Feature Selection in caret rfe + sum with ROC

Question

I have been trying to apply recursive feature selection using caret package. What I need is that ref uses the AUC as performance measure. After googling for a month I cannot get

Answer 1

One problem is a minor typo ('trControl=' instead of 'trainControl='). Also, you change caretFuncs after you attached it to rfe's control function. Lastly, you will need to tell trainControl to calculate the ROC curves.

This code works:

 caretFuncs$summary <- twoClassSummary

 ctrl <- rfeControl(functions=caretFuncs, 
                    method = "cv",
                    repeats =5, number = 10,
                    returnResamp="final", verbose = TRUE)

 trainctrl <- trainControl(classProbs= TRUE,
                           summaryFunction = twoClassSummary)
 rf.profileROC.Radial <- rfe(mdrrDescr, mdrrClass, 
                             sizes=subsets,
                             rfeControl=ctrl,
                             method="svmRadial",
                             ## I also added this line to
                             ## avoid a warning:
                             metric = "ROC",
                             trControl = trainctrl)


 > rf.profileROC.Radial

 Recursive feature selection

 Outer resampling method: Cross-Validated (10 fold) 

 Resampling performance over subset size:

  Variables    ROC   Sens   Spec   ROCSD  SensSD  SpecSD Selected
          1 0.7805 0.8356 0.6304 0.08139 0.10347 0.10093         
          2 0.8340 0.8491 0.6609 0.06955 0.10564 0.09787         
          3 0.8412 0.8491 0.6565 0.07222 0.10564 0.09039         
          4 0.8465 0.8491 0.6609 0.06581 0.09584 0.10207         
          5 0.8502 0.8624 0.6652 0.05844 0.08536 0.09404         
          6 0.8684 0.8923 0.7043 0.06222 0.06893 0.09999         
          7 0.8642 0.8691 0.6913 0.05655 0.10837 0.06626         
          8 0.8697 0.8823 0.7043 0.05411 0.08276 0.07333         
          9 0.8792 0.8753 0.7348 0.05414 0.08933 0.07232        *
         10 0.8622 0.8826 0.6696 0.07457 0.08810 0.16550         
        342 0.8650 0.8926 0.6870 0.07392 0.08140 0.17367         

 The top 5 variables (out of 9):
    nC, X3v, Sp, X2v, X1v

For the prediction problems, you should use rf.profileROC.Radial instead of the fit component:

 > predict(rf.profileROC.Radial, head(mdrrDescr))
       pred    Active  Inactive
 1 Inactive 0.4392768 0.5607232
 2   Active 0.6553482 0.3446518
 3   Active 0.6387261 0.3612739
 4 Inactive 0.3060582 0.6939418
 5   Active 0.6661557 0.3338443
 6   Active 0.7513180 0.2486820

Max