "The 'test object' argument cannot be NULL." ) + # Unrecognized names expect_error( check_list(list("c" = c("a", "b")), "test object", check_names = c("a", "b")), "The 'c' objects are not recognized by the 'test object' argument." ) + # Missing required names expect_error( check_list(list("a" = c("a", "b")), "test object", check_names = c("a", "b"), all_objects = TRUE), "The 'b' objects are missing in the 'test object' argument." ) + # NULL not allowed expect_error( check_list(NULL, "test object"), "The 'test object' argument cannot be NULL." ) }) -test_that("Other tools",{ - - expect_warning(.depreciate.transform_orthonormal(TRUE, FALSE), - "'transform_orthonormal' argument will be depreciated in favor of 'transform_factors' argument.") - - expect_error(.extract_stan(NULL), "'fit' must be an rstan fit") +test_that("check functions handle empty vectors correctly", { + # Empty vectors treated like NULL + expect_error(check_bool(logical(0), "test")) + expect_error(check_char(character(0), "test")) + expect_error(check_real(numeric(0), "test")) + expect_error(check_int(integer(0), "test")) - expect_null(.check_transformation_input(transformation = list( - "fun" = function(x) exp(x), - "inv" = function(x) log(x), - "jac" = function(x) exp(x) - ), NULL, FALSE)) + # Empty vectors allowed with allow_NULL + expect_null(check_bool(logical(0), "test", allow_NULL = TRUE)) + expect_null(check_char(character(0), "test", allow_NULL = TRUE)) + expect_null(check_real(numeric(0), "test", allow_NULL = TRUE)) + expect_null(check_int(integer(0), "test", allow_NULL = TRUE)) +}) - expect_error(.check_transformation_input(transformation = list( - "fun" = function(x) exp(x), - "inv" = function(x) log(x), - "err" = function(x) exp(x) - ), NULL, FALSE), "The 'jac' objects are missing in the 'transformation' argument.") - expect_error(.check_transformation_input(transformation = 1, NULL, FALSE), "Uknown format of the 'transformation' argument.") +test_that("check functions support custom error prefix", { + expect_error( + check_bool("string", "test", call = "[custom] "), + "\\[custom\\] The 'test' argument must be a logical" + ) + expect_error( + check_char(1, "test", call = "[custom] "), + "\\[custom\\] The 'test' argument must be a character" + ) + expect_error( + check_real("a", "test", call = "[custom] "), + "\\[custom\\] The 'test' argument must be a numeric" + ) + expect_error( + check_int("a", "test", call = "[custom] "), + "\\[custom\\] The 'test' argument must be a numeric" + ) + expect_error( + check_list("a", "test", call = "[custom] "), + "\\[custom\\] The 'test' argument must be a list" + ) }) diff --git a/vignettes/ComparisonR.Rmd b/vignettes/ComparisonR.Rmd index 1bdae289..ff4cdfb2 100644 --- a/vignettes/ComparisonR.Rmd +++ b/vignettes/ComparisonR.Rmd @@ -11,7 +11,6 @@ vignette: > %\VignetteIndexEntry{Comparison to other R packages} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} - %\VignetteEngine{knitr::rmarkdown_notangle} --- diff --git a/vignettes/SpikeAndSlab.Rmd b/vignettes/SpikeAndSlab.Rmd index 33b3d24e..d7772153 100644 --- a/vignettes/SpikeAndSlab.Rmd +++ b/vignettes/SpikeAndSlab.Rmd @@ -4,14 +4,13 @@ author: "Frantiลกek Bartoลก" date: "`r Sys.Date()`" output: rmarkdown::html_vignette: - self_contained: yes + self_contained: yes bibliography: ../inst/REFERENCES.bib csl: ../inst/apa.csl vignette: > %\VignetteIndexEntry{Bayes factors via spike and slab prior vs. bridge sampling} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} - %\VignetteEngine{knitr::rmarkdown_notangle} ---