similar to: masking

Dear r-help, I am trying to build a new function (to process rpart objects) that will output matrix that has a row for each node and a column for each feature. With each entry in the table is a numerical property at that node for that feature (e.g. surrogate split agreement, improvement). My current trouble is that the only clue to the identity of the feature is stored as the *name* of the

Dear r-devel, I am mutating rpart to do calculations on trees. I am trying to extract information from the tree. However, I got a seg. fault. This is the offending line in "rpmatrix.c": deltaI[0][0] = spl->improve; (Commenting it out cures the seg fault) I would like some advice on how to debug this. I have allocated memory with calloc and deltaI[0][0] should be

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

Dear R users, I want to do discriminant analysis on my data. I have successfully followed the discriminant analysis in V & R on the iris data: > ir <- rbind (iris3[,,1],iris3[,,2],iris3[,,3]) > ir.species <- c(rep("s",50),rep("c",50),rep("v",50)) > a <- lda(log(ir),ir.species) > a$svd^2/sum(a$svd^2) [1] 0.996498601 0.003501399 > a.x <-

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

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

Hi, I would like to step-through a non-visible function. but apparently I don't know enough about namespaces to get that to work: > methods(predict) ... deleted lines ... [27] predict.rpart* predict.smooth.spline* [31] predict.survreg.penal* Non-visible functions are asterisked > debug(predict.rpart) Error: Object "predict.rpart" not found >

Hello, I just upgraded my Ubuntu Xenial system to R 3.5.1 (from 3.4.?) by changing the sources.list entry and doing an "apt-get dist-upgrade". Everything works except loading the rpart package in R: > library(rpart) Error: package or namespace load failed for ?rpart?: package ?rpart? was installed by an R version with different internals; it needs to be reinstalled for use with

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

I just updated rpart to the latest version (3.1.0). There are a number of changes between this and previous versions, and some of the code I've been using with earlier versions (e.g. 3.0.2) no longer work. Here is a simple illustration of a problem I'm having with xpred.rpart. iris.test.rpart<-rpart(iris$Species~., data=iris[,1:4], parms=list(prior=c(0.5,0.25, 0.25))) + ) >

Dear list For my work, it would be helpful if rpart worked seamlessly with an empty model: library(rpart); rpart(formula=y~0, data=data.frame(y=factor(1:10))) Currently, an unrelated error (originating from na.rpart) is thrown. At some point in the near future, I'd like to release a package to CRAN which uses rpart and relies on that functionality. I have prepared a patch (minor

? data=read.table("http://statcourse.com/research/boston.csv", , sep=",", header = TRUE) ? library(rpart) ? fit=rpart (MV~ CRIM+ZN+INDUS+CHAS+NOX+RM+AGE+DIS+RAD+TAX+ PT+B+LSTAT) predict(fit,data[4,]) plot only reveals part of the tree in contrast to the results on obtains with CART or C5 -------- Original Message -------- Subject: Re: [R] help with rpart From: Sarah

I?m not using the PPA, but the CRAN mirror from https://cran.r-project.org/bin/linux/ubuntu/#installation: deb https://cloud.r-project.org/bin/linux/ubuntu xenial-cran35/ On 14 Aug 2018, at 12:25, George N. White III <gnwiii at gmail.com<mailto:gnwiii at gmail.com>> wrote: qOn Tue, 14 Aug 2018 at 05:04, Ulrich KELLER <ulrich.keller at uni.lu<mailto:ulrich.keller at

Dear R-devel, I am trying to alter rpart so that it makes additional calculations when growing the tree. In the "rpart.s" there is a call to the C routine: rp <- .C("s_to_rp2", as.integer(nobs), as.integer(nsplit), as.integer(nodes), as.integer(ncat),

I've written a small adaptation of the text.rpart function of the rpart package to better suite my tree presentation needs (I basically left the code untouched, only changing some numbers relating to label positioning). When I changed to version 1.7.0 this function stopped working printing the error : couldn't find function "rpartco" As far as I understand this has to do

Dear all For my research, I am learning classification now. I was trying some example about classification tree pakages, such as tree and rpart, for instance, in Pima.te dataset have 8 variables (include class=type): library(rpart) library(datasets) pima.rpart <- rpart(type ~ npreg+glu+bp+skin+bmi+ped+age,data=Pima.te, method='class') plot(pima.rpart, uniform=TRUE) text(pima.rpart)

Dear R, Short verse of the question: Is there R code which will calculate surrogate splits and/or delta impurity for decision trees at each node? Long Version: I have local, legacy code which I use to calculate my decision trees. I would like to switch to R, but as I understand it surrogate splits are not implemented. Surrogate splits and feature ranking are described in Breiman et al

Hello useR, Could you please tell me how to draw the decision boundaries in a scatterplot of the original data for a LDA or Rpart object. For example: > library(rpart) >fit.rpart <- rpart(as.factor(group.id)~., data=data.frame(Data) ) How can I draw the cutting lines on the orignial Data? Or is there any built in functions that can read the rpart object 'fit.rpart' to do

Dear all, I am having some trouble with getting the rpart function to work as expected. I am trying to use rpart to combine levels of a factor to reduce the number of levels of that factor. In exploring the code I have noticed that it is possible for chisq.test to return a statistically significant result whilst the rpart method returns only the root node (i.e. no split is made). The following

After entering ?library(rpart)?, I tried to plot an existing rpart tree, and got this error message: Error: couldn't find function "plot.rpart". However, ??plot.rpart? does bring up the help for the function. The same things occur for text.rpart, although print(my.tree) does work. So, I tried to re-install rpart using Packages | Install from CRAN, but then I get this