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Feature: GWDA #34
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Feature: GWDA #34
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,180 @@ | ||
| #' Geographically Weighted Discriminant Analysis | ||
| #' | ||
| #' @param formula Regresison model. | ||
| #' @param data A `sf` objects. | ||
| #' @param bw Bandwidth value | ||
| #' @param adaptive Whether the bandwidth value is adaptive or not. | ||
| #' @param quantile Whether to calculate local quantiles. | ||
| #' @param kernel Kernel function used. | ||
| #' @param longlat Whether the coordinates | ||
| #' @param p Power of the Minkowski distance, | ||
| #' default to 2, i.e., Euclidean distance. | ||
| #' @param theta Angle in radian to roate the coordinate system, default to 0. | ||
| #' @param method Discriminant method for GWDA, can be set as | ||
| #' - `wqda`, weighted quadratic discriminant analysis | ||
| #' - `wlda`, weighted linear discriminant analysis | ||
| #' If not provided, this parameter will be set to `wqda`. | ||
| #' @param parallel_method Parallel method. | ||
| #' @param parallel_arg Parallel method argument. | ||
| #' | ||
| #' @return A `gwdam` object. | ||
| #' | ||
| #' @export | ||
| gwda <- function( | ||
| formula, | ||
| data, | ||
| bw, | ||
| adaptive = FALSE, | ||
| kernel = c("gaussian", "exp", "bisquare", "tricube", "boxcar"), | ||
| longlat = FALSE, | ||
| p = 2.0, | ||
| theta = 0.0, | ||
| method = c("wqda", "wlda"), | ||
| parallel_method = c("no", "omp"), | ||
| parallel_arg = c(0)) | ||
| { | ||
| ### Check args | ||
| kernel <- match.arg(kernel) | ||
| parallel_method <- match.arg(parallel_method) | ||
| attr(data, "na.action") <- getOption("na.action") | ||
|
|
||
| ### Extract coords | ||
| data <- do.call(na.action(data), args = list(data)) | ||
| coords <- as.matrix(sf::st_coordinates(sf::st_centroid(data))) | ||
| if (is.null(coords) || nrow(coords) != nrow(data)) { | ||
| stop("Missing coordinates.") | ||
| } | ||
|
|
||
| ### Extract variables | ||
| mc <- match.call(expand.dots = FALSE) | ||
| mf <- model.frame(formula = formula(update(formula, ~ . - 1)), data = sf::st_drop_geometry(data)) | ||
| mt <- attr(mf, "terms") | ||
| y <- model.extract(mf, "response") | ||
| y <- as.character(y) | ||
| x <- model.matrix(mt, mf) | ||
| dep_var <- as.character(attr(terms(mf), "variables")[[2]]) | ||
| indep_vars <- colnames(x) | ||
|
|
||
| ### Check whether bandwidth is valid. | ||
| if (missing(bw)) { | ||
| stop("Bandwidth is missing.") | ||
| } else if (is.numeric(bw) || is.integer(bw)) { | ||
| if (bw == 0) stop("Bandwidth must be non-zero.") | ||
| } else { | ||
| stop("Bandwidth must be a number.") | ||
| } | ||
|
|
||
| method <- match.arg(method) | ||
| wqda_method <- ifelse(method == "wqda", TRUE, FALSE) | ||
|
|
||
| c_results <- gwda_cal( | ||
| x, y, coords, bw, | ||
| adaptive, enum(kernel), | ||
| longlat, p, theta, | ||
| wqda_method, | ||
| enum_list(parallel_method, parallel_types), parallel_arg | ||
| ) | ||
|
|
||
| sdf_data <- data.frame() | ||
| res <- c_results$res | ||
| group <- as.data.frame(c_results$group) | ||
| probs <- c_results$probs | ||
| pmax <- c_results$pmax | ||
| entropy <- c_results$entropy | ||
|
|
||
| colnames(res) <- indep_vars | ||
| colnames(group) <- "groups" | ||
| colnames(probs) <- paste0(indep_vars, "_probs") | ||
| colnames(pmax) <- "pmax" | ||
| colnames(entropy) <- "entropy" | ||
| sdf_data <- as.data.frame(cbind(res, group, probs, pmax, entropy)) | ||
|
|
||
| sdf_data$geometry <- sf::st_geometry(data) | ||
| sdf <- sf::st_sf(sdf_data) | ||
|
|
||
| ### Return result | ||
| gwdam <- list( | ||
| SDF = sdf, | ||
| args = list( | ||
| x = x, | ||
| y = y, | ||
| correctRate = c_results$correctRate, | ||
| coords = coords, | ||
| bw = bw, | ||
| adaptive = adaptive, | ||
| kernel = kernel, | ||
| longlat = longlat, | ||
| p = p, | ||
| theta = theta, | ||
| method = method, | ||
| parallel_method = parallel_method, | ||
| parallel_arg = parallel_arg | ||
| ), | ||
| call = mc, | ||
| indep_vars = indep_vars, | ||
| dep_var = dep_var | ||
| ) | ||
| class(gwdam) <- "gwdam" | ||
| gwdam | ||
| } | ||
|
|
||
| #' Print description of a `gwdam` object | ||
| #' | ||
| #' @param x An `gwdam` object returned by [gwr_basic()]. | ||
| #' @param decimal_fmt The format string passing to [base::sprintf()]. | ||
| #' @inheritDotParams print_table_md | ||
| #' | ||
| #' @method print gwdam | ||
| #' @rdname print | ||
| #' @export | ||
| print.gwdam <- function(x, ..., decimal_fmt) { | ||
| if (!inherits(x, "gwdam")) { | ||
| stop("It's not a 'gwdam' object.") | ||
| } | ||
|
|
||
| cat(" ***********************************************************************\n") | ||
| cat(" * Package GWmodel3 *\n") | ||
| cat(" ***********************************************************************\n") | ||
| cat(" * Results of Geographically Discriminant Analysis *\n") | ||
| cat(" ***********************************************************************\n") | ||
| cat("\n *********************Model Calibration Information*********************\n") | ||
|
|
||
| cat(" Formula:", deparse(x$call$formula), fill = T) | ||
| cat(" Data:", deparse(x$call$data), fill = T) | ||
| cat(" Method:", x$args$method, fill = T) | ||
| cat(" Number of summary points:", nrow(x$args$x), fill = T) | ||
| cat(" Kernel function:", x$args$kernel, fill = T) | ||
| cat(" Bandwidth:", x$args$bw, | ||
| ifelse(x$args$adaptive, "(Nearest Neighbours)", "(Meters)"), | ||
| fill = T | ||
| ) | ||
| distance_type <- "Euclidean" | ||
| if (x$args$longlat) { | ||
| distance_type <- "Geodetic" | ||
| } else if (x$args$p == 2) { | ||
| distance_type <- "Euclidean" | ||
| } else if (x$args$p == 1) { | ||
| distance_type <- "Manhattan" | ||
| } else if (is.infinite(x$args$p)) { | ||
| distance_type <- "Chebyshev" | ||
| } else { | ||
| distance_type <- "Generalized Minkowski" | ||
| } | ||
| distance_rotated <- (x$args$theta != 0 && x$args$p != 2 && !x$args$longlat) | ||
| cat(" Distance:", distance_type, ifelse(distance_rotated, " (rotated)", ""), fill = T) | ||
| res <- st_drop_geometry(x$SDF) | ||
| group_counts <- as.data.frame(table(res$groups)) | ||
| cat(" Discrimination Result:", fill = T) | ||
| colnames(group_counts) <- c("Group", "Count") | ||
| output <- capture.output(print(group_counts, row.names = FALSE)) | ||
| output <- paste0(" ", output) | ||
| cat(output, sep = "\n") | ||
|
|
||
| cat("\n ***********************Local Summary Statistics************************", fill = T) | ||
| numeric_res <- res[sapply(res, is.numeric)] | ||
| df <- as.data.frame(t(sapply(numeric_res, summary))) | ||
| output <- capture.output(print(df)) | ||
| output <- paste0(" ", output) | ||
| cat(output, sep = "\n") | ||
| cat(" ***********************************************************************\n") | ||
| } | ||
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