#' Returns Priors of a Model as Empirical Distributions #' #' Transforms priors information to actual distributions. #' #' @inheritParams effective_sample #' @param n Size of the simulated prior distributions. #' #' @examples #' \dontrun{ #' library(bayestestR) #' if (require("rstanarm")) { #' model <- stan_glm(mpg ~ wt + am, data = mtcars, chains = 1, refresh = 0) #' simulate_prior(model) #' } #' } #' @export simulate_prior <- function(model, n = 1000, ...) { UseMethod("simulate_prior") } #' @export simulate_prior.stanreg <- function(model, n = 1000, effects = c("fixed", "random", "all"), parameters = NULL, ...) { # check arguments effects <- match.arg(effects) priors <- insight::get_priors( model, effects = effects, parameters = parameters ) .simulate_prior(priors, n = n) } #' @export simulate_prior.brmsfit <- function(model, n = 1000, effects = c("fixed", "random", "all"), component = c("conditional", "zi", "zero_inflated", "all"), parameters = NULL, ...) { # check arguments effects <- match.arg(effects) component <- match.arg(component) priors <- insight::get_priors( model, effects = effects, component = component, parameters = parameters ) .simulate_prior(priors, n = n) } #' @keywords internal .simulate_prior <- function(priors, n = 1000) { simulated <- data.frame(.bamboozled = 1:n) # iterate over parameters for (param in priors$Parameter) { prior <- priors[priors$Parameter == param, ] # Get actual scale if ("Adjusted_Scale" %in% names(prior)) { scale <- prior$Adjusted_Scale # is autoscale = FALSE, scale contains NA values - replace # with non-adjusted then. if (anyNA(scale)) scale[is.na(scale)] <- prior$Scale[is.na(scale)] } else { scale <- prior$Scale } # Simulate prior prior <- distribution(prior$Distribution, n, prior$Location, scale) simulated[param] <- prior } simulated$.bamboozled <- NULL simulated }