fit_model: Generic modeling function for longitudinal data.
In osofr/longGriDiSL: Discrete Super Learner with Grid-Search for Longitudinal Data

Generic modeling function for longitudinal data.

1
2
3

fit_model(ID, t_name, x, y, train_data, valid_data, models, nfolds, fold_column,
  seed, useH2Oframe = FALSE, subset_exprs = NULL, subset_idx = NULL,
  verbose = getOption("gridisl.verbose"), ...)

`ID`	A character string name of the column that contains the unique subject identifiers.
`t_name`	A character string name of the column with integer-valued measurement time-points (in days, weeks, months, etc).
`x`	A vector containing the names of predictor variables to use for modeling. If x is missing, then all columns except `ID`, `y` are used.
`y`	A character string name of the column that represent the response variable in the model.
`train_data`	Input dataset, can be a `data.frame` or a `data.table`.
`valid_data`	Optional `data.frame` or `data.table` containing the validation data. When provided, this dataset will be used for scoring the final model fit(s). Can be used with either method = "cv" or "holdout". When method = "cv", the validation data must have exactly the same number of rows as the `train_data`. Each CV model will be re-scored (MSE) based on validation fold rows in `valid_data`.
`models`	Parameters specifying the model(s) to fit. This must be a result of calling `defModel(...) + defModel(...)` functions.
`nfolds`	Number of folds to use in cross-validation.
`fold_column`	The name of the column in the input data that contains the cross-validation fold indicators (must be an ordered factor).
`seed`	Random number seed for selecting random holdouts or validation folds.
`useH2Oframe`	Use existing H2OFrame object (if modeling with h2o R package) in input data object, rather than loading a new H2OFrame.
`subset_exprs`	(Optional) Specify a logical R expression (as character string) for selecting training / validation rows in the input data. The expression will be evaluated in the environment of the input data. By default all rows of the input data will be used.
`subset_idx`	(Optional) Specify an vector index of rows in the input data to be used in model fitting / validation. By default all rows of the input data will be used.
`verbose`	Set to `TRUE` to print messages on status and information to the console. Turn this on by default using `options(gridisl.verbose=TRUE)`.