brms: reloo.brmsfit – R documentation

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reloo.brmsfit

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. Models for problematic observations can be run in parallel using the future package.

Usage

## S3 method for class 'brmsfit'
reloo(
  x,
  loo,
  k_threshold = 0.7,
  newdata = NULL,
  resp = NULL,
  check = TRUE,
  ...
)

## S3 method for class 'loo'
reloo(x, fit, ...)

reloo(x, ...)

Arguments

`x`	An R object of class `brmsfit` or `loo` depending on the method.
`loo`	An R object of class `loo`.
`k_threshold`	The threshold at which Pareto k estimates are treated as problematic. Defaults to `0.7`. See `pareto_k_ids` for more details.
`newdata`	An optional data.frame for which to evaluate predictions. If `NULL` (default), the original data of the model is used. `NA` values within factors are interpreted as if all dummy variables of this factor are zero. This allows, for instance, to make predictions of the grand mean when using sum coding.
`resp`	Optional names of response variables. If specified, predictions are performed only for the specified response variables.
`check`	Logical; If `TRUE` (the default), some checks check are performed if the `loo` object was generated from the `brmsfit` object passed to argument `fit`.
`...`	Further arguments passed to `update.brmsfit` and `log_lik.brmsfit`.
`fit`	An R object of class `brmsfit`.

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 loo.

Examples

## Not run: 
fit1 <- brm(count ~ zAge + zBase * Trt + (1|patient),
            data = epilepsy, family = poisson())
# throws warning about some pareto k estimates being too high
(loo1 <- loo(fit1))
(reloo1 <- reloo(fit1, loo = loo1, chains = 1))

## End(Not run)

brms

Bayesian Regression Models using 'Stan'

v2.15.0

GPL-2

Authors

Paul-Christian Bürkner [aut, cre], Jonah Gabry [ctb], Sebastian Weber [ctb], Andrew Johnson [ctb], Martin Modrak [ctb]

Initial release

2021-03-10