Compute and evaluate predictions after performing K-fold
cross-validation via `kfold`

.

`kfold_predict(x, method = "posterior_predict", resp = NULL, ...)`

- x
Object of class

`'kfold'`

computed by`kfold`

. For`kfold_predict`

to work, the fitted model objects need to have been stored via argument`save_fits`

of`kfold`

.- method
Method used to obtain predictions. Can be set to

`"posterior_predict"`

(the default),`"posterior_epred"`

, or`"posterior_linpred"`

. For more details, see the respective function documentations.- resp
Optional names of response variables. If specified, predictions are performed only for the specified response variables.

- ...
Further arguments passed to

`prepare_predictions`

that control several aspects of data validation and prediction.

A `list`

with two slots named `'y'`

and `'yrep'`

.
Slot `y`

contains the vector of observed responses.
Slot `yrep`

contains the matrix of predicted responses,
with rows being posterior draws and columns being observations.

```
if (FALSE) {
fit <- brm(count ~ zBase * Trt + (1|patient),
data = epilepsy, family = poisson())
# perform k-fold cross validation
(kf <- kfold(fit, save_fits = TRUE, chains = 1))
# define a loss function
rmse <- function(y, yrep) {
yrep_mean <- colMeans(yrep)
sqrt(mean((yrep_mean - y)^2))
}
# predict responses and evaluate the loss
kfp <- kfold_predict(kf)
rmse(y = kfp$y, yrep = kfp$yrep)
}
```