Dear all, I'm currently exploring a dataset with the help of conditional inference trees (still very much a beginner with this technique & log. reg. methods as a whole t.b.h.), since they explained more variation in my dataset than a binary logistic regression with /glm/. I started out with the /party /package, but after I while I ran into the 'updated' /partykit /package and tried this out, too. Now, the strange thing is that both trees look quite different - actually even the very first split is different. So I did some research and came across the 'forest' concept. However, it seems that the /varImp /function does not yet work in the /partykit /implementation, which raises the question for me how I should evaluate the /partykit /forest - how can I find out whether the variables are important in the forest as in my /partykit /tree? Is there some way to do this or some other solution for this problem? I'd prefer to continue the /partykit /implementation of ctree, since it allows more settings for the final plot, which I'd need to get the final (large) plot into a readable form.
Related to this project, I'd also like to give statistics for the overall model, e.g. overall significance, Nagelkerke's R², a C-value. After a 'regular' binary log. reg., I would use the lrm function to get these values, but I am unsure whether it would be correct to also apply this method to my tree data. Any help would be greatly appreciated! -- Christopher -- View this message in context: http://r.789695.n4.nabble.com/Trees-and-Forests-with-packages-party-vs-partykit-Different-results-tp4712214.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
