Hi
I did not see any response but what about
par(mfrow=c(2,2))
before plotting?
Cheers
Petr
> -----Original Message-----
> From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Elahe
chalabi
> via R-help
> Sent: Monday, October 16, 2017 2:26 PM
> To: R-help Mailing List <r-help at r-project.org>
> Subject: [R] ROC curve for each fold in one plot
>
> Hi all,
>
> I have tried a 5 fold cross validation using caret package with random
forest
> method on iris dataset as example. Then I need ROC curve for each fold:
>
>
> > set.seed(1)
> > train_control <- trainControl(method="cv",
number=5,savePredictions > TRUE,classProbs = TRUE)
> > output <- train(Species~., data=iris, trControl=train_control,
method="rf")
> > library(pROC)
> > selectedIndices <- output$pred$Resample == "Fold1"
> >
>
plot.roc(output$pred$obs[selectedIndices],output$pred$setosa[selectedIndices
> ]) > selectedIndices <- output$pred$Resample == "Fold2"
> >
>
plot.roc(output$pred$obs[selectedIndices],output$pred$setosa[selectedIndices
> ])
> > selectedIndices <- output$pred$Resample == "Fold3"
> >
>
plot.roc(output$pred$obs[selectedIndices],output$pred$setosa[selectedIndices
> ])
>
> and the same for Fold4 and Fold5,now how can I bring all the plots in one
plot
> with labels for each fold?
>
> Thanks for any help!
> Elahe
>
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