ROC function error "Predictor must be numeric or ordered."

Question

I am not able to get ROC function to work, I get the error "Predictor must be numeric or ordered".

I've looked through other posts, but nothing solves my problem. Any help is highly appreciated.

"Get data"
flying=dget("https://www.math.ntnu.no/emner/TMA4268/2019v/data/flying.dd")
ctrain=flying$ctrain
ctest=flying$ctest


library(MASS)
fly_qda=qda(diabetes~., data=ctrain)


#Test error is given below:
predict_qda=predict(fly_qda, newdata=ctest, probability=TRUE)
table_qda<-table(ctest$diabetes, predict_qda$class)
error_qda<-1-sum(diag(table_qda))/sum(table_qda)
error_qda

"ROC curve and AUC"
predict_qdatrain<-predict(fly_qda, newdata=ctrain)
roc_qda=roc(response=ctrain$diabetes, predictor= predict_qdatrain$class, plot=TRUE)
plot(roc_qda, col="red", lwd=3, main="ROC curve QDA")
auc_qda<-auc(roc_qda)

I want the plotted ROC curve and AUC

Answer 1

As Ollie Perkins explained in his answer, the error you are getting indicates that your are passing something that is not of sortable nature and therefore cannot be used for ROC analysis. In the case of the predict.qda, the class item is a factor with 1s and 0s indicating the class.

Instead of converting the class to an ordered predictor, it is a better idea to use the posterior probabilities instead. Let's use the probability to belong to class 1:

roc_qda <- roc(response = ctrain$diabetes, predictor = predict_qdatrain$posterior[,"1"])
plot(roc_qda, col="red", lwd=3, main="ROC curve QDA")
auc(roc_qda)

This will give you a smoother curve and more classification thresholds to choose from.