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/ ______________________________________________ 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.
