brms (version 1.10.2)

reloo: Compute exact cross-validation for problematic observations

Description

Compute exact cross-validation for problematic observations for which approximate leave-one-out cross-validation may return incorrect results.

Usage

reloo(x, ...)
# S3 method for loo
reloo(x, fit, k_threshold = 0.7, check = TRUE, ...)

Arguments

x

An R object typically of class loo.

...

Further arguments passed to update.brmsfit such as iter, chains, or cores.

fit

An R object typically of class brmsfit.

k_threshold

The threshold at which pareto \(k\) estimates are treated as problematic. Defaults to 0.7. See pareto_k_ids for more details.

check

Logical; If TRUE (the default), a crude check is performed if the loo object was generated from the brmsfit object passed to argument fit.

Value

An object of the class as x.

Details

Warnings about Pareto \(k\) estimates indicate observations for which the approximation to LOO is problematic (this is described in detail in Vehtari, Gelman, and Gabry (2017) and the loo package documentation). If there are \(J\) observations with \(k\) estimates above k_threshold, then reloo will refit the original model \(J\) times, each time leaving out one of the \(J\) problematic observations. The pointwise contributions of these observations to the total ELPD are then computed directly and substituted for the previous estimates from these \(J\) observations that are stored in the original loo object.

See Also

loo, kfold

Examples

# NOT RUN {
fit1 <- brm(count ~ log_Age_c + log_Base4_c * Trt + (1|patient),
           data = epilepsy, family = poisson())
# throws warning about some pareto k estimates being too high
(loo1 <- loo(fit1))
(loo1 <- reloo(loo1, fit1))
# }
# NOT RUN {
# }

Run the code above in your browser using DataCamp Workspace