brms (version 2.19.0)

waic.brmsfit: Widely Applicable Information Criterion (WAIC)

Description

Compute the widely applicable information criterion (WAIC) based on the posterior likelihood using the loo package. For more details see waic.

Usage

# S3 method for brmsfit
waic(
  x,
  ...,
  compare = TRUE,
  resp = NULL,
  pointwise = FALSE,
  model_names = NULL
)

Value

If just one object is provided, an object of class loo. If multiple objects are provided, an object of class loolist.

Arguments

x

A brmsfit object.

...

More brmsfit objects or further arguments passed to the underlying post-processing functions. In particular, see prepare_predictions for further supported arguments.

compare

A flag indicating if the information criteria of the models should be compared to each other via loo_compare.

resp

Optional names of response variables. If specified, predictions are performed only for the specified response variables.

pointwise

A flag indicating whether to compute the full log-likelihood matrix at once or separately for each observation. The latter approach is usually considerably slower but requires much less working memory. Accordingly, if one runs into memory issues, pointwise = TRUE is the way to go.

model_names

If NULL (the default) will use model names derived from deparsing the call. Otherwise will use the passed values as model names.

Details

See loo_compare for details on model comparisons. For brmsfit objects, WAIC is an alias of waic. Use method add_criterion to store information criteria in the fitted model object for later usage.

References

Vehtari, A., Gelman, A., & Gabry J. (2016). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. In Statistics and Computing, doi:10.1007/s11222-016-9696-4. arXiv preprint arXiv:1507.04544.

Gelman, A., Hwang, J., & Vehtari, A. (2014). Understanding predictive information criteria for Bayesian models. Statistics and Computing, 24, 997-1016.

Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. The Journal of Machine Learning Research, 11, 3571-3594.

Examples

if (FALSE) {
# model with population-level effects only
fit1 <- brm(rating ~ treat + period + carry,
            data = inhaler)
(waic1 <- waic(fit1))

# model with an additional varying intercept for subjects
fit2 <- brm(rating ~ treat + period + carry + (1|subject),
            data = inhaler)
(waic2 <- waic(fit2))

# compare both models
loo_compare(waic1, waic2)
}