#' Diagnostic values for each iteration
#'
#' Returns the accumulated log-posterior, the average Metropolis acceptance rate, divergent transitions, treedepth rather than terminated its evolution normally.
#' @inheritParams diagnostic_posterior
#'
#' @examples
#' \dontrun{
#' set.seed(333)
#'
#' if (require("brms", quietly = TRUE)) {
#' model <- brm(mpg ~ wt * cyl * vs,
#' data = mtcars,
#' iter = 100, control = list(adapt_delta = 0.80),
#' refresh = 0
#' )
#' diagnostic_draws(model)
#' }
#' }
#'
#' @export
diagnostic_draws <- function(posteriors, ...) {
UseMethod("diagnostic_draws")
}
#' @export
diagnostic_draws.brmsfit <- function(posteriors, ...) {
insight::check_if_installed("brms")
data <- brms::nuts_params(posteriors)
data$idvar <- paste0(data$Chain, "_", data$Iteration)
out <- stats::reshape(
data,
v.names = "Value",
idvar = "idvar",
timevar = "Parameter",
direction = "wide"
)
out$idvar <- NULL
out <- merge(out, brms::log_posterior(posteriors), by = c("Chain", "Iteration"), sort = FALSE)
# Rename
names(out)[names(out) == "Value.accept_stat__"] <- "Acceptance_Rate"
names(out)[names(out) == "Value.treedepth__"] <- "Tree_Depth"
names(out)[names(out) == "Value.stepsize__"] <- "Step_Size"
names(out)[names(out) == "Value.divergent__"] <- "Divergent"
names(out)[names(out) == "Value.n_leapfrog__"] <- "n_Leapfrog"
names(out)[names(out) == "Value.energy__"] <- "Energy"
names(out)[names(out) == "Value"] <- "LogPosterior"
out
}