similar to: rpart: Writing values of the leaves to a dateset

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, I am building a regression tree (method=anova) by using rpart package and as a final result I get the final leaves characterized by different means and standard deviations for the dependent variable. However, differently from the classification tree for categorical variables I cannot find a way to assign each observation to a leaf, i.e. I can find no frame whcih contains the observation id

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 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

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, 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}

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)

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

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

Hi all I am hoping to use the SamplingStrata R package for a dataset describing a population of businesses wherein I have information on the type of business, as well as, for designated employment number bands, number of employees and business turnover information. So in this context the employment number bands can be described as micro, small, medium and large, i.e. size of business. Hence I

Hi Bert thank you for the reply. Not a coding query as such. Just wanted some pointers towards how to handle strata using the package in my situation, i.e. business type X business size with information for count and financial turnover. many thanks On 31 October 2017 at 14:37, Bert Gunter <bgunter.4567 at gmail.com> wrote: > 1. There is no question here. > > 2. In any case, this

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", >

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

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

1. There is no question here. 2. In any case, this is not a code writing service, so a question about how to code models without any offering of your own attempts might not be replied to anyway. 3. For what sorts of queries you might expect replies to, please read and follow the posting guide below. Also, if you do post, please post in plaint text, not html, as the latter (especially code) can

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

Dear all, I have a script about iteration classification, like this below data(iris) N <- 5 ir.tr.iter <- vector('list',N) ir.tr <- vector('list',N) for (j in 1:N) { ir.tr[[j]] <- rpart(Species ~., data=iris) ir.tr.iter[j] <- ir.tr[[j]]$frame result <- list(ir.tr=ir.tr, ir.tr.iter=ir.tr.iter) } as.data.frame(as.matrix(ir.tr.iter))

Not sure if anyone has posted on this problem ... I want to use rpart to build a binary tree on a relatively large dataset with ~1400 data points and 15 predictors. But I've noticed that rpart fails almost immediately in the call to C_s_to_rp, as that code returns nonsense. Looking at the code itself isn't terribly helpful, and there don't seem to be any hard limits coded anywhere.

Hello there. I have fitted a rpart model. > rpartModel <- rpart(y~., data=data.frame(y=y,x=x),method="class", ....) and can use rpart$where to find out the terminal nodes that each observations belongs. Now, I have a set of new data and used predict.rpart which seems to give only the predicted value with no information similar to rpart$where. May I know how