log_lik.brmsfit | R Documentation |
Compute the Pointwise Log-Likelihood
## 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,
...
)
object |
A fitted model object of class |
newdata |
An optional data.frame for which to evaluate predictions. If
|
re_formula |
formula containing group-level effects to be considered in
the prediction. If |
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 |
draw_ids |
An integer vector specifying the posterior draws to be used.
If |
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 |
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 |
cores |
Number of cores (defaults to |
... |
Further arguments passed to |
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.
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.
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