Run pupil-specific posterior predictive checks — check_pupil_posterior

Compares observed and replicated trajectories, distributional features, whole-support peak/latency and AUC, optional declared-window response, lag-one serial structure, participant/trial heterogeneity, residual trajectories, and blink/interpolation context. The object reports evidence and never declares a model adequate.

Usage

check_pupil_posterior_predictive(
  fit,
  ndraws = 200L,
  probability = 0.9,
  window = NULL,
  max_cells = 3000000L
)

Arguments

fit: A fitted pupil model.
ndraws: Posterior predictive draws.
probability: Predictive envelope probability.
window: Optional user-declared event-time window in canonical seconds.
max_cells: Maximum draw-by-observation cells.

Value

A gp3bayes_pupil_ppc.

Examples

# See vignette("bayesian-dynamic-pupillometry", package = "gp3bayes") for a complete workflow.