R: Compute the Pointwise Log-Likelihood

log_lik.brmsfit {brms} | R Documentation

## Compute the Pointwise Log-Likelihood

### Description

Compute the Pointwise Log-Likelihood

### Usage

    ## S3 method for class 'brmsfit'
    log_lik(object, newdata = NULL, re_formula = NULL,
      resp = NULL, nsamples = NULL, subset = NULL, pointwise = FALSE,
      combine = TRUE, ...)

### Arguments

`object` | A fitted model object of class `brmsfit`.
`newdata` | An optional data.frame for which to evaluate predictions. If `NULL` (default), the original data of the model is used.
`re_formula` | formula containing group-level effects to be considered in the prediction. If `NULL` (default), include all group-level effects; if `NA`, include no group-level effects.
`resp` | Optional names of response variables. If specified, predictions are performed only for the specified response variables.
`nsamples` | Positive integer indicating how many posterior samples should be used. If `NULL` (the default) all samples are used. Ignored if `subset` is not `NULL`.
`subset` | A numeric vector specifying the posterior samples to be used. If `NULL` (the default), all samples are used.
`pointwise` | A flag indicating whether to compute the full log-likelihood matrix at once (the default), or just return the likelihood function along with all data and samples required to compute the log-likelihood separately for each observation. The latter option is rarely useful when calling `log_lik` directly, but rather when computing `waic` or `loo`.
`combine` | Only relevant in multivariate models. Indicates if the log-likelihoods of the submodels should be combined per observation (i.e. added together; the default) or if the log-likelihoods should be returned separately.
`...` | Further arguments passed to `extract_draws` that control several aspects of data validation and prediction.

### Value

Usually, an S x N matrix containing the pointwise log-likelihood samples, where S is the number of samples and N is the number of observations in the data. For multivariate models and if `combine` is `FALSE`, an S x N x R array is returned, where R is the number of response variables. If `pointwise = TRUE`, the output is a function with a `draws` attribute containing all relevant data and posterior samples.

[Package brms version 2.6.0 Index]