Add model fit criteria to model objects

add_criterion(x, ...)

# S3 method for brmsfit
add_criterion(
  x,
  criterion,
  model_name = NULL,
  overwrite = FALSE,
  file = NULL,
  force_save = FALSE,
  ...
)

Arguments

x: An R object typically of class brmsfit.
...: Further arguments passed to the underlying functions computing the model fit criteria. If you are recomputing an already stored criterion with other ... arguments, make sure to set overwrite = TRUE.
criterion: Names of model fit criteria to compute. Currently supported are "loo", "waic", "kfold", "loo_subsample", "bayes_R2" (Bayesian R-squared), "loo_R2" (LOO-adjusted R-squared), and "marglik" (log marginal likelihood).
model_name: Optional name of the model. If NULL (the default) the name is taken from the call to x.
overwrite: Logical; Indicates if already stored fit indices should be overwritten. Defaults to FALSE. Setting it to TRUE is useful for example when changing additional arguments of an already stored criterion.
file: Either NULL or a character string. In the latter case, the fitted model object including the newly added criterion values is saved via saveRDS in a file named after the string supplied in file. The .rds extension is added automatically. If x was already stored in a file before, the file name will be reused automatically (with a message) unless overwritten by file. In any case, file only applies if new criteria were actually added via add_criterion or if force_save was set to TRUE.
force_save: Logical; only relevant if file is specified and ignored otherwise. If TRUE, the fitted model object will be saved regardless of whether new criteria were added via add_criterion.

Value

An object of the same class as x, but with model fit criteria added for later usage.

Details

Functions add_loo and add_waic are aliases of add_criterion with fixed values for the criterion argument.

Examples

if (FALSE) {
fit <- brm(count ~ Trt, data = epilepsy)
# add both LOO and WAIC at once
fit <- add_criterion(fit, c("loo", "waic"))
print(fit$criteria$loo)
print(fit$criteria$waic)
}