similar to: parallel processing for bridge and blasso

Good day, I need to subset a data by removing several rows. I know the %in% operator, i.e. sub <- mydata[group %in% c("A","B","E","G"), ] What I need is the opposite, that is remove rows and/or columns. What is the operator for "NOT IN"? I tried (i)! %in% and (ii) ^%in% and both resulted in a "could not find function" error.

Good day, I'm ran aov () and lme() on a split-plot using a 64-bit machine. For aov() I don't see the values for ErrorA, F-value and p-value in the output. For lme(), output is different from results from a 32-bit. Please see codes used and corresponding output. Is my code wrong and/or not sufficient or is this a compatibility issue? ************** model1<-aov(Y~Main*Sub +

1.what is the application to install for to speed up processing for multicore processor in windows environment? 2. how to compute time for executing a particular a code? [[alternative HTML version deleted]]

Hello All, In general when we use Rprof for performance evaluation on Multicore systems the output provides the time on the basis of the "user" time and the sampling time is equal to the the user time as reported by system.time. This does not seem right behavior when R is linked to BLAS/Lapack or other libraries which are optimized for parallel or multicore architectures as

Hello All, In general when we use Rprof for performance evaluation on Multicore systems the output provides the time on the basis of the "user" time and the sampling time is equal to the the user time as reported by system.time. This does not seem right behavior when R is linked to BLAS/Lapack or other libraries which are optimized for parallel or multicore architectures as

dear R experts--- I am experimenting with multicore processing, so far with pretty disappointing results. Here is my simple example: A <- 100000 randvalues <- abs(rnorm(A)) minfn <- function( x, i ) { log(abs(x))+x^3+i/A+randvalues[i] } ?## an arbitrary function ARGV <- commandArgs(trailingOnly=TRUE) if (ARGV[1] == "do-onecore") { ?library(foreach) ?discard <-

Hi everyone, Thanks for the help in advance. I just want to know if there is absolutely any way of using multicore if my loop can't run in parallel as each iteration depends on the previous iteration. I have Windows R 2.9.2 and REvolution R Enterprise 2.0 for Windows 64 bit. I also have if necessary R on Ubuntu. 8gb of ram and a core 2 duo processor but I could use a quad core as well. Many

People of R(th), I have been ramming my head against this problem, and I wondered if anyone could lend a hand. I want to parallelize a bootstrap of a linear model on my 8-core mac. Below is the process that I want to parallelize (namely, the m2.ph.rlm.boot<-boot(m2.ph,m2.ph.fun, R = nboot) command). This is an extension of the bootstrapping linear models example in Venables and Ripley to

?I want to take advantage of my multicore CPU to speed up a loop in a simulation program.? I didn?t write the code, but the iterations appear independent to me, at least in the sense that the results of one loop do not depend on previous ones.? Right now I?m relegated to a Windows box that runs Windows 7.? These appear to be the options: ? Pnmath-appears to parallelize non-BLAS routine but

Hi, After reading the slices from ff project site I know ff can be used with snowfall to achieve parallel processing, I want to know is it the only way, can we combine package foreach and ff, especially in a SMP server, with doMC and multicore behind foreach. Xiaobo.Gu [[alternative HTML version deleted]]

Hi, I would like to get suggestion about parallel computing package on a multicore windows workstation. I tried doSMP, but it crashes R a lot. I am wondering if "snow" and "snowfall" can be used on a single workstation (i,e, not cluster). At suggestion would be appreciated, Best, Richard [[alternative HTML version deleted]]

I am using the package Multicore/Parallel to do importance sampling. I have 5 cores on my computer. And I have let's say 10 000 particles to generate. What I did was to send 5 particles in each time, calling the package parallel. Which means in all I am calling the parallel command 2000 times. What happens is in the end somewhere along the way I end up with the error message error in fork

REvolution Computing has just released three new packages for R to CRAN (under the open-source Apache 2.0 license): foreach, iterators, and doMC. Together, they provide a simple, scalable parallel computing framework for R that lets you take advantage of your multicore or multiprocessor workstation to program loops that run faster than traditional loops in R. The three packages build on each

REvolution Computing has just released three new packages for R to CRAN (under the open-source Apache 2.0 license): foreach, iterators, and doMC. Together, they provide a simple, scalable parallel computing framework for R that lets you take advantage of your multicore or multiprocessor workstation to program loops that run faster than traditional loops in R. The three packages build on each

**** CALL FOR PAPERS **** The 21st International ACM Symposium on High-Performance Parallel and Distributed Computing (HPDC'12) Delft University of Technology, Delft, the Netherlands June 18-22, 2012 http://www.hpdc.org/2012 The ACM International Symposium on High-Performance

**** CALL FOR PAPERS **** The 21st International ACM Symposium on High-Performance Parallel and Distributed Computing (HPDC'12) Delft University of Technology, Delft, the Netherlands June 18-22, 2012 http://www.hpdc.org/2012 The ACM International Symposium on High-Performance

**** CALL FOR PAPERS **** **** CALL FOR WORKSHOP PROPOSALS **** The 21st International ACM Symposium on High-Performance Parallel and Distributed Computing (HPDC'12) Delft University of Technology, Delft, the Netherlands June 18-22, 2012

**** CALL FOR PAPERS **** **** CALL FOR WORKSHOP PROPOSALS **** The 21st International ACM Symposium on High-Performance Parallel and Distributed Computing (HPDC'12) Delft University of Technology, Delft, the Netherlands June 18-22, 2012

I've got a win98 laptop and a linux box. On my linux box, I smbmount a directory from the laptop. Alas, win98 boxes crash a lot, and I move the laptop a lot... to and from work. If the laptop crashes or if I move it with a shared directory mounted on my linux box, that mount gets "Stuck".... it's present in /etc/mtab but when I cd to it and do an ls it says: 243 emily ~>ls

Hi all, How can I have a dynamic list for different combinations of grouping factors in the following example? Thanks. dat <- data.frame(x=rnorm(100), a=sample(letters[1:5], replace = T), b=sample(letters[1:5], replace = T), c=sample(letters[1:5], replace = T), d=sample(letters[1:5], replace = T)) ## define grouping