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

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



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.


A flag indicating if the information criteria of the models should be compared to each other via loo_compare.


Optional names of response variables. If specified, predictions are performed only for the specified response variables.


If NULL (the default) will use model names derived from deparsing the call. Otherwise will use the passed values as model names.


More details can be found on loo_subsample.


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)