similar to: rpart: how to assign observations to nodes in regression trees

Dear useRs: Is there a way I could predict the terminal node associated with a new data entry in an rpart environment? In the example below, if I had a new data entry with an AM of 5, I would like to link it to the terminal node 2. My searches led to http://tolstoy.newcastle.edu.au/R/e4/help/08/07/17702.html but I do not seem to be able to operationalize Professor Ripley's suggestions. Many

Brian The following (which is new to rw1030) occurs with both Windows 98 & Windows ME. I have not tested behaviour under Unix or Linux, but I expect it is no different. text.rpart() prints unwanted NAs (presumably in the splitting criterion position) on terminal nodes. Criterion <- factor(paste("Leaf", 1:5)) Node <- factor(1:5)

I am trying to create a classification tree using either tree or rpart but when it comes to plotting the results the formatting I get is different than what I see in all the tutorials. What I would like to see is the XX/XX format but all I get is a weird decimal value. I was also wondering how you know which is yes and which is no in each leaf of the tree? Is yes always on the left?

Hi, I am using rpart decision trees to analyze customer churn. I am finding that the decision trees created are not effective because they are not able to recognize factors that influence churn. I have created an example situation below. What do I need to do to for rpart to build a tree with the variable experience? My guess is that this would happen if rpart used the loss matrix while creating

Hi - I'm trying to append simple density histograms of a continuous variable to the leaves of an rpart tree. The splits in the tree are all levels of a factor and I'm hoping to make the histograms out of the subsets of the dataframe corresponding to the splits and for them to be attached to the appropriate leaf of the final tree. Any help would be much appreciated, thanks, Jon Zelner

Hi Everyone, Is there a way to get predict.rpart() to return the nodes reached by the new examples in addition to the predicted probabilities it already returns? In other words, I would like to know the leaf node in the tree object that each new example data drops down to. Thanks in advance for your help. Osei

Dear mailinglist members, I have the following problem: I run a decision tree using the rpart function and, afterwords, I try to find to which leaf node a new register (not used to build the decision tree) belongs to. I will try to explain better: rpart.tree <- rpart(target.value ~., data) leaf.node <- new.function(rpart.tree, new.register) The new register has all the explanatory values

I'm fitting a regression tree with rpart and I want to write the values for every leaf in a dataset. As an example take the variable turnover. Let's suppose my tree for turnover has 30 leaves and I want to have 30 datasets with dataset 1 containing the turnover values of the units in leaf 1, dataset 2 containing turnover values for the observations in leaf 2 and so on. How can I do

I am trying to create a classification tree using either tree or rpart functions but when it comes to plotting the results the formatting I get is different than what I see in all the tutorials (like http://www.youtube.com/watch?v=9XNhqO1bu0A or http://www.youtube.com/watch?v=m3mLNpeke0I&feature=related or http://www.statmethods.net/advstats/cart.html "tree for kyphosis"). I am

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

This question concerns rpart's facility for user-defined functions that accomplish splitting. I was interested in modifying the code so that in each terminal node, a linear regression is fit to the data. It seems that from the allowable inputs in the user-defined functions, that this may not be possible, since they have the form: function(y, wt, parms) (in the case of the

Hi all, I used rpart to fit a model, where the covariates in the model are categorical variables. Then I plotted the tree (mytree) and used the command "text" to add labels to the tree. In the nodes of the tree, the values of the covariates are represented with a, b or c. Is there a way to show the real value(s) of the variable in the nodes instead of a, b or c ? I found that the

Hello, I use rpart to predict survival time and have a problem in interpreting the output of ?estimated rate?. Here is an example of what I do: > stagec <- > read.table("http://www.stanford.edu/class/stats202/DATA/stagec.data", > col.names=c("pgtime", "pgstat", "age","eet", "g2", "grade", "gleason", >

Hi, have anybody positive experience how it is possible get the post output from a rpart-object in a large sweave file or is only the less nicely plot function instead of post possible??? \begin{figure}[htbp] \begin{center} <<fig=true,eps=T>>= post(dtreeEB, title="Tree",digits=getOption("digits") - 0,use.n=TRUE) @ \caption{Tree1} \end{center}

I have a class with 732 members, so using rpart.plot is giving me a tiny plot in the middle of the window. Is there a good way to modify the plot, or replace the long list with something like "group1"? -- View this message in context: http://r.789695.n4.nabble.com/Plotting-rpart-trees-with-long-list-of-class-members-tp4635671.html Sent from the R help mailing list archive at

Hi, the predict.rpart() function from the rpart library allows for calculating the class probabilities for a given test case instead of a discrete class label. How are these class probabilities derived? Is it simply the proportion of the majority class to all cases in a leaf node? Thanks in advance, Chris

Hello all R-help list subscribers, I'd like to create a regression tree of a data set with binary response variable. Only 5% of observations are a success, so the regression tree will not find really any variable value combinations that will yield more than 50% of probability of success. I am however interested in areas where the probability of success is noticeably higher than 5%, for

Jonathan, Try making a list instead of an array. See ?list. Also, did you look into random forests? I'm not sure what you want to do, but there might be methods there to do some of the work for you. Sean On 3/19/04 1:12 PM, "Jonathan Williams" <jonathan.williams at pharmacology.oxford.ac.uk> wrote: > I would like to collect the trees grown by rpart fits in an array,

Hi, I wonder if anyone knows a study dealing with the minimum valid number of observations when using CART?. On top of that, when using RandomForest, is it possible to obtained a interpretable tree model as the graphical output of the analysis, just like in "rpart"? Thanks a lot in advance Javier Lozano Universidad de Le?n Spain

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