ROC curve from train/test set in caret R package

Question

I am working to plot a ROC curve of a model that uses a test/train set created with the caret R package. I either am not putting in the right data to plot or am missing something about the creation of my test/train set. Any insight??

*Edited with correct answer

library(caret)
library(mlbench)
set.seed(506)
data(whas)
inTrain <- createDataPartition(y = whas$bin.frail,
p = .75, list = FALSE)
str(inTrain)
training <- whas[ inTrain,]
testing <- whas[-inTrain,]
nrow(training)
nrow(testing)
tc <- trainControl("cv", 10, savePredictions=T)  #"cv" = cross-validation, 10-fold
mod1 <- train(bin.frail ~ ,
                      data      = training    ,
                      method    = "glm"    ,
                      family    = binomial ,
                      trControl = tc)

library(pROC)
mod1pred<- predict(mod1, newdata=testingresponse="prob")
plot(roc(testing$bin.frail, mod1pred[[2]]), print.auc=TRUE, col="red", 
xlim=c(0,1))

Answer 1

It's hard to know for sure without a reproducible answer, but presumably your response variable bin.frail isn't numeric. For example, it might be coded using letters (eg, "Y", "N"); or with numbers which are being stored as a factor. You could check this using is.numeric(whas$bin.frail) .

As a side note, in your call to roc() it looks like mod1pred is being created from your training data whereas testing$bin.frail is from your test data. You could correct this by adding newdata = testing to your call to predict when creating mod1pred .