log_lik.brmsfit: Compute the Pointwise Log-Likelihood

In brms: Bayesian Regression Models using 'Stan'

Compute the Pointwise Log-Likelihood

Description

Compute the Pointwise Log-Likelihood

Usage

## 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,
  ...
)

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.

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.

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.

brms documentation built on Sept. 26, 2023, 1:08 a.m.