longGriDiSL: Discrete Super Learner with Grid-Search for Longitudinal Data

Description Usage Arguments Value

By default this function will extract out-of-sample/validation/holdout predictions from original training data (automatically done by h2o) to evaluate the cross-validated MSE. However, when newdata is supplied, the predictions for each CV model will be based on this external validation dataset. These predictions and the outcome stored in newdata are then used to re-evalute the CV MSE. Note that newdata must be of the same dimensionality as the original training data used for fitting the h2o models.

1 2	eval_MSE(modelfit, newdata, subset_idx = NULL, verbose = getOption("gridisl.verbose"))

`modelfit`	Model fit object returned by `fit_model` function.
`newdata`	Optional new validation data for evaluating MSE, either a `data.table` or `DataStorageClass` object.
`subset_idx`	Optional row indices if MSE needs to be evaluating for a subset of the input data.
`verbose`	Set to `TRUE` to print messages on status and information to the console. Turn this on by default using `options(gridisl.verbose=TRUE)`.