add_holdout_ind: Define and fit growth models evaluated on holdout...
In osofr/GriDiSL: Discrete Super Learner with Grid-Search for Longitudinal Data
Description
Usage
Arguments
Value
Description
Define and fit growth models evaluated on holdout observations.
Usage
1
2
add_holdout_ind(data, ID, hold_column = "hold", random = TRUE,
seed = NULL)
Arguments
data
Input dataset, can be a data.frame or a data.table.
ID
A character string name of the column that contains the unique subject identifiers.
hold_column
A name of the column that will contain the the holdout indicators
random
Logical, specifying if the holdout observations should be selected at random.
If FALSE then the last observation for each subject is selected as a holdout.
seed
Random number seed for selecting a random holdout.
Value
An input data with added holdout indicator column (TRUE for holdout observation indicator, FALSE for training observation indicator).
osofr/GriDiSL documentation built on May 24, 2019, 4:54 p.m.
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Description Usage Arguments Value
Description
Define and fit growth models evaluated on holdout observations.
Usage
1 2 | add_holdout_ind(data, ID, hold_column = "hold", random = TRUE,
seed = NULL)
|
Arguments
data |
Input dataset, can be a |
ID |
A character string name of the column that contains the unique subject identifiers. |
hold_column |
A name of the column that will contain the the holdout indicators |
random |
Logical, specifying if the holdout observations should be selected at random. If FALSE then the last observation for each subject is selected as a holdout. |
seed |
Random number seed for selecting a random holdout. |
Value
An input data with added holdout indicator column (TRUE for holdout observation indicator, FALSE for training observation indicator).
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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