reloo.brmsfit {brms}  R Documentation 
Compute exact crossvalidation for problematic observations for which approximate leaveoneout crossvalidation may return incorrect results. Models for problematic observations can be run in parallel using the future package.
## 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, ...)
x 
An R object of class 
loo 
An R object of class 
k_threshold 
The threshold at which Pareto k
estimates are treated as problematic. Defaults to 
newdata 
An optional data.frame for which to evaluate predictions. If

resp 
Optional names of response variables. If specified, predictions are performed only for the specified response variables. 
check 
Logical; If 
... 
Further arguments passed to

fit 
An R object of class 
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.
An object of the class loo
.
## Not run: fit1 < brm(count ~ zAge + zBase * Trt + (1patient), 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)