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adding mcmc trace plots #142

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Feb 20, 2025
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adding mcmc trace plots #142

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20 changes: 15 additions & 5 deletions R/barg.R
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Original file line number Diff line number Diff line change
Expand Up @@ -56,6 +56,10 @@
#' fine down to about 0.1. A summary and the total values are returned, with the summary being
#' useful if several models are included in a list for fit argument
#'
#' \item \bold{mcmcTrace}: A plot of each model's MCMC traces. Ideally these should be very mixed
#' and stationary. For more options for visualizing MCMC diagnostics see
#' \code{bayesplot::mcmc_trace}.
#'
#' \item \bold{priorPredictive}: A plot of data simulated from the prior using \link{plotPrior}.
#' This should generate data that is biologically plausible for your situation, but it will
#' probably be much more variable than your data. That is the effect of the mildly informative thick
Expand All @@ -74,7 +78,7 @@
#'
#'
#' @keywords Bayesian brms prior
#' @return A named list containing Rhat, ESS, NEFF, and Prior/Posterior Predictive plots.
#' @return A named list containing Rhat, ESS, NEFF, and Trace/Prior/Posterior Predictive plots.
#' See details for interpretation.
#' @importFrom rlang is_installed
#' @seealso \link{plotPrior} for visual prior predictive checks.
Expand Down Expand Up @@ -132,17 +136,17 @@ barg <- function(fit, ss = NULL) {
}))
out[["General"]] <- general
#* `Rhat summary`
rhats <- do.call(rbind, lapply(fitList, function(fitobj) {
rhats <- as.data.frame(do.call(rbind, lapply(fitList, function(fitobj) {
return(brms::rhat(fitobj))
}))
})))
rhat_metrics <- apply(rhats, MARGIN = 2, summary)
rhats$model <- seq_along(fitList)
out[["Rhat"]][["summary"]] <- rhat_metrics
out[["Rhat"]][["complete"]] <- rhats
#* `NEFF summary`
neff <- do.call(rbind, lapply(fitList, function(fitobj) {
neff <- as.data.frame(do.call(rbind, lapply(fitList, function(fitobj) {
return(brms::neff_ratio(fitobj))
}))
})))
neff_metrics <- apply(neff, MARGIN = 2, summary)
neff$model <- seq_along(fitList)
out[["NEFF"]][["summary"]] <- neff_metrics
Expand All @@ -168,6 +172,12 @@ barg <- function(fit, ss = NULL) {
ess_metrics <- rbind(tag_b_ess, tag_t_ess)
out[["ESS"]][["summary"]] <- ess_metrics
out[["ESS"]][["complete"]] <- ess
#* `MCMC Diagnostic Plot`
tracePlots <- lapply(fitList, function(fit) {
p <- suppressMessages(brms::mcmc_plot(fit, type = "trace"))
return(p)
})
out[["mcmcTrace"]] <- tracePlots
#* `Prior Predictive Check`
if (methods::is(eval(ssList[[1]]$call$start), "list")) {
pri_preds <- lapply(seq_along(ssList), function(i) {
Expand Down
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