similar to: R on a multi core unix box

> x <- train[,c( 2:18, 20:21, 24, 27:31)] > y <- train$out > > svm.pr <- svm(x, y, probability = TRUE, method="C-classification", kernel="radial", cost=bestc, gamma=bestg, cross=10) > > pred <- predict(svm.pr, valid[,c( 2:18, 20:21, 24, 27:31)], decision.values = TRUE, probability = TRUE) > attr(pred, "decision.values")[1:4,]

I have a dataset of multiple variables and a response. For example, > str(x) 'data.frame': 3557238 obs. of 44 variables: $ response : Factor w/ 2 levels $ var2: Factor w/5000 levels If var2 for example is a factor with 5000 levels, what is the best approach to determine which of these levels is the most important to include in building the model, and which ones to discard.

Thanks to Saeed Abu Nimeh. I used pkg_add to install R package on 4.4BSD. My directory now has the following: BUILDDIR Makefrag.cc_lo config.log m4 tests Makeconf Makefrag.cxx config.status po tools Makefile R-2.6.1 doc roots Makefile.bak R-2.6.1.tar.gz etc share Makefrag.cc SVN-REVISION libtool

If we use the ROCR package to find the accuracy of a classifier pred <- prediction(svm.pred, testset[,2]) perf.acc <- performance(pred,"acc") Do we?find the maximum accuracy?as follows?(is there a simplier way?): > max(perf.acc at x.values[[1]]) Then to find the cutoff point that maximizes the accuracy?do we do the following?(is there a simpler way): > cutoff.list <-

Hi, I am studying using SVM functions of e1071 package to do prediction, and I found during the training data are "factor" type, then svm.predict() can predict data directly by categories; but if response variables are "numerical", the predicted value from svm will be continuous quantitative numbers, then how can I connect these quantitative numbers to categories? (for

Rmpi is not currently available on CRAN, and I don't think it has been for a few months. It is available at http://www.stats.uwo.ca/faculty/yu/Rmpi/ Does anyone know its current status? A few months ago I corresponded with the author, who noted some build problems (specifically on Debian) were the hang up, and seemed to be working on it. I wasn't able to get it to work then (on a

Is the rmpi package (or rpvm) needed to exploit multiple cores on a single unix box using the snow package. The documentation of the package does not provide info about setting up a single machine with multiple cores. Also, if how effective is it to run a bayesian simulation on parallel (or distributed) processors using the snow package. Thanks, Saeed

Hi all, I'm trying to get the snow package with mpi working on my linux ubuntu 10.04 machine. When I tried to install it (see below) it already had problems with the dependencies. R version 2.14.0 (2011-10-31) Copyright (C) 2011 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You

Hi, does anybody know how to use the dual processors in the machines of a cluster? I am using R with rpvm and snow packages. I usually start pvm daemon and add host machines first, and then run R to start my computing work. But I find that only one processor in each machine is used in this way and the other one always stays idle. Is there any simple way to tell pvm to use the two processors at

Hi, I am working on a 64-bit rocks cluster and am relatively new to the R package. I am trying to get Snow working with R and Rmpi and have run into the following issue. R is able to load the Rmpi and snow libraries and is able to run simple commands both interactively and batch as follows: -------------------------------------------------------------------------------------------------------

I tried ot build rpvm in my own makefile. But runs into some linker errors like e.g. undefined reference to `_R_alloc' My enviornment looks like this: CYGWIN pvm 3.4 compiled under cygwin myself R installed from the rw2001.exe setup file. I guess that the R under rw2001.exe was build with some other compiler? I then tried to compile R myself under CYGWIN but runs into the following

I am trying to install the rpvm package doing this: C:\R\rw2000\bin>rcmd install rpvm_0.6-2.tar.gz '.' is not recognized as an internal or external command, operable program or batch file. '.' is not recognized as an internal or external command, operable program or batch file. make: *** /rpvm: No such file or directory. Stop. make: *** [pkg-rpvm] Error 2 *** Installation of

Hello, I have a question that I was wondering if anyone had a fairly straightforward answer to: what is the quickest and easiest way to take advantage of the extra cores / processors that are now commonplace on modern machines? And how do I do that in Windows? I realise that this is a complex question that is not answered easily, so let me refine it some more. The type of scripts that I'm

Caveat: I've only played with this a couple of years ago... I believe you can just add each host _twice_ (or as many times as the number of CPUs at that host) to get both CPUs to work. Andy From: Paul Y. Peng > > Hi, > > does anybody know how to use the dual processors in the > machines of a cluster? I am using R with rpvm and snow > packages. I usually start pvm

All, We are researching approaches to parallel R with the end goal of running R in a distributed manner on a Linux cluster. We expect of course to do some work decomposing our problems to be task-parallel or data-parallel, but wouldn't mind getting an initial boost working with "embarrassingly parallel" code sections and one of the approaches below. Incidentally our environment

I see about 7 different R packages for multi-process parallel programming. Which do you think is the best, most complete, and most robust to pick for general purpose Erlang-style message-passing programming in R, and why? First here's my use case, and then my analysis so far. I often have code whose basic organization looks something like this: 1. Fetch step: For each date, gather up or

On Wed, Aug 05, 2020 at 04:01:58PM +0300, Eli Cohen wrote: > On Wed, Aug 05, 2020 at 08:48:52AM -0400, Michael S. Tsirkin wrote: > > > > > > Did you merge this?: > > > git pull git://git.kernel.org/pub/scm/linux/kernel/git/mellanox/linux.git mlx5-next > > > > > > I can only merge this tree if no one else will. Linus does not like > > getting

On Wed, Aug 05, 2020 at 04:01:58PM +0300, Eli Cohen wrote: > On Wed, Aug 05, 2020 at 08:48:52AM -0400, Michael S. Tsirkin wrote: > > > > > > Did you merge this?: > > > git pull git://git.kernel.org/pub/scm/linux/kernel/git/mellanox/linux.git mlx5-next > > > > > > I can only merge this tree if no one else will. Linus does not like > > getting

Hello, I'm trying to install the rpvm package under Windows, but I am having problems. I have pvm3.4 installed properly. I've defined the system variables PVM_ROOT = C:\PROGRA~1\pvm3.4\ PVM_ARCH = win32 When I try to install, I get this: C:\R\Packages>Rcmd INSTALL rpvm_1.0.1.tar.gz ---------- Making package rpvm ------------ **********************************************

I upgraded from Fedora Core 4 to Fedora Core 5 and I find a lot of previously installed packages won't run because shared libraries or other system things have changed "out from under" the installed R libraries. I do not know for sure if the R version now from Fedora-Extras (2.2.1) is exactly the same one I was using in FC4. I see problems in many packages. Example, Hmisc: unable