similar to: rpart and TREE, can be the same?

Hi, there, I am having trouble using 'lrm' function in package 'Design'. Basically, the ' . ' after ' ~ ' wouldn't work. Here are some sample codes: > temp <- data.frame(a=c(rep(0,3),rep(1,3)),b=rnorm(6),c=c('a','b','c','a','b','c')) > lrm(a~.,data=temp) Error in terms.formula(formula, specials =

Hi, List, I am using function 'cph' in package 'Design'. I have run into this error message but could not find documentation after looking for a long time. Could someone help me out? What kind of problem it is in my data set and how to fix it? Thanks a lot! Auston Error in fitter(X, Y, strata = Strata, offset = offset, weights = weights, :NA/NaN/Inf in foreign function

Hi, List, I used the following codes to generate ps plots but foo.ps contains nothing. Would someone please point out what is wrong with my codes? Thanks a million! postscript('foo.ps') par(mfrow=c(2,1)) par(mfg=c(1,1)) hist(rnorm(100),col='blue') par(mfrow=c(2,2)) par(mfg=c(2,1)) hist(rnorm(50),col='blue') par(mfg=c(2,2)) hist(rnorm(60),col='blue') dev.off()

Hi all, I apologies in advance if I am missing something very simple here, but since I failed at resolving this myself, I'm sending this question to the list. I would appreciate any help in understanding how the rpart function is (exactly) computing the "improve" (which is given in fit$split), and how it differs when using the split='information' vs split='gini'

Hi, I am trying to make a multi-class classification tree by using rpart. I used MASS package'd data: fgl to test and it works well. However, when I used my small-sampled data as below, the program seems to take forever. I am not sure if it is due to slowness or there is something wrong with my codes or data manipulation. Please be advised ! The data is described as the output from str()

Dear R-users, I'm working with the Rpart package and trying to understand how the procedure select the best split in the case the method "class" and the splitting index "Gini" are used. In particular I'd like to have look to the source code that works out the best split for un unordered predictor. Does anyone can suggest me which functions in the sources I should

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Hi! Could you please explain the difference between "prior" and "weight" in rpart? It seems to be the same. But in this case why including a weight option in the latest versions? For an unbalanced sampling what is the best to use : weight, prior or the both together? Thanks a lot. Aur?lie Davranche.

Dear all, I have a 100 iteration loop. Within each loop, there are some calls to rpart() like: ctl <- rpart.control(maxcompete=0, maxsurrogate=0, maxdepth=10) temp <- rpart(y~., x, w=wt, method="class", parms=list(split="gini"), control=ctl) res <- log(predict.rpart(temp, type="prob")) newres <- log(predict.rpart(temp, newdata=newx,

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",

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.

Hello everyone, I'm a newbie to R and to CART so I hope my questions don't seem too stupid. 1.) My first question concerns the rpart() method. Which method does rpart use in order to get the best split - entropy impurity, Bayes error (min. error) or Gini index? Is there a way to make it use the entropy impurity? The second and third question concern the output of the printcp() function.

Running on an Alpha machine which reports (uname -a) OSF1 bsdx01.bs.ehu.es V4.0 878 alpha and using the binary distribution put together by Albrecht Gebhardt (in http://cran.at.r-project.org/bin/osf/osf4.0/tar/alpha_ev5/) I obtain core dumps whenever I try to use package rpart. I have R REMOVE'd the rpart package, downloaded the source rpart_1.0-7.tar from CRAN and

Hi all, I am trying to play around with the randomForest function for classification. I know its performance is great. I am currently using the default options. It has many options. How do I further tweak the options so that I can make its performance even better? What are the options that are mostly used? Thanks a lot! M [[alternative HTML version deleted]]

Hi, list, suppose i have such a data frame: trash <- data.frame(cbind(seq(1:5),c('a','a','b','a','b'),c('b','a','b','b','a'))) names(trash) <- c('age','typeI','typeII') and I want to change all 'a's to be 0 and 'b's to be 1. temp <- as.matrix(trash)

Hi, I am currently studying Decision Trees by using rpart package in R. I artificially created a data set which includes the dependant variable (y) and a few independent variables (x1, x2...). The dependant variable y only comprises 0 and 1. 90% of y are 1 and 10% of y are 0. When I apply rpart to it, there is no splitting at all. I am wondering whether this is because of the

--- included message ---- Thus, my question is: *What common measures exists for ranking/measuring variable importance of participating variables in a CART model? And how can this be computed using R (for example, when using the rpart package)* ---end ---- Consider the following printout from rpart summary(rpart(time ~ age + ph.ecog + pat.karno, data=lung)) Node number 1: 228 observations,

Dear All, We came across a problem when using the "tree" package to analyze our data set. First, in the "tree" function, if we use the default value "mindev=0.01", the resulting regression tree has a single node. So, we set "mindev=0", and obtain a tree with 931 terminal nodes. However, when we further use the "cv.tree" function to run a 10-fold

Hi netters, I want to learn a decision tree from a series of instances (learning data). The packages tree or rpart can do this quite well, but the scoring functions (splitting criteria) are fixed in these packages, like gini or something. However, I'm going to use another scoring function. At first I wanna modify the R code of tree or rpart and put my own scoring function in. But it

I have a problem with running the tree package (dec.8, 2001) for R. The problem is, it will only give me 5/6 terminal node and then stop, while using Splus's tree on the same data with the same specification give me hundreds of nodes. Here's a little more background info: R-1.4.1 Solaris 5.7 rpart (most recent version) tree (..) Splus 6.0 Solaris 5.7 tree