swh:1:snp:cf20732a2a91717cec7759cd75c5d1a72600b018
Tip revision: 6acd4f1707b182a2cbe2f4a4c2d4196571d36eb3 authored by Dominique Makowski on 05 December 2020, 08:30:02 UTC
version 0.8.0
version 0.8.0
Tip revision: 6acd4f1
check_prior.R
#' Check if Prior is Informative
#'
#' Performs a simple test to check whether the prior is informative to the posterior. This idea, and the accompanying heuristics, were discussed in \href{https://statmodeling.stat.columbia.edu/2019/08/10/}{this blogpost}.
#'
#' @inheritParams effective_sample
#' @param method Can be "gelman" or "lakeland". For the "gelman" method, if the SD of the posterior is more than 0.1 times the SD of the prior, then the prior is considered as informative. For the "lakeland" method, the prior is considered as informative if the posterior falls within the 95\% HDI of the prior.
#' @param simulate_priors Should prior distributions be simulated using
#' \code{\link{simulate_prior}} (default; faster) or sampled via \code{\link{unupdate}} (slower, more accurate).
#' @examples
#' \dontrun{
#' library(bayestestR)
#' if (require("rstanarm")) {
#' model <- stan_glm(mpg ~ wt + am, data = mtcars, chains = 1, refresh = 0)
#' check_prior(model, method = "gelman")
#' check_prior(model, method = "lakeland")
#'
#' # An extreme example where both methods diverge:
#' model <- stan_glm(mpg ~ wt,
#' data = mtcars[1:3, ],
#' prior = normal(-3.3, 1, FALSE),
#' prior_intercept = normal(0, 1000, FALSE),
#' refresh = 0
#' )
#' check_prior(model, method = "gelman")
#' check_prior(model, method = "lakeland")
#' plot(si(model)) # can provide visual confirmation to the Lakeland method
#' }
#' }
#' @references https://statmodeling.stat.columbia.edu/2019/08/10/
#'
#' @export
check_prior <- function(model, method = "gelman", simulate_priors = TRUE, ...) {
UseMethod("check_prior")
}
#' @export
check_prior.brmsfit <- function(model, method = "gelman", simulate_priors = TRUE, effects = c("fixed", "random", "all"), component = c("conditional", "zi", "zero_inflated", "all"), parameters = NULL, ...) {
# check arguments
effects <- match.arg(effects)
component <- match.arg(component)
posteriors <-
insight::get_parameters(
model,
effects = effects,
component = component,
parameters = parameters
)
if (isTRUE(simulate_priors)) {
priors <-
simulate_prior(
model,
effects = effects,
component = component,
parameters = parameters
)
} else {
priors <- unupdate(model, verbose = FALSE)
priors <-
insight::get_parameters(
priors,
effects = effects,
component = component,
parameters = parameters
)
}
.check_prior(priors, posteriors, method)
}
#' @export
check_prior.stanreg <- check_prior.brmsfit
#' @importFrom stats sd
#' @keywords internal
.check_prior <- function(priors, posteriors, method = "gelman") {
.gelman <- function(prior, posterior) {
if (stats::sd(posterior) > 0.1 * stats::sd(prior)) {
"informative"
} else {
"uninformative"
}
}
.lakeland <- function(prior, posterior) {
hdi <- hdi(prior, ci = .95)
r <- rope(posterior, ci = 1, range = c(hdi$CI_low, hdi$CI_high))
if (as.numeric(r) > 0.99) {
"informative"
} else {
"misinformative"
}
}
if (method == "gelman") {
result <- mapply(.gelman, priors, posteriors)
} else if (method == "lakeland") {
result <- mapply(.lakeland, priors, posteriors)
} else {
stop("method should be 'gelman' or 'lakeland'.")
}
data.frame(
Parameter = names(posteriors),
Prior_Quality = unname(result),
stringsAsFactors = FALSE
)
}