#' Density Estimation #' #' This function is a wrapper over different methods of density estimation. By default, it uses the base R \link{density} with by default uses a different smoothing bandwidth (\code{"SJ"}) from the legacy default implemented the base R \link{density} function (\code{"nrd0"}). However, Deng \& Wickham suggest that \code{method = "KernSmooth"} is the fastest and the most accurate. #' #' @inheritParams hdi #' @inheritParams stats::density #' @param method Method of density estimation. Can be \code{"kernel"} (default), \code{"logspline"} or \code{"KernSmooth"}. #' @param precision Number of points of density data. See the \code{n} parameter in \link[=density]{density}. #' @param extend Extend the range of the x axis by a factor of \code{extend_scale}. #' @param extend_scale Ratio of range by which to extend the x axis. A value of \code{0.1} means that the x axis will be extended by \code{1/10} of the range of the data. #' #' @examples #' library(bayestestR) #' #' x <- rnorm(250, 1) #' #' # Methods #' density_kernel <- estimate_density(x, method = "kernel") #' density_logspline <- estimate_density(x, method = "logspline") #' density_KernSmooth <- estimate_density(x, method = "KernSmooth") #' #' hist(x, prob = TRUE) #' lines(density_kernel$x, density_kernel$y, col = "black", lwd = 2) #' lines(density_logspline$x, density_logspline$y, col = "red", lwd = 2) #' lines(density_KernSmooth$x, density_KernSmooth$y, col = "blue", lwd = 2) #' #' # Extension #' density_extended <- estimate_density(x, extend = TRUE) #' density_default <- estimate_density(x, extend = FALSE) #' #' hist(x, prob = TRUE) #' lines(density_extended$x, density_extended$y, col = "red", lwd = 3) #' lines(density_default$x, density_default$y, col = "black", lwd = 3) #' #' df <- data.frame(replicate(4, rnorm(100))) #' head(estimate_density(df)) #' #' # rstanarm models #' # ----------------------------------------------- #' library(rstanarm) #' model <- stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200) #' head(estimate_density(model)) #' #' library(emmeans) #' head(estimate_density(emtrends(model, ~1, "wt"))) #' \dontrun{ #' # brms models #' # ----------------------------------------------- #' library(brms) #' model <- brms::brm(mpg ~ wt + cyl, data = mtcars) #' estimate_density(model) #' } #' #' @references Deng, H., & Wickham, H. (2011). Density estimation in R. Electronic publication. #' #' @importFrom stats density #' @importFrom utils install.packages #' @export estimate_density <- function(x, method = "kernel", precision = 2^10, extend = FALSE, extend_scale = 0.1, bw = "SJ", ...) { UseMethod("estimate_density") } #' @rdname estimate_density #' @export estimate_probability <- estimate_density #' @keywords internal .estimate_density <- function(x, method = "kernel", precision = 2^10, extend = FALSE, extend_scale = 0.1, bw = "SJ", ...) { method <- match.arg(method, c("kernel", "logspline", "KernSmooth", "smooth")) # Range x_range <- range(x) if (extend) { extension_scale <- diff(x_range) * extend_scale x_range[1] <- x_range[1] - extension_scale x_range[2] <- x_range[2] + extension_scale } # Replace inf values if needed x_range[is.infinite(x_range)] <- 5.565423e+156 # Kernel if (method == "kernel") { return(as.data.frame(density(x, n = precision, bw = bw, from = x_range[1], to = x_range[2], ...))) # Logspline } else if (method == "logspline") { if (!requireNamespace("logspline")) { if (interactive()) { readline("Package \"logspline\" needed for this function. Press ENTER to install or ESCAPE to abort.") install.packages("logspline") } else { stop("Package \"logspline\" needed for this function. Press run 'install.packages(\"logspline\")'.") } } x_axis <- seq(x_range[1], x_range[2], length.out = precision) y <- logspline::dlogspline(x_axis, logspline::logspline(x, ...), ...) return(data.frame(x = x_axis, y = y)) # KernSmooth } else if (method %in% c("KernSmooth", "smooth")) { if (!requireNamespace("KernSmooth")) { if (interactive()) { readline("Package \"KernSmooth\" needed for this function. Press ENTER to install or ESCAPE to abort.") install.packages("KernSmooth") } else { stop("Package \"KernSmooth\" needed for this function. Press run 'install.packages(\"KernSmooth\")'.") } } return(as.data.frame(KernSmooth::bkde(x, range.x = x_range, gridsize = precision, truncate = TRUE, ...))) } else { stop("method should be one of 'kernel', 'logspline' or 'KernSmooth'") } } #' @export estimate_density.numeric <- function(x, method = "kernel", precision = 2^10, extend = FALSE, extend_scale = 0.1, bw = "SJ", ...) { out <- .estimate_density(x, method = method, precision = precision, extend = extend, extend_scale = extend_scale, bw = bw, ...) class(out) <- c("estimate_density", "see_estimate_density", class(out)) out } #' @export estimate_density.data.frame <- function(x, method = "kernel", precision = 2^10, extend = FALSE, extend_scale = 0.1, bw = "SJ", ...) { x <- .select_nums(x) out <- sapply(x, estimate_density, method = method, precision = precision, extend = extend, extend_scale = extend_scale, bw = bw, simplify = FALSE) for (i in names(out)) { out[[i]]$Parameter <- i } out <- do.call(rbind, out) row.names(out) <- NULL out[, c("Parameter", "x", "y")] } #' @export estimate_density.emmGrid <- function(x, method = "kernel", precision = 2^10, extend = FALSE, extend_scale = 0.1, bw = "SJ", ...) { if (!requireNamespace("emmeans")) { stop("Package \"emmeans\" needed for this function to work. Please install it.") } x <- as.data.frame(as.matrix(emmeans::as.mcmc.emmGrid(x, names = FALSE))) estimate_density(x, method = method, precision = precision, extend = extend, extend_scale = extend_scale, bw = bw, ... ) } #' @importFrom insight get_parameters #' @export estimate_density.stanreg <- function(x, method = "kernel", precision = 2^10, extend = FALSE, extend_scale = 0.1, bw = "SJ", effects = c("fixed", "random", "all"), parameters = NULL, ...) { effects <- match.arg(effects) out <- estimate_density(insight::get_parameters(x, effects = effects, parameters = parameters), method = method, precision = precision, extend = extend, extend_scale = extend_scale, bw = bw, ...) out } #' @importFrom insight get_parameters #' @export estimate_density.brmsfit <- function(x, method = "kernel", precision = 2^10, extend = FALSE, extend_scale = 0.1, bw = "SJ", effects = c("fixed", "random", "all"), component = c("conditional", "zi", "zero_inflated", "all"), parameters = NULL, ...) { effects <- match.arg(effects) component <- match.arg(component) out <- estimate_density(insight::get_parameters(x, effects = effects, component = component, parameters = parameters), method = method, precision = precision, extend = extend, extend_scale = extend_scale, bw = bw, ...) out } #' Coerce to a Data Frame #' #' @inheritParams base::as.data.frame #' @method as.data.frame density #' @export as.data.frame.density <- function(x, ...) { data.frame(x = x$x, y = x$y) } #' Probability of a Given Point #' #' Compute the density of a given point of a distribution. #' #' @param posterior Vector representing a posterior distribution. #' @param x The value of which to get the approximate probability. #' @inheritParams estimate_density #' #' @examples #' library(bayestestR) #' posterior <- distribution_normal(n = 10) #' density_at(posterior, 0) #' density_at(posterior, c(0, 1)) #' @importFrom stats approx density #' @export density_at <- function(posterior, x, precision = 2^10, ...) { density <- estimate_density(posterior, precision = precision, ...) stats::approx(density$x, density$y, xout = x)$y } #' @rdname density_at #' @export probability_at <- density_at