% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/reloo.R
\name{reloo.brmsfit}
\alias{reloo.brmsfit}
\alias{reloo.loo}
\alias{reloo}
\title{Compute exact cross-validation for problematic observations}
\usage{
\method{reloo}{brmsfit}(
  x,
  loo = NULL,
  k_threshold = 0.7,
  newdata = NULL,
  resp = NULL,
  check = TRUE,
  recompile = NULL,
  future_args = list(),
  ...
)

\method{reloo}{loo}(x, fit, ...)

reloo(x, ...)
}
\arguments{
\item{x}{An \R object of class \code{brmsfit} or \code{loo} depending
on the method.}

\item{loo}{An \R object of class \code{loo}. If \code{NULL},
brms will try to extract a precomputed \code{loo} object
from the fitted model, added there via \code{\link{add_criterion}}.}

\item{k_threshold}{The threshold at which Pareto \eqn{k}
estimates are treated as problematic. Defaults to \code{0.7}.
See \code{\link[loo:pareto-k-diagnostic]{pareto_k_ids}}
for more details.}

\item{newdata}{An optional data.frame for which to evaluate predictions. If
\code{NULL} (default), the original data of the model is used. \code{NA}
values within factors (excluding grouping variables) 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. \code{NA} values
within grouping variables are treated as a new level.}

\item{resp}{Optional names of response variables. If specified, predictions
are performed only for the specified response variables.}

\item{check}{Logical; If \code{TRUE} (the default), some checks
check are performed if the \code{loo} object was generated
from the \code{brmsfit} object passed to argument \code{fit}.}

\item{recompile}{Logical, indicating whether the Stan model should be
recompiled. This may be necessary if you are running \code{reloo} on
another machine than the one used to fit the model.}

\item{future_args}{A list of further arguments passed to
\code{\link[future:future]{future}} for additional control over parallel
execution if activated.}

\item{...}{Further arguments passed to
\code{\link{update.brmsfit}} and \code{\link{log_lik.brmsfit}}.}

\item{fit}{An \R object of class \code{brmsfit}.}
}
\value{
An object of the class \code{loo}.
}
\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
\pkg{future} package.
}
\details{
Warnings about Pareto \eqn{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
\pkg{\link[loo:loo-package]{loo}} package documentation).
If there are \eqn{J} observations with \eqn{k} estimates above
\code{k_threshold}, then \code{reloo} will refit the original model
\eqn{J} times, each time leaving out one of the \eqn{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 \eqn{J} observations that are stored in the
original \code{loo} object.
}
\examples{
\dontrun{
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))

# no more warnings after reloo
(reloo1 <- reloo(fit1, loo = loo1, chains = 1))
}

}
\seealso{
\code{\link{loo}}, \code{\link{kfold}}
}
