kfold_predict: Predictions from K-Fold Cross-Validation

In brms: Bayesian Regression Models using 'Stan'

Predictions from K-Fold Cross-Validation

Description

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

Usage

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

Arguments

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.

Value

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.

See Also

kfold

Examples

## Not run: 
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)

## End(Not run)

brms documentation built on Sept. 23, 2024, 5:08 p.m.