# Using partial AUC as Caret metric for cross-validation?

I'm evaluating a grid of tuning parameters using Caret with metric="ROC" for cross-validation. Is there any simple way to use as metric the area under the curve for an specified interval of the ROC curve?

My code is similar to this:

fitControl <- trainControl(method = "repeatedcv",
number = 10,
repeats = 10,
classProbs = TRUE,
## Evaluate performance using
## the following function
summaryFunction = twoClassSummary)

gbmFit3 <- train(Class ~ ., data = training,
method = "nnet",
trControl = fitControl,
verbose = FALSE,
tuneGrid = myGrid,
metric = "ROC")


And I would like, using caret, a metric as the partial area under the curve. The most simple way I think it's using cross-validation + pROC package without using caret, but I would like to know if there is a simple way to do it before I try my custom cv.

Anyone?

You can emulate what the package's twoClassSummary function does. See the help page for custom performance metrics.