One way to do it (no p-values) is explained in the original CART book. You basically add up all the "improvement" (in fit$split[, "improve"]) due to each splitting variable.
Andy From: Tal Galili > > Simple example: > > # Classification Tree with rpart > > library(rpart) > > # grow tree > > fit <- rpart(Kyphosis ~ Age + Number + Start, > > method="class", data=kyphosis) > > Now I would like to know how can I measure the "importance" > of each of my > three explanatory variables (Age, Number, Start) in the model? > > If this was a regression model, I could have looked at p > values from the > "anova" F test (between lm models with and without the > variable). But what > is the equivalence of using "anova" on lm to an rpart object ? > > Any pointers, insights and references to this question will > be helpful. > > Thanks, > > Tal > > > > ----------------Contact > Details:------------------------------------------------------- > Contact me: tal.gal...@gmail.com | 972-52-7275845 > Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il > (Hebrew) | > www.r-statistics.com (English) > -------------------------------------------------------------- > -------------------------------- > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org 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. > Notice: This e-mail message, together with any attachme...{{dropped:10}} ______________________________________________ R-help@r-project.org 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.
