similar to: problem in forcing variables in CART

Hi All, I'm a newcomer to the R community, just started learning R. I need to use partykit for developing CART. But I'm not getting any document/help for using partykit/rpart. http://www.agrocampus-ouest.fr/math/useR-2009/slides/Hothorn+Zeileis.pdfgives some example, but it doesn't tell how my own data should be formatted so that it can be accepted by partykit. Example,

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)

New package `partykit': A Toolkit for Recursive Partytioning The purpose of the package is to provide a toolkit with infrastructure for representing, summarizing, and visualizing tree-structured regression and classification models. Thus, the focus is not on _inferring_ such a tree structure from data but to _represent_ a given tree so that printing/plotting and computing predictions can be

New package `partykit': A Toolkit for Recursive Partytioning The purpose of the package is to provide a toolkit with infrastructure for representing, summarizing, and visualizing tree-structured regression and classification models. Thus, the focus is not on _inferring_ such a tree structure from data but to _represent_ a given tree so that printing/plotting and computing predictions can be

Hi, I am trying to use CART to find an ideal cut-off value for a simple diagnostic test (ie when the test score is above x, diagnose the condition). When I put in the model fit=rpart(outcome ~ predictor1(TB144), method="class", data=data8) sometimes it gives me a tree with multiple nodes for the same predictor (see below for example of tree with 1 or multiple nodes). Is there a way

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 ...

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}}

I am receiving the following error while executing KLdiv method: kl<-KLdiv(y) Error in function (classes, fdef, mtable) : unable to find an inherited method for function "KLdiv", for signature "data.frame" the code is as follows: require(flexmix) KLdiv(y) the object y (snippet, actual object contains 188 entries) is as follows: s1p5 s2p5 1 0.6 0.8 2 0.8

Hi all, I am a beginar user of R and try to do some CART analysis with it. With "rpart" we can get several terminals and draw it in the TREE plot. Now I am trying to draw a plot like this: x-axis is each terminal's value, and y-axis is those observe values. Does anyone has idea what gramma should I use? Thanks in advance. Sunny [[alternative HTML version deleted]]

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)

I run the following code: library(rpart) data(kyphosis) fit <- rpart(Kyphosis ~ ., data=kyphosis) plot(fit) text(fit, use.n=TRUE) The text labels represent the count of each class at the leaf node. Unfortunately, the numbers are rounded and in scientific notation rather than the exact number of examples sorted by that node in each class. The plot is supposed to look like

Hi Manuel, find our SoC proposal below. Best wishes, Torsten & Achim _______________________________________________________________________ Party On! New Recursive Partytioning Tools. Mentor: Torsten Hothorn & Achim Zeileis Short Description: The aim of the project is the implementation of recursive partitioning methods ("trees") which aren't available in R at the

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

Hi, Being a novice this is my first usage of R. I am trying to use rpart for building a decision tree in R. And I have the following dataframe Outlook Temp Humidity Windy Class Sunny 75 70 Yes Play Sunny 80 90 Yes Don't Play Sunny 85 85 No Don't Play Sunny 72 95 No Don't Play Sunny 69 70 No Play Overcast 72 90 Yes Play Overcast 83 78 No Play Overcast 64 65 Yes Play Overcast 81 75

According to Dr. Breiman, the RF should be more accurate method than a single tree. However, the performance of each method seems to depend on the proprotion of outcome variable in my case. My data set is a typical classification problem (predict bad guys). When I ran both of them with different proportion of outcome variables(there's a criterion to measure the degree of bad behavior), I

Dear R people, I was recently reading the book `Classification and Regression Trees' by Breiman. This book talks about the CART program. Both Splus and R have implementations of this. However, the book talks about the possibility of extending the existing `standard' set of questions (for continuous variables, these are of the form X < c where X is the variable, c some const) to

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

I would like to know if is it possible implemet a partitioning tree using a function like rpart, or mvpart, and with formula a glm-object (as a logistic models) or a robust linear regression (as least sum of absolute errors). In this case, the appropriate "method" to use is "mrt"? Or another one? Thanks, Anna Maria Paganoni

Hello all, Could anyone advise me how can I implement classification and regression tree analyses for a circular response (angles)? Is there available R code or I would have to adapt code from "rpart" or "tree" libraries, which I am not sure would be able to do it? I would really appreciate any suggestion. Duarte

Greetings, I checked the Indian diabetes data again and get one tree for the data with reordered columns and another tree for the original data. I compared these two trees, the split points for these two trees are exactly the same but the fitted classes are not the same for some cases. And the misclassification errors are different too. I know how CART deal with ties --- even we are using the