predict_generic: Generic SuperLearner prediction function
In osofr/gridisl: Discrete Super Learner with Grid-Search for Longitudinal Data

Generic SuperLearner prediction function

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predict_generic(modelfit, newdata, add_subject_data = FALSE,
  subset_idx = NULL, best_only = TRUE, holdout = FALSE,
  force_data.table = TRUE, verbose = getOption("gridisl.verbose"))

`modelfit`	Model fit object returned by `fit` functions. Must be an object of class `PredictionModel` or `PredictionStack`.
`newdata`	Subject-specific data for which predictions should be obtained.
`add_subject_data`	Set to `TRUE` to add the subject-level data to the resulting predictions (returned as a data.table). When `FALSE` (default) only the actual predictions are returned (as a matrix with each column representing predictions from a specific model).
`subset_idx`	A vector of row indices in `newdata` for which the predictions should be obtain. Default is `NULL` in which case all observations in `newdata` will be used for prediction.
`best_only`	Set to `TRUE` (default) to obtain predictions from the top-ranked model (based on validation or CV MSE). When `FALSE` the attempt will to made to obtain predictions from all models. Note that when `holdout` is `FALSE` and `best_only` is `TRUE`, the predictions will be based on the best scoring model that was re-trained on all available data.
`holdout`	Set to `TRUE` for out-of-sample predictions for validation folds or holdouts.
`force_data.table`	Force the prediction result to be `data.table`.
`verbose`	Set to `TRUE` to print messages on status and information to the console. Turn this on by default using `options(gridisl.verbose=TRUE)`.

A data.table of subject level predictions (subject are rows, columns are different models) or a data.table with subject level covariates added along with model-based predictions.

osofr/gridisl documentation built on May 24, 2019, 4:55 p.m.