similar to: pruning trees using rpart

hi, I have a question regarding cp values in rpart(). When I use plotcp() I get a figure with cp values on the x-axsis, but then I use printcp() the cp values in that list are different from the values in the figure by plotcp(). Does someone know why? Silje [[alternative HTML version deleted]]

Full_Name: Gregory R. Warnes Version: R 1.3.1 OS: Solaris 2.8 Submission from: (NULL) (192.77.198.200) rpart library version 3.1-2 Error message: > plotcp(fit.thirds.1,ylim=c(0.7,1.5)); Error in plot.default(ns, xerror, axes = FALSE, xlab = "cp", ylab = "X-val Relative Error", : formal argument "ylim" matched by multiple actual arguments > This can be

Hi All, I have some questions on using library rpart. Given my data below, the plotcp gives me increasing 'xerrors' across different cp's with huge xstd (plot attached). What causes the problem or it's not a problem at all? I am thinking 'xerror's should be decreasing when 'cp' gets smaller. Also what the 'xstd' really tells us? If the error bars for

Dear r-help-list: If I use the rpart method like cfit<-rpart(y~.,data=data,...), what kind of tree is stored in cfit? Is it right that this tree is not pruned at all, that it is the full tree? If so, it's up to me to choose a subtree by using the printcp method. In the technical report from Atkinson and Therneau "An Introduction to recursive partitioning using the rpart

I ran this code (several times) from the Quick-R web page ( http://www.statmethods.net/advstats/cart.html) but my cross-validation errors increase instead of decrease (same thing happens with an unrelated data set). Why does this happen? Am I doing something wrong? # Classification Tree with rpart library(rpart) # grow tree fit <- rpart(Kyphosis ~ Age + Number + Start,

Hi R folks, I am using R version 2.2.1 for Unix. I am exploring the rpart function, in particular the rpart.control parameter. I have tried using different values for xval (0, 1, 10, 20) leaving other parameters constant but I receive the same tree after each run. Is the10 fold cross-validation default still running every time? I would expect the trees to change at least a little when I

I've run into another postscript/rpart problem unrelated to the issues I've mentioned in a previous query. I'm using 1.3.1 on a Win2K box. >plotcp(some.rpart.object) draws a very nice plot on the windows graphic device. If I save this as a postscript file, either by opening a postscript device before calling plotcp, or by saving the graphics window as a postscript file, the

Hello, I am working with rpart function but geting some error in prediction. the same code works fine with iris dataset. but applying other dataset it doesn't work. sample code is given for reference. > acc_model<-rpart(V1~V2+V3+V4+V5+V6+V7+V8, data=accEx.train) > plotcp(acc_model) >

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Dear community, I've an rpart object, and I know the CP I want. I'd like to know if it's possible also to fix the number of terminal nodes I want. Thanks in advance, user at host.com as user at host.com -- View this message in context: http://r.789695.n4.nabble.com/prune-in-rpart-choose-number-terminal-nodes-tp4643837.html Sent from the R help mailing list archive at

Hi all! The randomForest in R enables us to prune the trees using the nodesize feature where we can stop splitting a node if it contains less than the specified no.of of records/entities at that node. However is there a way to stop the tree growing after a specified number of levels. To be more clear on what I mean by a level. Level 0 is the parent node, Level 1 has 2 daughter nodes, Level 2 has

Hi dear R-help memebers, When building a CART model (specifically classification tree) using rpart, it is sometimes obvious that there are variables (X's) that are meaningful for predicting some of the outcome (y) variables - while other predictors are relevant for other outcome variables (y's only). *How can it be estimated, which explanatory variable is "used" for which of

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

After fitting and pruning an rpart model, it is often the case that one or more of the original predictors is not used by any of the splits of the final tree. It seems logical, therefore, that values for these "unused" predictors would not be needed for prediction. But when predict() is called on such models, all predictors seem to be required. Why is that, and can it be easily

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 list, I'm trying to generate classifiers for a certain task using several methods, one of them being decision trees. The doubts come when I want to estimate the cross-validation error of the generated tree: tree <- rpart(y~., data=data.frame(xsel, y), cp=0.00001) ptree <- prune(tree, cp=tree$cptable[which.min(tree$cptable[,"xerror"]),"CP"]) ptree$cptable

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

I'm stumped with this. When I execute the lines in the function singly, they run fine, but when I run the function, I get this error on the read.table() line: Error in count.fields(file, sep, quote, skip) : can't open file fspci1.dat Can anyone tell my why this should be so? Here is the program: library(rpart) wait <- function(str="Press a key when ready...")

In the help for rpart it says, "This differs from the tree function mainly in its handling of surrogate variables." And it says that an rpart object is a superset of a tree object. Both cite Brieman et al. 1984. Both call external code which looks like martian poetry to me. I've seen posts in the archives where BDR, and other knowledgeable folks, have said that rpart() is to be

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