Hello Xiaogang, and thank you for your answer. Your solution was indeed the way I had considered taking (after consulting with some friends in my lab), however, I had hoped to find out if this information (what variable effects which outcome variable) was somehow implicitly contained within the rpart object/information. Due to the current silence of the list, I understand that maybe taking the approach you proposed would be better.
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) ---------------------------------------------------------------------------------------------- On Wed, Dec 15, 2010 at 1:03 AM, Xiaogang Su <xiaogan...@gmail.com> wrote: > Hi, Tal, > > Here is a quick way of getting around. First create two responses via > dummy variables > > y1 <- ifelse(y=="a", 1, 0) > y2 <- ifelse(y=="b", 1, 0) > > and then built two separate tree models for y1 and y2 separately. > > Hope it helps. > Xiaogang > > > On Tue, Dec 14, 2010 at 8:33 AM, Tal Galili <tal.gal...@gmail.com> wrote: > > Hi dear R-help memebers, > > > > When building a CART model (specifically classification tree) using > rpart, > > it is sometimes obvious that there are variables (X's) that are > meaningful > > for predicting some of the outcome (y) variables - while other predictors > > are relevant for other outcome variables (y's only). > > > > *How can it be estimated, which explanatory variable is "used" for which > of > > the predicted value in the outcome variable?* > > > > Here is an example code in which x2 is the only important variable for > > predicting "b" (one of the y outcomes). There is no predicting variable > for > > "c", and x1 is a predictor for "a", assuming that x2 permits it. > > > > How can this situation be shown using the an rpart fitted model? > > > > N <- 200 > > set.seed(5123) > > > > x1 <- runif(N) > > > > x2 <- runif(N) > > > > x3 <- runif(N) > > > > y <- sample(letters[1:3], N, T) > > > > y[x1 <.5] <- "a" > > > > y[x2 <.1] <- "b" > > > > fit <- rpart(y ~ x1+x2) > > > > fit2 <- prune(fit, cp= 0.07) > > > > plot(fit2) > > > > text(fit2, use.n=TRUE) > > > > 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. > > > > > > -- > ============================== > Xiaogang Su, Ph.D. > Associate Professor, Statistician > School of Nursing, University of Alabama > Birmingham, AL 35294-1210 > (205) 934-2355 [Office] > x...@uab.edu > xiaogan...@gmail.com > http://homepage.uab.edu/xgsu/ > [[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.
