Compute the widely applicable information criterion

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

Watanabe S (2010). “Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory.” Journal of Machine Learning Research, 11, 3571–3594. https://www.jmlr.org/papers/v11/watanabe10a.html.

Vehtari A, Gelman A, Gabry J (2017). “Practical Bayesian Model Evaluation Using Leave-One-Out Cross-Validation and WAIC.” Statistics and Computing, 27(5), 1413–1432. doi:10.1007/s11222-016-9696-4 .

Examples

set.seed(1)
model <- fit(
  choice ~ x | 0, dgp_parameters = list(beta = c(x = 1)), n_occasions = 5,
  chains = 1
)
WAIC(model)
#> 
#> Computed from 500 by 100 log-likelihood matrix.
#> 
#>           Estimate   SE
#> elpd_waic   -185.5 12.2
#> p_waic         0.8  0.2
#> waic         371.0 24.3