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,
  ...
)

Arguments

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.

Value

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.

Details

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.