WAIC: Compute the widely applicable information criterion

In RprobitB: Bayesian Probit Choice Modeling

Compute the widely applicable information criterion

Description

Computes WAIC from posterior log-likelihood draws using loo::waic().

Usage

WAIC(object, ghk_draws = 500L, progress = interactive(), ...)

Arguments

object	[RprobitB_fit] Fitted choice model.
ghk_draws	[integer(1)] Number of draws of the GHK simulator for multivariate normal probabilities of more than three dimensions, see oeli::pmvnorm().
progress	[logical(1)] Show progress?
...	Further arguments passed to loo::waic().

Value

A waic object from loo. Its estimates matrix contains WAIC, effective parameter counts, and their standard errors.

References

\insertRef

Watanabe2010RprobitB

\insertRef

Vehtari2017RprobitB

Examples

set.seed(1)
model <- fit(
  choice ~ x | 0, dgp_parameters = list(beta = c(x = 1)), n_occasions = 5,
  chains = 1
)
WAIC(model)

RprobitB documentation built on Sept. 20, 2026, 9:07 a.m.