loo_subsample.brmsfit: Efficient approximate leave-one-out cross-validation (LOO)...

In brms: Bayesian Regression Models using 'Stan'

Efficient approximate leave-one-out cross-validation (LOO) using subsampling

Description

Efficient approximate leave-one-out cross-validation (LOO) using subsampling

Usage

## S3 method for class 'brmsfit'
loo_subsample(x, ..., compare = TRUE, resp = NULL, model_names = NULL)

Arguments

x	A brmsfit object.
...	More brmsfit objects or further arguments passed to the underlying post-processing functions. In particular, see prepare_predictions for further supported arguments.
compare	A flag indicating if the information criteria of the models should be compared to each other via loo_compare.
resp	Optional names of response variables. If specified, predictions are performed only for the specified response variables.
model_names	If NULL (the default) will use model names derived from deparsing the call. Otherwise will use the passed values as model names.

Details

More details can be found on loo_subsample.

Examples

## Not run: 
# model with population-level effects only
fit1 <- brm(rating ~ treat + period + carry,
            data = inhaler)
(loo1 <- loo_subsample(fit1))

# model with an additional varying intercept for subjects
fit2 <- brm(rating ~ treat + period + carry + (1|subject),
            data = inhaler)
(loo2 <- loo_subsample(fit2))

# compare both models
loo_compare(loo1, loo2)

## End(Not run)

brms documentation built on Sept. 23, 2024, 5:08 p.m.