Hierarchical Effects and Predictive Uncertainty

Python-facing port of hierarchical-effects-and-uncertainty.Rmd from the frozen R gp3bayes 0.5.0 reference. The statistical and governance framing below follows the canonical vignette; executable Python workflows use the mapped APIs listed later.

gp3bayespy treats group-level estimates as posterior quantities to inspect, not as automatic rankings of participants or items.

Grouped posterior predictive checks

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

Descriptive uncertainty decomposition

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.

Python API mapping

• gp3bayespy.group_effect_table
• gp3bayespy.grouped_prediction_check
• gp3bayespy.plot_group_effects
• gp3bayespy.plot_grouped_prediction_check
• gp3bayespy.plot_uncertainty_decomposition
• gp3bayespy.plot_variance_components
• gp3bayespy.prediction_uncertainty_decomposition
• gp3bayespy.variance_component_table

Python usage

import gp3bayespy as gp

# All functions listed above are available from the package root.
# Use help(gp.<function>) or the API reference for the exact Python signature.

An executable workflow for this family is included in ../../examples/predictive_diagnostics.py.