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.