Efficient approximate leave-one-out cross-validation (LOO) using subsampling
Efficient approximate leave-one-out cross-validation (LOO) using subsampling
## S3 method for class 'brmsfit' loo_subsample(x, ..., compare = TRUE, resp = NULL, model_names = NULL)
x |
A |
... |
More |
compare |
A flag indicating if the information criteria
of the models should be compared to each other
via |
resp |
Optional names of response variables. If specified, predictions are performed only for the specified response variables. |
model_names |
If |
More details can be found on
loo_subsample.
## 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)
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