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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.