kfold_predict: Predictions from K-Fold Cross-Validation

View source: R/kfold.R

kfold_predictR Documentation

Predictions from K-Fold Cross-Validation


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


kfold_predict(x, method = c("predict", "fitted"), resp = NULL, ...)



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.


The method used to make predictions. Either "predict" or "fitted". See predict.brmsfit for details.


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.

See Also



## 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. 19, 2022, 5:06 p.m.