% Generated by roxygen2: do not edit by hand % Please edit documentation in R/check_prior.R \name{check_prior} \alias{check_prior} \title{Check if Prior is Informative} \usage{ check_prior(model, method = "gelman", simulate_priors = TRUE, ...) } \arguments{ \item{model}{A \code{stanreg}, \code{stanfit}, or \code{brmsfit} object.} \item{method}{Can be \code{"gelman"} or \code{"lakeland"}. For the \code{"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 \code{"lakeland"} method, the prior is considered as informative if the posterior falls within the 95\% HDI of the prior.} \item{simulate_priors}{Should prior distributions be simulated using \code{\link{simulate_prior}} (default; faster) or sampled via \code{\link{unupdate}} (slower, more accurate).} \item{...}{Currently not used.} } \value{ A data frame with two columns: The parameter names and the quality of the prior (which might be \code{"informative"}, \code{"uninformative"}) or \code{"not determinable"} if the prior distribution could not be determined). } \description{ 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}. } \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/ }