First of all, thanks a lot to you and your team for such a wonderful R package.
I have a question, though, regarding the ROC curve, and the value of outcome represented in the plot (I attach an example, where S and M are my two groups)
First, what is the outcome shown in the plot? Accuracy? how is it decided?
second, if this value is computed using cross-validation, what are the default values of the folds and the nrepeats?
I have read some documentation were it states:
An AUC plot can also be obtained using the function auroc, where the AUC is calculated from training cross-validation sets and averaged (see the perf function outputs for each component, perf.plsda.srbct$auc and…
and here, were it states:
As PLS-DA acts as a classifier, we can plot a ROC Curve to complement the sPLS-DA classification performance results detailed in 4.7.5. The AUC is calculated from training cross-validation sets and averaged.