Compute the Pointwise Log-Likelihood

```
# S3 method for 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,
...
)
```

- 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.

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.

`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.