R: Compute the LOO information criterion

loo.brmsfit {brms} | R Documentation

## Compute the LOO information criterion

### Description

Perform approximate leave-one-out cross-validation based on the posterior likelihood using the loo package. For more details see `loo`.

### Usage

    ## S3 method for class 'brmsfit'
    loo(x, ..., compare = TRUE, resp = NULL,
      pointwise = FALSE, reloo = FALSE, k_threshold = 0.7,
      reloo_args = list(), model_names = NULL)

### Arguments

`x` | A fitted model object.
`...` | More fitted model objects or further arguments passed to the underlying post-processing functions.
`compare` | A flag indicating if the information criteria of the models should be compared to each other via `compare_ic`.
`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.
`reloo` | Logical; Indicate whether `reloo` should be applied on problematic observations. Defaults to `FALSE`.
`k_threshold` | The threshold at which pareto k estimates are treated as problematic. Defaults to `0.7`. Only used if argument `reloo` is `TRUE`. See `pareto_k_ids` for more details.
`reloo_args` | Optional `list` of additional arguments passed to `reloo`.
`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

When comparing models fitted to the same data, the smaller the LOO, the better the fit. For `brmsfit` objects, `LOO` is an alias of `loo`. Use method `add_ic` to store information criteria in the fitted model object for later usage.

### Value

If just one object is provided, an object of class `ic`. If multiple objects are provided, an object of class `iclist`.

### Author(s)

Paul-Christian Buerkner paul.buerkner@gmail.com

### 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, family = "gaussian")
    loo(fit1)

    # model with an additional varying intercept for subjects
    fit2 <- brm(rating ~ treat + period + carry + (1|subject),
                data = inhaler, family = "gaussian")
    # compare both models
    loo(fit1, fit2)

    ## End(Not run)

[Package brms version 2.6.0 Index]