Classification MCCV, aims to find the best feature subsets using default model parameters
PerformCV.explore(mSetObj, cls.method, rank.method="auroc", lvNum=2, propTraining=2/3)Input the name of the created mSetObj (see InitDataObjects)
Select the classification method, "rf" for random forest classification, "pls" for PLS-DA, and "svm" for support vector machine
Select the ranking method, "rf" for random forest mean decrease accuracy, "fisher" for Fisher's univariate ranking based on area under the curve "auroc" for univariate ranking based on area under the curve, "tt" for T-test univariate ranking based on area under the curve, "pls" for partial least squares, and "svm" for support vector machine
Input the number of latent variables to include in the analyis, only for PLS-DA classification
Input the proportion of samples to use for training