waic.brmsfit {brms} | R Documentation |

## 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 class 'brmsfit'
waic(
x,
...,
compare = TRUE,
resp = NULL,
pointwise = FALSE,
model_names = NULL
)
```

### Arguments

`x` |
A |

`...` |
More |

`compare` |
A flag indicating if the information criteria
of the models should be compared to each other
via |

`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, |

`model_names` |
If |

### 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.

### Value

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

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

.

### 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

```
## Not run:
# 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)
## End(Not run)
```

*brms*version 2.21.0 Index]