README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```
# ilasso

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The goal of ilasso is to help identifying interactions via hierarchical lasso regularisation using a two-step procedure.

## Installation

You can install the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("abuchardt/ilasso")
```
## Example

This is a basic example on simulated data:

```{r example}
library(ilasso)

# Gaussian
p <- 200
n <- 100
nz <- dplyr::arrange(expand.grid(a = 1:p, b = 1:p), a)
X <- matrix(sample(c(TRUE, FALSE), n * p, replace = TRUE), nrow = n, ncol = p)
ind <- t(combn(ncol(X),2))
out <- apply(ind, 1, function(x) X[,x[1]] * X[,x[2]])
XX <- cbind(X, out)
beta <- c(3,3, rep(0, ncol(X)-2))
beta12 <- c(3, rep(0, choose(p, 2)-1))
y <- XX %*% c(beta,beta12) + rnorm(n)
```

Run step 1
```{r step1}
# Step 1
fit1 <- ilasso(x = X, y = y, step = "first")
me1 <- fit1$maineffects1
print(fit1)
```

Run step 2 under strong hierarchy (default)
```{r step2}
# Step 2
fit2 <- ilasso(x = X, y = y, step = "second", maineffects1 = me1)
print(fit2)
```

Run both steps under strong hierarchy (default)
```{r both}
# Both steps
fit12 <- ilasso(x = X, y = y, step = "both")
print(fit2)
```