Does anyone know how to perform a decision curve analysis for a random forest? I could not find any codes on the internet, even though i read papers that report this. I used the caret package to create my model and thought maybe through the predict function i could get the necessary information, however, this does not work. Really appreciate any help, especially as there seems to be no information available!
This is what i tried (model was tuned before)
tunegrid <- expand.grid(.mtry=mtry) model <- train( Lymph_node_involvement ~ predictors, data = train.data, method = "rf", ntree = 800, preProcess = c("scale", "center"), tuneGrid = tunegrid, trControl = trainControl( method = "cv", summaryFunction = twoClassSummary, classProbs = T, savePredictions = T, ), importance = TRUE, metric = "ROC" ) LNIClasses <- predict(model, newdata = test.data, type="raw") LNIClasses <- as.data.frame(LNIClasses ) result2 = dca(data="test.data", outcome="Lymph_node_involvement", predictors="LNIClasses ", smooth="TRUE", xstop=0.50)``