similar to: rpart question on loss matrix

I have created plots of rpart objects with the fancy option for text.rpart ("fancy" creates ellipses and rectangles and labels branches with splitting criteria). The ellipses and rectangles are supposed to "interrupt" the tree lines (as seen in Therneau and Atkinson 1997, page 48, Fig. 18, http://www.mayo.edu/hsr/techrpt/61.pdf), but they do not, even when I use Therneau and

Dear r-help-list: If I use the rpart method like cfit<-rpart(y~.,data=data,...), what kind of tree is stored in cfit? Is it right that this tree is not pruned at all, that it is the full tree? If so, it's up to me to choose a subtree by using the printcp method. In the technical report from Atkinson and Therneau "An Introduction to recursive partitioning using the rpart

Hallo! I am a student of the Politecnico di Milano (Milan, italy) and I'm working on CARTs. I'm trying to use the R rpart function with a personalized splitfunction... but I'm not able to do it! More precisely, I would like to know what is the meaning of the function 'init', 'split' and 'eval' named in the help page.I can't find any answer in

Hello everyone, I'm a newbie to R and to CART so I hope my questions don't seem too stupid. 1.) My first question concerns the rpart() method. Which method does rpart use in order to get the best split - entropy impurity, Bayes error (min. error) or Gini index? Is there a way to make it use the entropy impurity? The second and third question concern the output of the printcp() function.

Hello again I have modeled a tree using rpart, with the DV being a log transformation of the variable I am really interested in (I transformed the DV due to extreme skewness). By default, text.rpart labels the nodes with the value of yval, which in this case is not what I want; I'd like the labels to be on the original metric, but label in text.rpart requires a "column name of

I was under the impression that the tree method (e.g. as implemented in rpart) was insensitive to monotonic transformations of the dependent variable. e.g. Breiman Olshen et al. Classification and Regression Trees state "In a standard data structure [a tree] is invariant under all monotone transformations of individual ordered varaibles" (p. 57) However, I get very different results

Dear all, I'm writing my own method to be used in Rpart by defining the list of functions named init, split and eval. I'm following the example given in the file 'tests/usersplits.R' in the sources. By now I'm able to define the split function (and it works correctly in the tree construction) while I have some problems with the init and the eval function. The task I'm

Hello dear R-help members, I would appreciate any help in understanding how the rpart function computes the "improve" (which is given in fit$split) when using the split='information' parameter. Thanks to Professor Atkinson help, I was able to find how this is done in the case that split='gini'. By following the explanation here:

Greetings. I'm trying to determine whether to use rpart or randomForest for a classification tree. Has anybody tested efficacy formally? I've run both and the confusion matrix for rf beats rpart. I've looking at the rf help page and am unable to figure out how to extract the tree. But more than that I'm looking for a more comprehensive user's guide for randomForest including

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)

Dear r-help, I am looking for additional documentation on the "adj" column in rpart's splits matrix. The help says: adj gives the adjusted concordance for surrogate splits I am looking info about "adjusted concordance". I cannot find this phrase in either Therneau & Atkinson original RPART documentation or the CART book. This question came up in the

--- 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,

Dear R community, as stated in Breiman et.al. (1984) and De'Ath & Fabricius (2000) using sums of absolute deviations about the median as an impurity measure gives robust trees. I would like to use this method in rpart. Has somebody already tried this method in rpart? Is there maybe already a script available somewhere? I am aware of the possibility to define usersplits myself with

I've been using the package rpart with R 1.3.0 for Windows to produce simple classification trees for some measurement data from paleontological specimens. Both the rpart documentation and the output confirm that the program produces splits on continuous data that leave "holes" in the data. It is probably of little practical importance, but is there a reason why the binary

Hello: I am using rpart and would like more background on how the splits are made and how to interpret results - also how to properly use text(.rpart). I have looked through Venables and Ripley and through the rpart help and still have some questions. If there is a source (say, Breiman et al) on decision trees that would clear this all up, please let me know. The questions below pertain to a

Hi, I just noticed that rpart behaves unexpectecly, when performing classification learning and specifying a loss matrix. if the response variable y is a factor and if not all levels of the factor occur in the observations, rpart exits with an error: > df=data.frame(attr=1:5,class=factor(c(2,3,1,5,3),levels=1:6)) > rpart(class~attr,df,parms=list(loss=matrix(0,6,6))) Error in

The package mvpart is now available. mvpart includes partitioning based on (1) multivariate numeric responses and (2) dissimilarity matrices. The package mvpart is a modification of rpart -- -- authors of original: Terry M Therneau and Beth Atkinson <atkinson at mayo.edu>, and R port of rpart Brian Ripley <ripley at stats.ox.ac.uk>. Includes some modified routines from vegan --

The package mvpart is now available. mvpart includes partitioning based on (1) multivariate numeric responses and (2) dissimilarity matrices. The package mvpart is a modification of rpart -- -- authors of original: Terry M Therneau and Beth Atkinson <atkinson at mayo.edu>, and R port of rpart Brian Ripley <ripley at stats.ox.ac.uk>. Includes some modified routines from vegan --

Hello I want to build a classification tree for a binary response variable while the condition for the final tree should be : The total misclassification for each group (zero or one) will be less then 10% . for example: if I have in the root 100 observations, 90 from group 0 and 10 from group 1, I want that in the final tree a maximum of 9 and 1 observations out of group 0 and 1, respectively,

I have a tree created with tr.hh.logcas <- rpart(log(YCASSX + 1)~AGE+DRUGUSEY+SEX+OBSXNUM +WINDLE, xval = 10) I would like to label the nodes with YCASSX rather than log(YCASSX + 1). But the help file for text in library rpart says that you can only use labels that are part of x$frame, which YCASSX is not. Is there a way to do what I want? Thanks in advance Peter Peter L. Flom, PhD