Predict governed pupil trajectories — predict_pupil

Obtains expected, posterior-predictive, or linear-predictor draws from an approved pupil fit with explicit draw and grid-size guards.

Usage

predict_pupil_trajectory(
  fit,
  newdata = NULL,
  type = c("expected", "posterior_predictive", "linear"),
  ndraws = 500L,
  population_only = TRUE,
  allow_new_levels = FALSE,
  max_grid = 5000L,
  max_cells = 5000000L
)

Arguments

fit: A gp3bayes_pupil_fit.
newdata: Optional prepared prediction grid. When omitted, a compact population grid is built from observed event times and conditions. Participant-conditioned prediction requires explicit newdata.
type: "expected", "posterior_predictive", or "linear".
ndraws: Maximum posterior draws to retain.
population_only: If TRUE, group-level effects are excluded from the prediction via re_formula = NA.
allow_new_levels: Passed conservatively to brms prediction methods.
max_grid: Maximum grid rows.
max_cells: Maximum draw-by-grid cells.

Value

A gp3bayes_pupil_prediction.

Examples

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