Does k-fold cross-validation with the function optimPenalLikL2 and returns the values of lamda and w that maximize the area under the ROC.
tuneParamCL2(Data, nfolds = nfolds, grid, algorithm = c("QN"))a data frame, as a first column should have the response variable y and the other columns the predictors
the number of folds used for cross-validation. OBS! nfolds>=2
a grid (data frame) with values of lamda and w that will be used for tuning to tune the model. It is created by expand.grid see example below
choose between BFGS ("QN") and hjk (Hooke-Jeeves optimization free) to be used for optmization
A matrix with the following: the average (over folds) cross-validated AUC, the totalVariables selected on the training set, and the standard deviation of the AUC over the nfolds
It supports the BFGS optimization method ('QN') from the optim stats function, the Hooke-Jeeves derivative-free minimization algorithm ('hjk'). The value of lamda and w that yield the maximum AUC on the cross-validating data set is selected. If more that one value of lamda nad w yield the same AUC, then the biggest values of lamda and w are choosen.