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

# S3 method for 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

if (FALSE) {
# 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)
}