Hello, As I couldn't find anywhere in the help to rpart which element in the loss matrix means which loss, I played with this parameter and became a bit confused. What I did was this: I used kyphosis data(classification absent/present, number of 'absent' cases is 64, of 'present' cases 17) and I tried the following
> lmat=matrix(c(0,17,64,0),ncol=2) > lmat [,1] [,2] [1,] 0 64 [2,] 17 0 > set.seed(1003) > fit1<-rpart(Kyphosis~.,data=kyphosis,parms=list(loss=lmat)) > set.seed(1003) > fit2<-rpart(Kyphosis~.,data=kyphosis,parms=list(prior=c(0.5,0.5))) The results I obtained were identical, so I concluded that the losses were [L(true, predicted)]: L(absent,present)=17 L(present,absent)=64. And thus the arrangement of the elements in the loss matrix seemed clear as absent is considered as class 1 and present as class 2 and my problem seemed to be solved. However, I tried also this: >residuals(fit1) and became confused. Because for each misclassified 'absent' the residual(which should be loss in this case) was 64, while for a misclassified 'present' it was 17 (in contradiction to the previous.) So am I wrong somewhere? Is the arrangement of elements in the loss matrix such as I deduced it from fitting fit1 and fit2? Thanks for any comments. Barbora ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.