similar to: rpart node size

I am running R on Mac OS X 10.2x. When I create postscript graphics of rpart tree objects, a tiny part of the tree gets trimmed off, even when it has only a few terminal nodes. This happens even without fancy but worse if fancy=T. (This doesn't happen with boxplot, scatter plots, etc.) How do I fix this? postscript("tree.eps") plot(davb.tree, u=T) text(davb.tree, use.n=T,

There have been various messages about packages tree and rpart whilst I have been travelling, and I have now prepared updates. tree ==== Tree is one of the oldest packages on CRAN (Feb 2000 apart from adding the maintainer field), and I had been hoping that it would fade away in favour of rpart. 1) sys.parent needed to be replaced by parent.frame in all but the most recent R (post 1.3.0).

Hi, all Our understanding of how classification trees in Rpart treat missing is that if the variable is ordinal(continous), Rpart, by default, imputes a value for missing. How do we do the classification tree and tell Rpart not to impute. That is, what command is used to turn off the imputation. Also, if we do get true missing, how does classification tree analysis in Rpart treat missing when

Greetings, I am using rpart for classification with "class" method. The test data is the Indian diabetes data from package mlbench. I fitted a classification tree firstly using the original data, and then exchanged the order of Body mass and Plasma glucose which are the strongest/important variables in the growing phase. The second tree is a little different from the first one. The

I am performing a binary classification using a classification tree. Ironically, the data themselves are 2483 tree (real biological ones) locations as described by a suite of environmental variables (slope, soil moisture, radiation load, etc). I want to separate them from an equal number of random points. Doing eda on the data shows that there is substantial difference between the tree and random

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

Hi there. I was wondering if somebody knows how to perform a bagging procedure on a classification tree without running the classifier with weights. Let me first explain why I need this and then give some details of what I have found out so far. I am thinking about implementing the bagging procedure in Matlab. Matlab has a simple classification tree function (in their Statistics toolbox) but

Dear All, I have a question regarding predict.rpart. I use rpart to build classification and regression trees and I deal with data with relatively large number of input variables (predictors). For example, I build an rpart model like this rpartModel <- rpart(Y ~ X, method="class", minsplit =1, minbucket=nMinBucket,cp=nCp); and get predictors used in building the model like

Is there an optimal / minimum sample size for attempting to construct a classification tree using /rpart/? I have 27 seagrass disturbance sites (boat groundings) that have been monitored for a number of years. The monitoring protocol for each site is identical. From the monitoring data, I am able to determine the level of recovery that each site has experienced. Recovery is our

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

You could break your 3 class problem into several (2 or 3) 2 class problems, and then use Andy's suggestion (see the CART book). There are several ways to break the problem into 2 class problems, and several ways to combine the resulting classifiers. Tom Dietterich, Jerry Friedman, Trevor Hastie and Rob Tibshirani, among others, have articles on the question, in places like Annals of

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

All the documentation that I have on survival splitting is found in the technical report you mention. However, there is both a short form and a long form of this on our web site, did you get the larger one (52 pages)? I admit it is not a lot. There are no other split algorithms implimented for survival data. It would be possible to add your own. Attached is a slightly updated version of the

Hi, there: I am working on a classification problem by using rpart. when my response variable y is binary, the trees grow very fast, but if I add one more case to y, that is making y has 3 cases, the tree growing cannot be finished. the command looks like: x<-rpart(r0$V142~.,data=r0[,1:141], parms=list(split='gini'), cp=0.01) changing cp or removing parms does not help.

Hi, I am using the rpart package to build a classification tree. I did manage to build a tree with data on a previous project. However, when attampting to build a tree on a project I am working on, I seem to be getting the error shown below: > nhg3.rp <- rpart(profitresp ~., nhg3, method="class") > plot(nhg3.rp, branch=0.4, uniform=T); text(nhg3.rp, digits=3) Error in

Hi Everyone, I'm working on building some classification trees, and up to this point I've been using rpart. However, I recently discovered the tree package, and found that it had some useful functions (in particular deviance(), which I would really like to use for my project). I can't seem to find an equivalent function for rpart. I've considered using tree() in place of

Dear helpers, I am a Dutch student from the Erasmus University. For my Bachelor thesis I have written a script in R using boosting by means of classification and regression trees. This script uses the function the predefined function rpart. My input file consists of about 4000 vectors each having 2210 dimensions. In the third iteration R complains of a lack of memory, although in each iteration

partition.tree plots in 2d the partition of a classification tree produced by the function tree (assuming the data frame from which it was computed has two continuous predictors). I get an error when I feed a tree produced by rpart to partition.tree (since trees produced by rpart are superclasses of those produced by tree). Is there an equivalent of partition.tree for objects of class rpart?

Hi, I think I'm missing something very obvious, but I am missing it, so I would be very grateful for help. I'm using rpart to analyse data on skull base morphology, essentially predicting sex from one or several skull base measurements. The sex of the people whose skulls are being studied is known, and lives as a factor (M,F) in the data. I want to get back predictions of gender, and

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