How to compute ROC and AUC under ROC after training using caret in R?

Question

I have used caret package\'s train function with 10-fold cross validation. I also have got class probabilities for predicted classes by setting

Answer 1

A sample example for AUC:

rf_output=randomForest(x=predictor_data, y=target, importance = TRUE, ntree = 10001, proximity=TRUE, sampsize=sampsizes)

library(ROCR)
predictions=as.vector(rf_output$votes[,2])
pred=prediction(predictions,target)

perf_AUC=performance(pred,"auc") #Calculate the AUC value
AUC=perf_AUC@y.values[[1]]

perf_ROC=performance(pred,"tpr","fpr") #plot the actual ROC curve
plot(perf_ROC, main="ROC plot")
text(0.5,0.5,paste("AUC = ",format(AUC, digits=5, scientific=FALSE)))

or using pROC and caret

library(caret)
library(pROC)
data(iris)


iris <- iris[iris$Species == "virginica" | iris$Species == "versicolor", ]
iris$Species <- factor(iris$Species)  # setosa should be removed from factor



samples <- sample(NROW(iris), NROW(iris) * .5)
data.train <- iris[samples, ]
data.test <- iris[-samples, ]
forest.model <- train(Species ~., data.train)

result.predicted.prob <- predict(forest.model, data.test, type="prob") # Prediction

result.roc <- roc(data.test$Species, result.predicted.prob$versicolor) # Draw ROC curve.
plot(result.roc, print.thres="best", print.thres.best.method="closest.topleft")

result.coords <- coords(result.roc, "best", best.method="closest.topleft", ret=c("threshold", "accuracy"))
print(result.coords)#to get threshold and accuracy

Answer 2

Update 2019. This is what MLeval was written for (https://cran.r-project.org/web/packages/MLeval/index.html), it works with the Caret train output object to make ROCs, PR curves, calibration curves, and calculate metrics, such as ROC-AUC, sensitivity, specificity etc. It just uses one line to do all of this which is helpful for my analyses and may be of interest.

library(caret)
library(MLeval)

myTrainingControl <- trainControl(method = "cv", 
                                  number = 10, 
                                  savePredictions = TRUE, 
                                  classProbs = TRUE, 
                                  verboseIter = TRUE)

randomForestFit = train(x = Sonar[,1:60], 
                        y = as.factor(Sonar$Class), 
                        method = "rf", 
                        trControl = myTrainingControl, 
                        preProcess = c("center","scale"), 
                        ntree = 50)

##

x <- evalm(randomForestFit)

## get roc curve plotted in ggplot2

x$roc

## get AUC and other metrics

x$stdres