Compute the Pointwise Log-Likelihood
# S3 method for brmsfit
log_lik(object, newdata = NULL, re_formula = NULL,
allow_new_levels = FALSE, sample_new_levels = "uncertainty",
new_objects = list(), incl_autocor = TRUE, subset = NULL,
nsamples = NULL, pointwise = FALSE, nug = NULL, ...)A fitted model object of class brmsfit.
An optional data.frame for which to evaluate predictions.
If NULL (default), the orginal 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.
A flag indicating if new
levels of group-level effects are allowed
(defaults to FALSE).
Only relevant if newdata is provided.
Indicates how to sample new levels
for grouping factors specified in re_formula.
This argument is only relevant if newdata is provided and
allow_new_levels is set to TRUE.
If "uncertainty" (default), include group-level uncertainty
in the predictions based on the variation of the existing levels.
If "gaussian", sample new levels from the (multivariate)
normal distribution implied by the group-level standard deviations
and correlations. This options may be useful for conducting
Bayesian power analysis.
If "old_levels", directly sample new levels from the
existing levels.
A numeric vector specifying
the posterior samples to be used.
If NULL (the default), all samples are used.
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 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.
Small positive number for Gaussian process terms only.
For numerical reasons, the covariance matrix of a Gaussian
process might not be positive definite. Adding a very small
number to the matrix's diagonal often solves this problem.
If NULL (the default), nug is chosen internally.
Currently ignored
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.
If pointwise = TRUE, the output is a function
with a draws attribute containing all relevant
data and posterior samples.