similar to: model R^2 and partial R^2 values

Hi All, I have to say upfront that I am a complete neophyte when it comes to programming. Nevertheless I enjoy the challenge of using R because of its incredible statistical resources. My problem is this .........I am running a regression tree analysis using "rpart" and I need to run the calculation repeatedly (say n=50 times) to obtain a distribution of results from which I will pick

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Hello, I'm using mvpart option xv="1se" to compute a regression tree of good size with the 1-SE rule. To better understand 1-SE rule, I took a look on its coding in mvpart, which is : Let z be a rpart object , xerror <- z$cptable[, 4] xstd <- z$cptable[, 5] splt <- min(seq(along = xerror)[xerror <= min(xerror) + xvse * xstd]) I interprete this as following: the

I am running some models (for the first time) using rpart and am getting results I don't know how to interpret. I'm using cross-validation to prune the tree and the results look like: Root node error: 172.71/292 = 0.59148 n= 292 CP nsplit rel error xerror xstd 1 0.124662 0 1.00000 1.00731 0.093701 2 0.064634 1 0.87534 1.08076 0.092337 3 0.057300 2

Hi, I've searched online, in a few books, and in the archives, but haven't seen this. I believe that xerror is scaled to rel error on the first split. After fitting an rpart object, is it possible with a little math to determine the percentage of true classifications represented by a xerror value? -seth -- View this message in context:

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, I'm just wondering if it is planned to include the Lapack routine DGESDD (and friends) in R-1.4.0? This is faster (supposedly by a factor of ~6 for large matrices) than DGESVD which is currently (R-1.3.1) called by La.svd. And if it is not in the plans yet, is there a chance it could be? I've added it to my local version of R-1.3.1 and so far see a factor of 4 improvement over

Hi Listers I need to calculate and then plot a frequency histogram of the best tree calculated using the 1-se rule. I have included some code that has worked well for me in the past but it was only for selecting the minimum cross-validation error. I include the code for my model, some relevant output and the code for selecting and plotting the frequency histogram of minimum xerror. Here is the

Hi, If I used drop1 in a weighted lm fit, it seems to ignore the weights in the AIC calculation of the dropped terms, see the example below. Can this be right? Yan -------------------- library(car) > unweighted.model <- lm(trSex ~ (river+length +depth)^2- length:depth, dno2) > Anova(unweighted.model) Anova Table (Type II tests) Response: trSex Sum Sq Df F value

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

Does anybody know of any commercial providers of IAX termination with DIDs in the Seattle, WA area? I believe the area codes are: 425, 206, 253 Failing any commercial providers, is there anybody in the seattle area running Asterisk with a PRI coming in who might be willing to sell me an IAX trunk with a DID in Seattle? -- ____________________________________________________________ Muiz

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'm using rpart function for creating regression trees. > now how to measure the fitness of regression tree??? > > thanks n Regards, > Vibha I read R-help as a digest so often come late to a discussion. Let me start by being the first to directly answer the question: > fit <- rpart(time ~ age +ph.ecog,lung) > summary(fit) Call: rpart(formula = time ~ age +

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

On kde, gtk-window-decorator segfault everytime a minimised "Konqueror download window" close it self... Running with --sync fix the problem so no way to have a backtrace :( Cedric

Thanks in advance. I have a vector of numbers which contain sections that are sequences which increase by a value of 1 followed by a gap in the data and then another sequence occurs, etc: x<-c(1:3, 6: 7, 10:13) From the vector I need to extract 2 items of information A) the first number in the sequence (e.g., 1, 6, 10) and B) how many observations were in each sequence section (e.g., 3,

Hello: I am using rpart and would like more background on how the splits are made and how to interpret results - also how to properly use text(.rpart). I have looked through Venables and Ripley and through the rpart help and still have some questions. If there is a source (say, Breiman et al) on decision trees that would clear this all up, please let me know. The questions below pertain to a

Dear R users, I know cross-validation does not work in rpart with user defined split functions. As Terry Therneau suggested, one can use the xpred.rpart function and then summarize the matrix of the predicted values into a single "goodness" value. I need only a confirmation: set for example xval=10, if I correctly understood a single column of the matrix obatined by xpred.rpart gives

Dear all, I'm getting an error in one of the stock examples in the 'mvpart' package. I tried: require(mvpart) data(spider) fit3 <- rpart(gdist(spider[,1:12],meth="bray",full=TRUE,sq=TRUE)~water+twigs+reft+herbs+moss+sand,spider,method="dist") #directly from ?rpart summary(fit3) ...which returned the following: Error in apply(formatg(yval, digits - 3), 1,

Hi, I am having a hard time getting what the summary.lm-output for an ANOVA / ANCOVA means. Examples I find always seem to deal with simpler cases than what I meet in my data. My main problem is understanding the output when getting significant INTERACTION TERMS (what never occurs in examples :(). The following is the output after summary.lm(ancova) where "week" is continuous,