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

.

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

- 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.

If just one object is provided, an object of class `loo`

.
If multiple objects are provided, an object of class `loolist`

.

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.

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.

```
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)
}
```