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,
      ndraws = NULL,
      draw_ids = NULL,
      pointwise = FALSE,
      combine = TRUE,
      add_point_estimate = FALSE,
      cores = NULL,
      ...
    )

### 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. `NA` values within factors are interpreted as if all dummy variables of this factor are zero. This allows, for instance, to make predictions of the grand mean when using sum coding.
`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.
`ndraws` | Positive integer indicating how many posterior draws should be used. If `NULL` (the default) all draws are used. Ignored if `draw_ids` is not `NULL`.
`draw_ids` | An integer vector specifying the posterior draws to be used. If `NULL` (the default), all draws 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 draws 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.
`add_point_estimate` | For internal use only. Ensures compatibility with the `loo_subsample` method.
`cores` | Number of cores (defaults to `1`). On non-Windows systems, this argument can be set globally via the `mc.cores` option.
`...` | Further arguments passed to `prepare_predictions` that control several aspects of data validation and prediction.

### Details

`NA` values within factors in `newdata`, are interpreted as if all dummy variables of this factor are zero. This allows, for instance, to make predictions of the grand mean when using sum coding.

In multilevel models, it is possible to allow new levels of grouping factors to be used in the predictions. This can be controlled via argument `allow_new_levels`. New levels can be sampled in multiple ways, which can be controlled via argument `sample_new_levels`. Both of these arguments are documented in `prepare_predictions` along with several other useful arguments to control specific aspects of the predictions.

### Value

Usually, an S x N matrix containing the pointwise log-likelihood draws, where S is the number of draws 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 draws.

[Package brms version 2.16.1 Index]