loo.brmsfit | R Documentation |

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

.

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

`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, |

`moment_match` |
Logical; Indicate whether |

`reloo` |
Logical; Indicate whether |

`k_threshold` |
The threshold at which pareto |

`save_psis` |
Should the |

`moment_match_args` |
Optional |

`reloo_args` |
Optional |

`model_names` |
If |

See `loo_compare`

for details on model comparisons.
For `brmsfit`

objects, `LOO`

is an alias of `loo`

.
Use method `add_criterion`

to store
information criteria in the fitted model object for later usage.

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

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

.

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.

## Not run: # model with population-level effects only fit1 <- brm(rating ~ treat + period + carry, data = inhaler) (loo1 <- loo(fit1)) # model with an additional varying intercept for subjects fit2 <- brm(rating ~ treat + period + carry + (1|subject), data = inhaler) (loo2 <- loo(fit2)) # compare both models loo_compare(loo1, loo2) ## End(Not run)

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