Posterior Predictive Checks For Mcpfit Objects

Plot posterior (default) or prior (prior = TRUE) predictive checks. This is convenience wrapper around the bayesplot::ppc_*() methods.

pp_check(
  object,
  type = "dens_overlay",
  facet_by = NULL,
  newdata = NULL,
  prior = FALSE,
  varying = TRUE,
  arma = TRUE,
  nsamples = 100,
  ...
)

Arguments

object	An mcpfit object.
type	One of bayesplot::available_ppc("grouped", invert = TRUE) %>% stringr::str_remove("ppc_")
facet_by	Name of a column in data modeled as varying effect(s).
newdata	A tibble or a data.frame containing predictors in the model. If NULL (default), the original data is used.
prior	TRUE/FALSE. Plot using prior samples? Useful for mcp(..., sample = "both")
varying	TRUE: All varying effects (fit$pars$varying). FALSE: No varying efects (c()). Character vector: Only include specified varying parameters - see fit$pars$varying.
arma	Whether to include autoregressive effects. TRUE: Compute autoregressive residuals. Requires the response variable in newdata. FALSE: Disregard the autoregressive effects. For family = gaussian(), predict() just use sigma for residuals.
nsamples	Number of draws. Note that you may want to use all data for summary geoms. e.g., pp_check(fit, type = "ribbon", nsamples = NULL).
...	Further arguments passed to bayesplot::ppc_type(y, yrep, ...)

Value

A ggplot2 object for single plots. Enriched by patchwork for faceted plots.

See also

plot.mcpfit pp_eval

Examples

# \donttest{
pp_check(ex_fit)
pp_check(ex_fit, type = "ecdf_overlay")
#pp_check(some_varying_fit, type = "loo_intervals", facet_by = "id")
# }