R: Widely Applicable Information Criterion (WAIC)

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 `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.

### 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)

[Package brms version 2.16.1 Index]