similar to: CART with rpart

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Hi, I have a technical question about rpart: according to Breiman et al. 1984, different costs for misclassification in CART can be modelled either by means of modifying the loss matrix or by means of using different prior probabilities for the classes, which again should have the same effect as using different weights for the response classes. What I tried was this: library(rpart)

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Hello, I have a question regarding competing risk regression using cmprsk package (function crr()). I am using R2.9.1. How can I do to assess the effect of qualitative predictor (gg) with more than two categories (a,b,c) categorie c is the reference category. See above results, gg is considered like a ordered predictor ! Thank you for your help Jan > # simulated data to test > set.seed(10)

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

Dear useRs: I try to plot an rpart object but cannot get a nice tree structure plot. I am using plot.rpart and text.rpart (please see below) but the branches that connect the nodes overlap the text in the ellipses and rectangles. Is there a way to get a clean nice tree plot (as in the Rpart Mayo report)? I work under Windows and use R2.11.1 with rpart version 3.1-46. Thank you. Tudor ...

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--- included message ---- 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)* ---end ---- Consider the following printout from rpart summary(rpart(time ~ age + ph.ecog + pat.karno, data=lung)) Node number 1: 228 observations,

Can I say that RPART is a modified algo of CART and PARTY a modified of CHAID? Thanks. ---- Chua Siang Li Consultant - Operations Research Acceval Pte Ltd Tel: 6297 8740 Email: siang.li.chua at acceval-intl.com Website: www.acceval-intl.com This message and any attachments (the "message"...{{dropped:12}}

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

Dear R friends- I'm attempting to generate a regression tree with one gradient predictor and multiple responses, trying to test if change in size (turtle.data$Clength) acts as a single predictor of ten multiple diet taxa abundances (prey.data) Neither rpart or mvpart seem to allow me to do multiple responses. (Or if they can, I'm not using the functions properly.) > library(rpart)

Hi, Is here anyone knows the difference between rapart and bagging when grow a CART tree? Thanks Qin