similar to: How to draw the decision boundaries for LDA and Rpart object

Hello, I would like to plot the results of a LDA analysis plotting the discriminant scores with the decission boundaries on it with rggobi. I have GGobi already installed on my computer. I have three classes, so the plot would be LD1xLD2 plus the decission boundaries. Here there is the code I use make the plot: library(MASS) data <- zgcppr273K.pca$x[,1:7] Tumor <-

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 ...

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 Experts, I am new to R, using decision tree model for getting segmentation rules. A) Using behavioural data (attributes defining customer behaviour, ( example balances, number of accounts etc.) 1. Clustering: Cluster behavioural data to suitable number of clusters 2. Decision Tree: Using rpart classification tree for generating rules for segmentation using cluster number(cluster id) as target

I've been groping my way through a classification/discrimination problem, from a consulting client. There are 26 observations, with 4 possible categories and 24 (!!!) potential predictor variables. I tried using lda() on the first 7 predictor variables and got 24 of the 26 observations correctly classified. (Training and testing both on the complete data set --- just to get started.) I

Hello, I am trying to explore a classification with GGobi. I am trying to generate additional data according to the model so I can draw the decision boundaries on the GGobi plot. The problem is that I always get the same error: Error in predict.lda(model,data): wrong number of variables, even if I know that I used the same number of variables for the model generation (6) and for the additional

Hello, I am using the RPART library to find patterns in HIV mutations regarding drug-resistancy. My data consists of aminoacid at certain locations and two classes resistant and susceptible. The classification and pruning work fine with Rpart. however there is a problem with displaying the data as a tree in the display window. in the display window the data contain only levels at the splits

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

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

Dear R users, I'm working with the rpart package and want to evaluate the performance of user defined split functions. I have some problems in understanding the meaning of the xval argument in the two functions rpart.control and xpred.rpart. In the former it is defined as the number of cross-validations while in the latter it is defined as the number of cross-validation groups. If I am

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

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))) + ) >

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

I tried using rpart, as below, and got this error message "rpart Error in yval[, 1] : incorrect number of dimensions". Thinking it might somehow be related to the large number of missing values, I tried using complete data, but with the same result. Does anyone know what may be going on, and how to fix it? I have traced two similar error messages in the Archive, but following the

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

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

Hi R-helpers, I am using rpart package for decision tree using R.We are invoking R environment through JRI from our java application.Hence, the result of R command is returned in REXP and we use geterrMessage() to retrieve the error. When we execute the following command, cnr_model<-rpart(as.factor(Species)~Sepal Length+Sepal Width+Petal Length, method="class",

I have just upgraded from R 1.7.3 to R 1.9.0 and have found that the predict function no longer works for rpart: > predict(hmmm,sim3[1:10,]) Error in predict.rpart(hmmm, sim3[1:10, ]) : couldn't find function "pred.rpart" I have re-installed the rpart package to no avail. Any ideas? Giles Hooker

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

? 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