similar to: Parallel processing question ...

Hi Everybody, Thanks in advance for your help. This is my first time using the foreach statement and I cant get it to work properly so here is what i have test<-function(){ repeat { cand2[l-1]<-rinvgamma(1,phi,lambda[l-1]) q2<-dinvgamma(cand2[l-1],phi,lambda[l-1]) p2<-cand2[l-1]^-1.5*exp(-y[l]^2/(2*cand2[l-1]))*exp(-((log(cand2[l-1])-mu_t_cand[l-1])^2)/2*sigmasq)

Hi guys, I was wondering why this piece of code doesn't work: foreach (i = c(1.25,1.50)) %dopar% { assign(paste("test_",i,sep=""),i) } but, this does: foreach (i = c(1.25,1.50)) %do% { assign(paste("test_",i,sep=""),i) } Obviously, the difference is %dopar% vs. %do%. If I use %do%, I get objects test_1.25 and test_1.50, but I don't get these

All, I am not understanding the scoping used in foreach when it is used inside a function. I keep getting "could not find function" errors for functions that are in memory when I try to use foreach within a function call. I have a simple example below. "testFun" is in memory and works when called by foreach directly, but when I place foreach in a function called

Hi everyone, I'm working on a script trying to use foreach %dopar% but without success, so I manage to run the code with foreach %do% and looks like this: The code is part of a MCMC model for projects valuation, returning the most important results (VPN, TIR, EVA, etc.) of the simulation. foreach (simx = NsimT, .combine=cbind, .inorder=FALSE, .verbose=TRUE) %do% { MCPVMPA = MCVAMPA[simx]

Hi useRs, I am running a foreach loop and hoped to get a small message when it hits a multiple of 1000, but it does not work. p <- foreach(i=1:10000, .combine='c') %dopar% { if(i%%1000==0) print(i) sqrt(i) } What is the proper way to do it. Thanks everybody. Stephen B [[alternative HTML version deleted]]

I know this is not a revolution support forum, but as anyone noticed the following? I have a foreach loop to generate random samples. If I run the exact code below in normal r (2.14.1) it works as expected, but if I run it from revolution 4.2.0 each loop returns the same numbers. The only way I can get revolution to give different numbers is using 1 instead of 8 in registerDoSNOW(makeCluster(8,

I am trying to do parallel programming and I tried this library(doSNOW) library(foreach) testfunc<-function(x){ x<-x+1 x } noc<-2 cl <- makeCluster(do.call(rbind,rep(list("localhost"),noc)), type = "SOCK") registerDoSNOW(cl) clusterExport(cl=cl,c("testfunc.r")) testl<-foreach(pp=1:2) %dopar% { testfunc(pp) } And this works but if I try to

Buenas, Quiero ver si he paralelizado correctamente el proceso. Tengo dos dataframes, A y B y quiero calcular la distancia euclídea de todas las filas de A sobre todas las filas de B. Para ello he hecho lo siguiente #cargo las librerias library(foreach) library(doParallel) #establezco el numero de clusters, en mi caso 4, ya que el procesador tiene 4 nucleos cl<-makeCluster(4)

Hi, I'm trying to submit my first package which depends on doParallel: Depends: R (>= 3.0), igraph, doParallel Running hadley devtools: devtools::check() and devtools::release() result in no problem (no ERROR nor NOTE on Linux, Mac and Windows). However, when in use the devtools::build_win() command, it results in the following note: * checking R code for possible problems ... NOTE

I have used foreach() for parallel computing but in the current problem, it is not working. Given the volume and type of the data involved in the analysis, I will try to give below the complete code without reproducible example. In short, each R environment will draw a set of separate files, perform the analysis and dump in separate folders. splist <- c("juoc", "juos",

Hi, I am trying to parallel computing with foreach function, but not able to get the result. I know that in parallel processing, all result is collected in list format, but I am not able to get input there. Any help is really appreciated. esf.m <-foreach (i = 1:n.s, .combine=rbind) %dopar% { EV <- as.data.frame(eig$vectors[,1:n.candid[i]]) colnames(EV) <- paste("EV",

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 all, Within a foreach loop with doSNOW, we cant call functions which come from the non-default package. We need to load(require/library) the package once more within the foreach loop. Anyone knows why would happen like this? Is it caused by the snow package and something happened when "snow" parallelize the job? Other than load the package once more with in the foreach loop, is

Hello all: Please help me with this "can't find object" issue. I'm trying to get leave-one-out predicted values for Beta-binomial regression. It may be the gamlss issue because the code seems to work when %do% is used. I have searched for similar issues, but haven't managed to figure it out. This is on Windows 10 platform. Thanks in advance, Nik #

Your code generates an error that has nothing to do with dopar. I have no idea what your function stack is supposed to do; you may be inadvertently calling utils::stack which would produce this kind of error: > stack(1:25, RAT = FALSE) Error in data.frame(values = unlist(unname(x)), ind, stringsAsFactors = FALSE) : arguments imply differing number of rows: 25, 0 HTH, Peter On Wed, Dec 6,

Dear R-SIG-Debian, I am using R version 3.1.2 with rstudio-server 0.98.113 on debian build 3.2.0-4-amd64 #1 SMP Debian 3.2.68-1+deb7u1 x86_64 GNU/Linux. I often use the %dopar% operator in from the foreach package to run code in parallel. However, the only other use on the box seemingly installed a few items and suddenly %dopar% will use far more than the number of cores I am specifying and

Hi there! I have become a big fan of the 'foreach' package allowing me to do a lot of stuff in parallel. For example, evaluating the function f on all elements in a vector x is easily accomplished: foreach(i=1:length(x),.combine=c) %dopar% f(x[i]) Here the .combine=c option tells foreach to combine output using the c()-function. That is, to return it as a vector. Today I discovered the

Hello, I am trying to run a small example with foreach, but I am having some problems. Here is the code: *library(doMC) registerDoMC() zappa = list() frank = list() foreach (i = 1:4) %dopar% { zappa[[i]] = kmeans (iris[-5],4) frank[[i]] = warnings() }* The code runs without error. However the zappa and frank will be empty lists. If I use regular *for *instead, the list will be filled up

Dear expeRts, is it possible to carry out calculations between different foreach() calls? As for nested loops, you want to carry out calcuations not depending on the inner loop only once and not for each iteration of the innermost loop. Cheers, Marius library(foreach) foreach(i=1:3) %:% foreach(j=1:2) %do% { i <- i+1 print(paste(i,j)) } foreach(i=1:3) %:% i

If the code you are running in parallel is complicated, maybe foreach is not sophisticated enough to find all the variables you refer to. Maybe use parallel::clusterExport yourself? But be a aware that passing parameters is much safer than directly accessing globals in parallel processing, so this might just be your warning to not do that anyway. -- Sent from my phone. Please excuse my brevity.