R: Predictions from K-Fold Cross-Validation

kfold_predict {brms} | R Documentation

## Predictions from K-Fold Cross-Validation

### Description

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

### Usage

    kfold_predict(x, method = c("predict", "fitted"), 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` | The method used to make predictions. Either `"predict"` or `"fitted"`. See `predict.brmsfit` for details.
`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)

[Package brms version 2.16.3 Index]