This is a probably a daft question, but I would appreciate some help. I want to attempt to separate groups in a dataset using discriminant function analysis, and have been using linear discriminant analysis (lda(klaR)) and canonical discriminant analysis (candisc(candisc)). # CDA: iris.mod <- lm(cbind(Petal.Length, Sepal.Length, Petal.Width, Sepal.Width) ~ Species, data=iris) iris.can <- candisc(iris.mod, data=iris) # LDA: iris.lda<-lda(Species~Petal.Length+Sepal.Length+Petal.Width+Sepal.Width,data=iris) However, I now want to make classifications based on these discriminant functions. With LDA, this is easy: iris.lda.predict<-predict.lda(iris.lda) but I can't find a straightforward way of classifying from the CDA results, since predict() doesn't have a method for the candisc class. So how can I classify/predict from a candisc object? I think I may be misunderstanding something fairly fundamental about LDA vs CDA, but any suggestions would be gratefully received! Thanks. -- View this message in context: http://r.789695.n4.nabble.com/Classification-from-candisc-tp3800492p3800492.html Sent from the R help mailing list archive at Nabble.com.