# Hierarchical Effects and Predictive Uncertainty

`gp3bayes` treats group-level estimates as posterior quantities to inspect, not as automatic rankings of participants or items.

    effects <- group_effect_table(fit)
    components <- variance_component_table(fit)

    plot_group_effects(effects)
    plot_variance_components(components)

## Grouped posterior predictive checks

    participant_ppc <- grouped_prediction_check(
      fit,
      group = "participant_id",
      ndraws = 1000
    )

    as.data.frame(participant_ppc)
    plot_grouped_prediction_check(participant_ppc)

The check compares observed group summaries with their posterior predictive distribution. No group is automatically excluded.

## Descriptive uncertainty decomposition

    uncertainty <- prediction_uncertainty_decomposition(
      fit,
      include_group_effects = FALSE,
      ndraws = 1000
    )

    as.data.frame(uncertainty)
    plot_uncertainty_decomposition(uncertainty)

The expected-response component and remaining predictive component are Monte Carlo variance summaries under the fitted model. They should not be interpreted as a causal variance decomposition.