predict_growth: Predict from growth-curve SuperLearner fit
In osofr/growthcurveSL: SuperLearner for Imputing Growth Curves

Predict from growth-curve SuperLearner fit

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predict_growth(modelfit, newdata, add_subject_data = FALSE, grid = FALSE,
  subset_idx = NULL, holdout = FALSE, stack = TRUE,
  verbose = getOption("growthcurveSL.verbose"))

`modelfit`	Model fit object returned by `fit_growth`.
`newdata`	Subject-specific data for which predictions should be obtained. If missing then the predictions for the training data will be typically returned. See `holdout` for discussion of alternative cases.
`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).
`grid`	Optional flag, set to `TRUE` when making growth curve predictions for an entire grid of<c2><a0>equally spaced time-points.
`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.
`holdout`	Set to `TRUE` for out-of-sample predictions for validation folds (out-of-sample observations) or holdouts. When `newdata` is missing there are two possible types of holdout predictions, depending on the modeling approach. 1. For `method = "holdout"` the default holdout predictions will be based on validation data. 2. For `method = "cv"` the default is to leave use the previous out-of-sample (holdout) predictions from the training data.
`stack`	Stack the predictions from individual models into a single vector of Super Learner predictions (default). If set to `FALSE` the usual by model (by library) predictions are returned for every successful model fit in the library.
`verbose`	Set to `TRUE` to print messages on status and information to the console.

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/growthcurveSL documentation built on May 24, 2019, 4:56 p.m.