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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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.