similar to: parApply computing

I've started to use the parallel package and it works very well speeding things up. Thank you for making this easy to do. Should I have expected that parCapply would return a vector when parApply returns a matrix? library(parallel) x <- matrix(rnorm(8), nc = 2) apply(x, 2, function(y) y) [,1] [,2] [1,] -0.9649685 0.91339851 [2,] -1.4313140 0.13457671 [3,] 1.0499248

Hi I try parallelising some code using the snow package and the following lines: cl <- makeSOCKcluster(8) pfunc <- function (x) (if(x <= (-th)) 1 else 0) ###correlation coefficient clusterExport(cl,c("pfunc","th")) cor.c.f <- parApply(cl,tms,c(1,2),FUN=pfunc) The parApply results in the error message: > cor.c.f <- parApply(cl,tms,c(1,2),FUN=pfunc) Error

Hello All, I can run the "lower level" functions OK, but many of the higher level (eg. parSApply) functions are generating errors. When running the example (from the snow help docs) for parApply on MacOSX 10.5.5, I get the following error: cl <- makeSOCKcluster(c("localhost","localhost")) sum(parApply(cl, matrix(1:100,10), 1, sum)) Error in

Hi, I was wondering whether this query was addressed on how to perform validation through boostrapping. I am currently trying to implement a boostrapping approach to validation but don't know where to start. Help please. Thank you and Regards, Vivienne Ozohili Risk Model Validation Manager Group Risk Independent Control Unit A5, Bank of Ireland Head Office Lower Baggot Street Dublin 2 Tel:

Dear R users, we are trying to do some parallel computing using library(snow). In particular we have a cluster with 3 nodes >cl <- makeCluster(3, type = "MPI") 3 slaves are spawned successfully. 0 failed. and we want to compute the function op_mat (see below) first with the master and then with the cluster using system.time for checking the computational performance.

Hello, All, GWAF 1.2 R.Version() is below. system(lme.batch.imputed( phenfile = 'phenfile.csv', genfile = 'CARe_imputed_release.0.fhsR.gz', pedfile='pedfile.csv', phen='phen1', covar=c('covar1','covar2'), kinmat='imputed_fhs.kinship.RData', outfile='imputed.FHS.IBC.GWAF.LME.output.0.txt' )) Gives the error messages: Error in

invoking parLapply without a cluster fails to find a previously registered cluster > library(parallel) > setDefaultCluster(makePSOCKcluster(2)) > parLapply(X=1:2, fun=function(...) {}) Error in cut.default(i, breaks) : invalid number of intervals This is because in parLapply length(cl) is determined before defaultCluster(cl) is called. By inspection, this appears to be true of

I've been using parApply() in snow package for parallel computing with the following lines in R 2.8.1: library(snow) nNodes <- 4 cl <- makeCluster(nNodes, type = "SOCK") fm <- parApply(cl, myData, c(1,2), func1, ...) Since I have a Mac OS X (version 10.4.11) with two dual-core processors, I thought that I could run 4 simultaneous clusters. However with the 1st job

I am making the question clear. Please help. > Dear R experts > > I was using kinship package to fit mixed model with kinship matrix. > The package looks like lme4, but I could find a way to extract p-value > out of it. I need to extract is as I need to analyse large number of > variables (> 10000). > > Please help me: > > require(kinship) > > #Generating

Hi to everyone, I'm a PhD student and I'm involved in non parametric analyses of hierarchical models. I tried to use package DPpackage on my data, but I encountered some problems in interpreting ouputs. Can anybody help me? The problem can be remued as follows: I have a logit hierarchical model for survival (i.e. binary response) in patients affected by heart failure (the court

Hello, I'm new to boostrapping and I'd need some help to understand the error message that pops up when I run my script. I have a data.frame with 73 lines and 21 column. I am running a stepwise regression to find the best model using the R function "step". I apply bootstrapping to obtain model coefficients. This is my script: # "datare80" is the name of the

Dear all, I have used the Rmpi package many times before however this time I've installed it as I always do with openMPI tar.gz file direct from the website. I'm installing on my ubuntu 8.10. Linux 2.6.27-11-generic #1 SMP Thu Jan 29 19:28:32 UTC 2009 x86_64 GNU/Linux All i get is: > library(Rmpi) Segmentation fault :~$ Which dumps me back into the shell, and doens't give me much

Hi Greg, I don't know if this is related to your problem, but I get the same error (on both ubuntu and fedora linux, R 2.9) and just found a very curious behaviour - snowfall apply functions don't like the variable name "c". E.g.: c<-1 sfLapply(1:10, exp) issues the same error you had posted, while subsequent rm("c") sfLapply(1:10, exp) runs fine. Rainer

(Posted also to debian-user) In my office network I have access to a debian powerpc server and 2 freebsd 6 servers (actually one of them is my notebook). Experienced user of the statistical software R, I have now a go at parallel computation via (r-)pvm and snow under R to enhance the performance of a heavy duty statistical problem involving many iteration on the calculation of models. I moved

Hi, Here are the issues I found: Typos ----- (a) Found: It a parallel version of ?evalq?, "is" missing. (b) Found: 'parLapplyLB', 'parSapplyLB' are load-balancing versions, intended for use when applying ?FUN? to 'parLapplyLB' has no 'FUN' arg (more on this below). (c) Found: 'clusterApply' calls 'fun' on the first

Hello all. I have two small questions in one post, for the sake of brevity. 1. I have some objects that I want to delete. I have the line: rm (c (channelheader, paste ("channel", 1:3, sep=""))) I have tried a few variations, including list=, but cannot figure it out. In SAS, I can use a ':' as a wildcard. Is there any equivalent in R? 2. Is there any possible was to

Dear all, I can't get the labels slot in ICLUST to accept a character vector. library(psych) test.data <- Harman74.cor$cov ic.out <- ICLUST(test.data,nclusters =4,labels=letters[1:ncol(test.data)]) ##?Error in !labels : invalid argument type ic.out <- ICLUST(test.data,nclusters =4,labels=1:ncol(test.data)) ## OK Any ideas?

Hi, can I understand why this message was rejected ? Thanks, Eliano Sent from my iPhone On 6 Mar 2013, at 19:18, Eliano <eliano.m.marques at gmail.com> wrote: > Hi everyone, > > I am writing some code to generate a function. I am passing that code to a > dataset which i'm importing in R, e.g. > Test=read.table('C:/test.txt', header=F, sep='\t',

Just for the interest of discussion, I find it completely weird and interesting that GCC needs to build itself 3 times to fully bootstrap. Has there been any interest in looking at a single compile build? I don't exactly know the limitations, but my naive thinking is that C++14 compiler source parsed by C++14 capable compiler and codegen'd to C99 (or older) source should make it compilable

Hello, The problem I'm working now requires to operate on big matrices. I've noticed that there are some packages that allows to run some commands in parallel. I've tried snow and NetWorkSpaces, without much success (they are far more slower that the normal functions) My problem is very simple, it doesn't require any communication between parallel tasks; only that it divides