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, ...)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.
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.
Further arguments passed to extract_draws
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.