Posterior Predictive Density Overlay Plot

Wraps bayesplot::ppc_dens_overlay() with consistent styling via theme_ppc(). Subsamples y_rep rows for readability when \(S\) is large.

Usage

plot_ppc_overlay(y_obs, y_rep, n_samples = 50, ...)

Arguments

y_obs

Numeric vector of length \(n\) containing the observed outcomes.

y_rep

Numeric matrix of dimension \(S \times n\) where each row is one posterior predictive replicate (e.g. the output of simulate_ppc()).

n_samples

Positive integer. Number of rows to subsample from y_rep for the overlay. Subsampling improves readability when \(S\) is large. Defaults to 50. The subsample is drawn without replacement when n_samples < S, otherwise all rows are used.

...

Additional arguments passed to bayesplot::ppc_dens_overlay().

Value

A ggplot2 object.

See also

plot_ppc_stat(), bayesplot::ppc_dens_overlay()

Other ppc-workflow: compare_models_ppc(), plot_ppc_stat(), ppc_diagnostics(), print.ppc_diagnostics(), simulate_ppc(), theme_ppc()

Examples

set.seed(7)
y     <- rnorm(80, mean = 0, sd = 1)
draws <- matrix(rnorm(300 * 80, mean = 0), nrow = 300, ncol = 80)
y_rep <- simulate_ppc(draws)
# \donttest{
  p <- plot_ppc_overlay(y, y_rep, n_samples = 40)
# }