[R] Trees (and Forests) with packages 'party' vs. 'partykit': Different results
ch_koch at gmx.de
Mon Sep 14 12:11:34 CEST 2015
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!
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.
More information about the R-help