Compute the Pointwise Log-Likelihood
# S3 method for brmsfit
log_lik(
object,
newdata = NULL,
re_formula = NULL,
resp = NULL,
nsamples = NULL,
subset = NULL,
pointwise = FALSE,
combine = TRUE,
add_point_estimate = FALSE,
cores = getOption("mc.cores", 1),
...
)A fitted model object of class brmsfit.
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.
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.
Optional names of response variables. If specified, predictions are performed only for the specified response variables.
Positive integer indicating how many posterior samples should
be used. If NULL (the default) all samples are used. Ignored if
subset is not NULL.
A numeric vector specifying the posterior samples to be used.
If NULL (the default), all samples are used.
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.
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.
For internal use only. Ensures compatibility
with the loo_subsample method.
Number of cores (defaults to 1).
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
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.
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.