similar to: how to use the randomForest and rpart function?

Dear mailinglist members, I was wondering if there was a way to re-evaluate the instances of a tree (in the forest) again after I have manually changed a splitpoint (or split variable) of a decision node. Here's an illustration: library("randomForest") forest.rf <- randomForest(formula = Species ~ ., data = iris, do.trace = TRUE, ntree = 3, mtry = 2, norm.votes = FALSE) # I am

Hi all, I am using the "svm" command in the e1071 package. Does it have an automatic way of setting the "cost" parameter? I changed a few values for the "cost" parameter but I hope there is a systematic way of obtaining the best "cost" value. I noticed that there is a "cross" (Cross validation) parameter in the "svm" function. But I

Greetings. I'm trying to determine whether to use rpart or randomForest for a classification tree. Has anybody tested efficacy formally? I've run both and the confusion matrix for rf beats rpart. I've looking at the rf help page and am unable to figure out how to extract the tree. But more than that I'm looking for a more comprehensive user's guide for randomForest including

(Sorry for the cross-post--- I wasn't sure which list is more appropriate...) Hi everyone, I've run into segfaults when using my randomForest package on large dataset (e.g., 100 x 15200) and large number of trees (e.g., ntree=7000 and mtry=3000). I'm wondering if anyone can give me some hints on where to look for the problem. The randomForest package mainly consists of two things:

(Sorry for the cross-post--- I wasn't sure which list is more appropriate...) Hi everyone, I've run into segfaults when using my randomForest package on large dataset (e.g., 100 x 15200) and large number of trees (e.g., ntree=7000 and mtry=3000). I'm wondering if anyone can give me some hints on where to look for the problem. The randomForest package mainly consists of two things:

Dear R-help, I was bitten by the behavior of all() when given logical(0): It is TRUE! (And any(logical(0)) is FALSE.) Wouldn't it be better to return logical(0) in both cases? The problem surfaced because some un-named individual called randomForest(x, y, xtest, ytest,...), and gave y as a two-level factor, but ytest as just numeric vector. I thought I check for that in my code by testing

Hi, I am trying to run randomForests on a datasets of size 500000X650 and R pops up memory allocation error. Are there any better ways to deal with large datasets in R, for example, Splus had something like bigData library. Thank you, Nagu

When you have fairly large data, _do not use the formula interface_, as a couple of copies of the data would be made. Try simply: Myforest.rf <- randomForest(Mydata[, -46], Mydata[,46], ntrees=100, mtry=7) [Note that you don't need to set proximity (not proximities) or importance to FALSE, as that's the default already.] You might also want to use

Hello, I am a phd student in Bioinformatics and I am using the Random Forest package in order to classify my data, but I have some questions. Is there a function in order to visualize the trees, so as to get the rules? Also, could you please provide me with the code of "randomForest" function, as I would like to see how it works. I was wondering if I can get the classification having

Hello, I'm trying to find out the optimal number of splits (mtry parameter) for a randomForest classification. The classification is binary and there are 32 explanatory variables (mostly factors with each up to 4 levels but also some numeric variables) and 575 cases. I've seen that although there are only 32 explanatory variables the best classification performance is reached when

> From: Weiwei Shi > > it works. > thanks, > > but: (just curious) > why i tried previously and i got > > > is.vector(sample.size) > [1] TRUE Because a list is also a vector: > a <- c(list(1), list(2)) > a [[1]] [1] 1 [[2]] [1] 2 > is.vector(a) [1] TRUE > is.numeric(a) [1] FALSE Actually, the way I initialize a list of known length is by

Hi I listened to all your advise and ran my data on a computer with a 64 bits procesor but i still get the same error saying "it cannot allocate a vector of that size 1240 kb" . I don't want to cut my data in smaller pieces because we are looking at interaction. So are there any other options for me to try out or should i wait for the development of more advanced computers!

I try to use ?randomForest to find variables that are the most important to divide my dataset (continuous, categorical variables) in two given groups. But when I plot the outliers: plot(outlier(FemMalSex_NAavoid88.rf33, cls=FemMalSex_NAavoid88$Sex), type="h",col=c("red","green")[as.numeric(FemMalSex_NAavoid88$Sex)]) it seems to me that all my values appear as

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()

Hi, all, I am wondering if it is possible to set parameters of 'rpart' and 'tree' such that they will produce the exact same tree? Thanks. Auston Wei Statistical Analyst Department of Biostatistics and Applied Mathematics The University of Texas MD Anderson Cancer Center Tel: 713-563-4281 Email: wwei@mdanderson.org [[alternative HTML version deleted]]

Hi all, There is now a package available on CRAN that provides an R interface to Leo Breiman's random forest classifier. Basically, random forest does the following: 1. Select ntree, the number of trees to grow, and mtry, a number no larger than number of variables. 2. For i = 1 to ntree: 3. Draw a bootstrap sample from the data. Call those not in the bootstrap sample the

Hi all, There is now a package available on CRAN that provides an R interface to Leo Breiman's random forest classifier. Basically, random forest does the following: 1. Select ntree, the number of trees to grow, and mtry, a number no larger than number of variables. 2. For i = 1 to ntree: 3. Draw a bootstrap sample from the data. Call those not in the bootstrap sample the

Hello I would like to see the algorithm that R uses to generate density functions for several distributions (i.e. Normal,Weibull, etc). I tried: >dnorm function (x, mean = 0, sd = 1, log = FALSE) .Internal(dnorm(x, mean, sd, log)) <environment: namespace:stats> How can I see the code used for densities? Thanks!

Hi, i try several possibilities adn looking in the archive, but didn't getting success to install j.lindsey's usefuel "library repeated" on my linux (suse9.0 with kernel 2.6.7,R.1.9.1) P.S. Windows, works fine Many thanks for help Christian chris at linux:/space/downs> R CMD INSTALL - l /usr/lib/R/library repeated WARNING: invalid package '-' WARNING:

Dear R-devel, Can anyone give me some hints on how to go about debugging a strange segfault in my randomForest package? Here's the scoop: A user reported segfault when running predict() in the randomForest package. I asked for the data and code. The combination runs fine under WinXPPro, but does give segfault on one of our Linux boxes running R (1.7.0 through R-devel_2004-01-08) on