Hi Andy, Thank you for your response.
I've already came by this function but also noticed that the help file states that: "This method does *not* currently provide classspecific measures of importance when the *response is a factor*." Which is the case I need to deal with. Any suggestions as to how to adjust this function for the factor-response case? Best, 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) ---------------------------------------------------------------------------------------------- On Mon, Jan 24, 2011 at 5:21 PM, Liaw, Andy <andy_l...@merck.com> wrote: > Check out caret::varImp.rpart(). It's described in the original CART > book. > > Andy > > From: Tal Galili > > > > Hello all, > > > > When building a CART model (specifically classification tree) > > using rpart, > > it is sometimes interesting to know what is the importance of > > the various > > variables introduced to the model. > > > > Thus, my question is: *What common measures exists for > > ranking/measuring > > variable importance of participating variables in a CART > > model? And how can > > this be computed using R (for example, when using the rpart package)* > > > > For example, here is some dummy code, created so you might show your > > solutions on it. This example is structured so that it is clear that > > variable x1 and x2 are "important" while (in some sense) x1 is more > > important then x2 (since x1 should apply to more cases, thus make more > > influence on the structure of the data, then x2). > > > > set.seed(31431) > > > > n <- 400 > > > > x1 <- rnorm(n) > > > > x2 <- rnorm(n) > > > > x3 <- rnorm(n) > > > > x4 <- rnorm(n) > > > > x5 <- rnorm(n) > > > > X <- data.frame(x1,x2,x3,x4,x5) > > > > y <- sample(letters[1:4], n, T) > > > > y <- ifelse(X[,2] < -1 , "b", y) > > > > y <- ifelse(X[,1] < 0 , "a", y) > > > > require(rpart) > > > > fit <- rpart(y~., X) > > > > plot(fit); text(fit) > > > > info.gain.rpart(fit) # your function - telling us on each variable how > > important it is > > > > (references are always welcomed) > > > > > > 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 attach...{{dropped:16}}
______________________________________________ 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.
