Posterior predictive check for bmmfit objects

For models where brms provides pp_check support, this method delegates to brms::pp_check(). For models with multinomial families (e.g., the m3 model), brms's pp_check is unavailable; this method dispatches to a model-specific visualisation instead.

Usage

# S3 method for class 'bmmfit'
pp_check(object, type = "dens_overlay", ndraws = NULL, group = NULL, ...)

Arguments

object

A bmmfit object returned by bmm().

type

Character. Type of pp_check (default "dens_overlay"). For non-multinomial models, passed to brms::pp_check(). When group is specified, the grouped variant (e.g., "dens_overlay_grouped") is auto-selected if available. Multinomial models produce a response proportion profile regardless of the value supplied.

ndraws

Integer. Number of posterior draws. Defaults to 100 for multinomial models; otherwise passed to brms::pp_check().

group

Character. Optional grouping variable for faceting. For non-multinomial models, passed to brms::pp_check(); when specified, the grouped variant of type (e.g., "dens_overlay_grouped") is auto-selected if available. For multinomial models, facets by the named predictor.

...

Additional arguments forwarded to brms::pp_check() (non-multinomial) or to brms::posterior_predict() (multinomial). For multinomial models, probs (numeric vector of length 2, default c(0.025, 0.975)) controls the credible interval. Both model types accept re_formula (e.g., re_formula = NA to predict at the population level, excluding random effects).

Value

For multinomial models, a ggplot2 object. For other models, the result of brms::pp_check().

Details

For multinomial models, the plot mirrors the bayesplot ppc_bars style: observed proportions are shown as bars and posterior predictive medians with credible intervals are shown as point-ranges, using the bayesplot default colour scheme and theme.

See also

brms::pp_check()