similar to: snow, stopping cluster

Dear all, First I apologize for cross-posting, but I think that this could be of interest to BioC users, too. For DNA copy-number analysis I have downloaded PLASQ500K from: http://genome.dfci.harvard.edu/~tlaframb/PLASQ/ After creating sub-directories SND and STD containing 3 Sty Mapping arrays each, I tried to compute parent-specific copy number: > library(PLASQ500K) > psCN <-

Hi, I am using a cluster with LAM 7.1.3/MPI 2 and R 2.6.0. Rmpi version 0.5-5 is working very well. Now I have tested "Rmpi 0.5-6". During spawning the Rslaves I get an error: MPI_Error_string: error spawning process > sessionInfo() R version 2.6.0 (2007-10-03) x86_64-unknown-linux-gnu locale:

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Hello, I have this kind of dataframe and have to plot it. data <- data.frame(sw= c(1,2,3,4,5,6,7,8,9,10,11,12,15), zehn = c(33.44,20.67,18.20,18.19,17.89,19.65,20.05,19.87,20.55,22.53,NA,NA,NA), zwanzig = c(61.42,NA,26.60,23.28,NA,24.90,24.47,24.53,26.41,28.26,NA,29.80,35.49), fuenfzig =

Hello, I try to write parts of my code in C to accelerate the for-loops. But basic operations I want to do in R (e.g. start cluster). My R code looks something like this: library(Rmpi) mpi.spawn.Rslaves() mpi.remote.exec(....) dyn.load("test.so") erg <- .Call("test", ....) .... mpi.close.Rslaves() mpi.quit() And my C function looks something like this: #include

Hello, I want to use expresso for preprocessing the hgu133a-spikein data from affycompII. But there is an error: > library(affy) > path <- "z:/Microarray/hgu133a-spikein/rawdata" > celFile <- list.celfiles(path=path,full.names=TRUE); > affyBatch <- ReadAffy(filenames=celFile[1:6]); > eset1 <-

Hello, I try to write an extension in C, to get a faster functions. Therefore I have to add an element (vector) to a vector. The command in R is very simple: x = c(x,a) But in C I have the problem to reallocate my vector for getting more space. Everything I tried, I get a "Segmentation fault". So, how can I combine two vectors in C and give the result back to R (return(x))? Thanks

Hello, we are doing some computations with MPI and Rmpi. Therefore papply is a very comfortable function. Is it possible to reuse calculated data on slaves? We do some calculation with papply, then we have to do some overall calculation at the master then we want to do some calculation on the slaves with the old results and some new results from the master. We do not want to send all data

Hello, the package snow is not working on a windows cluster with MPICH2 and Rmpi. There is an error in makeCluster: launch failed: CreateProcess(/usr/bin/env "RPROG="C:\Programme\R\R-2.5.1\bin\R" "OUT=/dev/null" "R_LIBS=" C:/Programme/R/R-2.5.1/library/snow/RMPInode.sh) on 'cl1' failed, error 3 - Das System kann den angegbenen Pfad nicht finden. I

Hello, which possibilities are available in R for simultaneous or parallel computing? I only could find biopara (http://cran.r-project.org/src/contrib/Descriptions/biopara.html) Are there other possibilities? Are there special groups working on simultaneous computing with R? Thanks Markus -- Dipl.-Tech. Math. Markus Schmidberger Ludwig-Maximilians-Universit?t M?nchen IBE - Institut f?r

A new version of the 'snow' package for parallel computing in R is available at http://www.stat.uiowa.edu/~luke/R/cluster/snow_0.3-3.tar.gz This substantially revises the way in which worker processes are started to allow snow to be used on Windows and Mac/Windows/Linux combinations. I have successfully used the SOCK version on a standalone Windows machine and combinations of

Hi, Can anyone help me of the affylmGUI package, I can't get it work and searched for google but can't find any proper solutions. I get the error information each time when I load my cells files, which are shown in the following links. ------------- http://clarezoe.googlepages.com/1.png http://clarezoe.googlepages.com/2.png http://clarezoe.googlepages.com/3.png ------------- Errors also

Hi! I recognized that the argument matching of clusterApply (and therefore parLapply) goes wrong when one of the arguments of the function is called "c". In this case, the argument "c" is used as cluster and the functions give the following error message "Error in checkCluster(cl) : not a valid cluster". Of course, "c" is for many reasons an unfortunate

Thank you for your answer! I agree with you except for the 3 (Error) example and I realize now I should have started with that in the explanation. >From my point of view parLapply(cl = clu, X = 1:2, fun = fun, c = 1) shouldn't give an error. This could be easily avoided by using all the argument names in the custerApply call of parLapply which means changing, parLapply <-

On 03/15/2018 05:25 PM, Henrik Bengtsson wrote: > On Thu, Mar 15, 2018 at 3:39 AM, <FlorianSchwendinger at gmx.at> wrote: >> Thank you for your answer! >> I agree with you except for the 3 (Error) example and >> I realize now I should have started with that in the explanation. >> >> From my point of view >> parLapply(cl = clu, X = 1:2, fun = fun, c =

This is nothing specific to parallel::clusterApply() per se. It is the default behavior of R where it allows for partial argument names. I don't think there's much that can be done here except always using fully named arguments to the "apply" function itself as you show. You can "alert" yourself when there's a mistake by using: options(warnPartialMatchArgs =

On Thu, Mar 15, 2018 at 3:39 AM, <FlorianSchwendinger at gmx.at> wrote: > Thank you for your answer! > I agree with you except for the 3 (Error) example and > I realize now I should have started with that in the explanation. > > From my point of view > parLapply(cl = clu, X = 1:2, fun = fun, c = 1) > shouldn't give an error. > > This could be easily avoided by

Hello, I built a package, everything was working very well. Now I included a NAMESPACE to export only some variables. (export(f, g, ...) ) Everything is working fine, except one function. There I get an error: Fehler in UseMethod("open") : keine anwendbare Methode f?r "open" In this function I use the function "open" to generate an output bar. * if (verbose) {

Dear R community, cloudnumbers.com provides researchers and companies with the resources to perform high performance calculations in the cloud. As cloudnumbers.com's community manager I may invite you to register and test R on a computer cluster in the cloud for free: http://my.cloudnumbers.com/register Our aim is to change the way of research collaboration is done today by bringing together

I'm using text miner (the "tm" package) to process large numbers of blog and message board postings (about 245,000). Does anyone have any advice for how to efficiently extract the meta data from a corpus of this size? TM does a great job of using MPI for many functions (e.g. tmMap) which greatly speed up the processing. However, the "meta" function that I need does not