brms (version 2.22.0)

add_criterion: Add model fit criteria to model objects

Description

Add model fit criteria to model objects

Usage

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

Value

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

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.

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)
}