similar to: need help debugging on Windows

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

Dear all, Has anyone seen the same problem? I tried compiling R-1.9.0 alpha (2004-03-16) on our Opteron box running SUSE Linux ES8. I ran: ./configure --enable-R-shlib --with-blas=goto and got: Source directory: . Installation directory: /usr/local C compiler: gcc -m64 -O2 -g -msse2 -march=k8 -Wall --pedantic C++ compiler: g++ -m64 -O2 -g

On Tue, Jun 25, 2013 at 7:07 PM, Daniel P. Berrange <berrange@redhat.com>wrote: > On Tue, Jun 25, 2013 at 07:02:40PM +0200, Roland Giesler wrote: > > System: > > $ cat /etc/issue > > Ubuntu 12.10 \n \l > > > > $ uname -a > > Linux Matt-HP 3.5.0-34-generic #55-Ubuntu SMP Thu Jun 6 20:20:19 UTC 2013 > > i686 i686 i686 GNU/Linux > > > >

System: $ cat /etc/issue Ubuntu 12.10 \n \l $ uname -a Linux Matt-HP 3.5.0-34-generic #55-Ubuntu SMP Thu Jun 6 20:20:19 UTC 2013 i686 i686 i686 GNU/Linux I have a converted VMWare virtual machine which I converted by using: $ vmware2libvirt -f ./WinXPPro.vmx > WinXPPro.xml $ virsh -c qemu:///system define WinXPPro.xml After various efforts to start the machine, I changed some of the

Dear all, I am trying to train a randomForest using all my control data (12,000 cases, ~ 20 explanatory variables, 2 classes). Because of memory constraints, I have split my data into 7 subsets and trained a randomForest for each, hoping that using combine() afterwards would solve the memory issue. Unfortunately, combine() still runs out of memory. Is there anything else I can do? (I am not using

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

A few days ago I asked whether there were options other than na.action=na.fail for the R port of Breiman?s randomForest; the function?s help page did not say anything about other options. I have since discovered that a pdf document called ?The randomForest Package? and made available by Andy Liaw (who made the tool available in R---thank you) does discuss an option. It is an implementation of

hello, I want to use randomForest to classify a matrix which is 331030?42,the last column is class signal.I use ? Memebers.rf<-randomForest(class~.,data=Memebers,proximity=TRUE,mtry=6,ntree=200) which told me" the error is matrix(0,n,n) set too elements" then I use: Memebers.rf<-randomForest(class~.,data=Memebers,importance=TRUE,proximity=TRUE) which told me"the error is

I read through the "Writing R Extensions" and the "Debugging in R" website (http://www.stats.uwo.ca/faculty/murdoch/software/debuggingR/), looking for some hints about how to solve the issue of debugging problems encountered during the "R CMD check" process, but nothing seems to be mentioned about addressing issues encountered. Specifically, I am working with the R

Hi there, We're trying to use randomForest to do some predictions. The test-harness for our code is pretty straightforward: library ('randomForest'); data202 <- read.csv ("random.csv", header=TRUE); x<- data202[1:50000,1:6]; y<- data202[1:50000,8]; y<- y[,drop=TRUE]; x2 <- data202[50001:60000,1:6]; y2 <- data202[50001:60000,8]; y2 <-

I have some questions about 1. nomenclature, 2. recommended file locations and 3. overall procedure related to creating packages. To the extent that it matters, examples here relate to Windows XP R 2.0.1 beta. The questions are interspersed and prefaced with ***. My understanding is that there are actually 6 forms of a package that one should use in package development: 1.

Hello, When I plot or look at the error rate vector for a random forest (rf$err.rate) it looks like a descending function except for a few first points of the vector with error rates values lower(sometimes much lower) than the general level of error rates for a forest with such number of trees when the error rates stop descending. Does it mean that there is a tree(s) (that is built the first in

Dear R users, I want to install the Rcmdr package in a Quantian linux machine, but, when i tried that, following commands appeared: ** building package indices ... * DONE (Rcmdr) The downloaded packages are in /tmp/RtmpuOikyk/downloaded_packages > library("Rcmdr") Loading required package: tcltk Error in firstlib(which.lib.loc, package) : Tcl/Tk support is not

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

My organization has a policy against "installing software downloaded from the Internet." There is a waiver procedure, but it is difficult and lengthy. It is much easier to get approval to purchase software, even if it is a CD copy of open-source. However, a search on r-project.org didn't seem to show any way of obtaining the distribution in this way. Any clues would be

My organization has a policy against "installing software downloaded from the Internet." There is a waiver procedure, but it is difficult and lengthy. It is much easier to get approval to purchase software, even if it is a CD copy of open-source. However, a search on r-project.org didn't seem to show any way of obtaining the distribution in this way. Any clues would be

A bit of a perennial I'm afraid. My wife has filled up her iPhone 6 with photos, and wants them moved onto my computer. I'm running CentOS 7 patched about 30 minutes ago. Needless to say the computer can't see the data on the iPhone, though it does recognise the phone as an iPhone. Any suggestions (well any that don't involve a steam roller, sledge hammer or GBH to the whole of

Hi, here are three results of random forest (version 4.0-1). The results seem to be more or less the same which is strange because I changed the classwt. I hoped that for example classwt=c(0.45,0.1,0.45) would result in fewer cases classified as class 2. Did I understand something wrong? Christian x1rf <- randomForest(x=as.data.frame(mfilters[cvtrain,]),

Hello R Gurus, I am perplexed by the different results I obtained when I ran code like this: set.seed(100) test1<-randomForest(BinaryY~., data=Xvars, trees=51, mtry=5, seed=200) predict(test1, newdata=cbind(NewBinaryY, NewXs), type="response") and this code: set.seed(100) test2<-randomForest(BinaryY~., data=Xvars, trees=51, mtry=5, seed=200, xtest=NewXs, ytest=NewBinarY) The

Hello list, A short time ago, I found http://johnhaller.com/jh/useful_stuff/portable_apps_suite/, which contains basically a complete set of office tools that can be run *entirely* from a USB key. The concept is: - find a Windows PC - put in your USB key - run OpenOffice, Firefox, Gaim, Nvu, Thunderbird, ... directly from your USB key, with no app installation required - save your files wherever