Posterior predictive checks compare observed features with replicated data. They are evidence objects, not automatic model-validity certificates.
ppc <- check_pupil_posterior_predictive(
pupil_fit,
ndraws = 200,
window = c(0.3, 1.0)
)
pupil_ppc_table(ppc)
plot_pupil_ppc(ppc)The implementation summarizes observed and replicated trajectories, declared window summaries, AUC, peak response and latency, residual structure, and measurement-context overlays when corresponding indicators are available.
diag <- diagnose_pupil_fit(pupil_fit)
as.data.frame(diag)
acf_table <- pupil_residual_acf(pupil_fit, max_lag = 12)
head(acf_table)
plot_pupil_residual_acf(acf_table)Sampling diagnostics reuse the package’s posterior/MCMC infrastructure and report quantities such as R-hat, effective sample size, divergences, treedepth, and available energy diagnostics. Temporal diagnostics additionally show residual autocorrelation and support over event-relative time.
No single threshold is labelled proof of model adequacy. Measurement limitations, specification uncertainty, and the prediction target remain separate questions.
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