R: Compute exact cross-validation for problematic observations

reloo {brms}

R Documentation

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 class 'loo'
reloo(x, fit, k_threshold = 0.7, check = TRUE,
  resp = NULL, ...)

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

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.

Value

An object of the class as x.

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))

## End(Not run)

[Package brms version 2.6.0 Index]