Description Usage Arguments Value
View source: R/fit_SuperLearner_funs.R
Generic modeling function for longitudinal data.
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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 |
y |
A character string name of the column that represent the response variable in the model. |
train_data |
Input dataset, can be a |
valid_data |
Optional |
models |
Parameters specifying the model(s) to fit. This must be a result of calling |
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 |
An R6 object containing the model fit(s).
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