search for: plotcp

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 corrected by adding ylim to the list of formal argument and w...

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 erro...

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 resulting file is not properly interpreted by Adobe Illustrator...

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...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, method="class", data=kyphosis) printcp(fit) # display the results plotcp(fit) # visualize cross-validation results Thank you, Claudia [[alternative HTML version deleted]]

Hi, I am using the packages tree and rpart to build a classification tree to predict a 0/1 outcome. The package rpart has the advantage that the function plotcp gives a visual representation of the cross-validation results with a horizontal line indicating the 1 standard error rule, i.e. the recommendation to select the most parsimonious model (the smallest tree) whose error is not more than one standard error above the error of the best model. However, i...

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

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

HI, Dear R-community, I AM using the following codes to grow tree and plot tree: # Classification Tree with rpart library(rpart) pdf(file="/home/cdu/changbin/dimer_tree.pdf") # grow tree fit.dimer <- rpart(outcome ~ ., method="class", data=p.dimer[,2:402]) plotcp(fit.dimer) # visualize cross-validation results # plot tree plot(fit.dimer, uniform=TRUE, main="Classification Tree for AA.dimer") text(fit.dimer, use.n=TRUE, all=TRUE, cex=.8) dev.off() But when I open in the pdf file, I found the right side of tree is not shown up, also part of 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) > acc_find_cp<-(acc_model$cptable)[,"CP"][which.min(acc_model$cptable[,"xerror"])] > acc_prune_model<-prune(acc_model,acc_find_cp) > acc_predict_tree<-predict(acc_prune_model,newdata=accEx.test, type="class") Error in predict.rpart(acc_prun...

...- read.table(file=dsn, header=T) rownames(ds) <- ds$unit nmavgres <- ds$mavgres * 1000 nravgres <- ds$ravgres * 1000 ds.mrpt <- rpart(formula=fm1, data=ds, control=rpart.control(minbucket=20)) plot(prune(ds.mrpt, cp=0.018)) text(prune(ds.mrpt, cp=0.018), digits=2) wait() plotcp(ds.mrpt) wait() post.rpart(prune(ds.mrpt, cp=0.018), title=c(tit, paste("SES Quartile", sesnum, sep=" ", collapse=NULL)), pretty=0, filename=psn, horizontal=F, height=9, digits=3, pointsize=10) } doit("fspci", 1, "Parent Involvement...

...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) > > > acc_find_cp<-(acc_model$cptable)[,"CP"][which.min(acc_model$cptable[,"xerror"])] > > acc_prune_model<-prune(acc_model,acc_find_cp) > > > acc_predict_tree<-predict(acc_prune_model,newdata=accEx.test, > type="class") &...