reloo.brmsfit | R Documentation |
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.
## S3 method for class 'brmsfit'
reloo(
x,
loo = NULL,
k_threshold = 0.7,
newdata = NULL,
resp = NULL,
check = TRUE,
recompile = NULL,
future_args = list(),
...
)
## 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 |
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 |
recompile |
Logical, indicating whether the Stan model should be
recompiled. This may be necessary if you are running |
future_args |
A list of further arguments passed to
|
... |
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
.
loo
, kfold
## 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))
# no more warnings after reloo
(reloo1 <- reloo(fit1, loo = loo1, chains = 1))
## End(Not run)
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