Saves cross-validation results in a table with the
user-defined directory and outputs penalty term with the highest testing canonical correlation,
lowest prediction error, and lowest scaled prediction error.
Usage
aggregateCVSingle(CVDir, SCCAmethod = "SmCCA", K = 5, NumSubsamp = 500)
Arguments
- CVDir
A directory where the result is stored.
- SCCAmethod
The canonical correlation analysis method that is used in
the model, used to name cross-validation table file, default is set to 'SmCCA'.
- K
number of folds for cross-validation.
- NumSubsamp
Number of subsampling used.
Value
A vector of length 3 with indices of the penalty term that (1) maximize the testing canonical correlation,
(2) minimize the prediction error and (3) minimize the scaled prediction error.