similar to: Question about foreach (with doSNOW), is that a bug?

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,

Dear list, When I run an R CMD check --as-cran on my package (pROC) I get the following note: > Uses the superseded package: ?doSNOW? The fact that it uses the doSNOW package is correct as I have the following example in an .Rd file: > #ifdef windows > if (require(doSNOW)) { > registerDoSNOW(cl <- makeCluster(2, type = "SOCK")) > ci(roc2,

Oh, I completely missed that one. It's very neat as it seems to work both on Windows and Unix. Thanks! Xavier On 10/02/15 10:52, Martyn Plummer wrote: > The CRAN package snow is superseded by the parallel package which is > distributed with R since version 2.14.0. Here are the release notes > > \item There is a new package \pkg{parallel}. > > It incorporates (slightly

Dear All, I have three classes of questions about generating random numbers with different packages (windows xp 32bit R). . 1. Suppose I would like to use package *foreach*, can I use current Sys.time as a seed? Although I can get the time up to1e-6 second precesion, the code below dose not work well on a local machine with two cores. ################# library(foreach) library(snow)

Whas missing the R in the command line: "mpirun -n --hostfile /home/hostfile R --no-save -f rtest.R" Hope this helps mario On 13-Jan-11 22:08, Justin Moriarty wrote: > Hi, > Just wanted to share a working example of doSNOW and foreach for an openMPI cluster. The function eddcmp() is just an example and returns some inocuous warnings. The

Shouldn't -n be 4 in the bsub command? One master+3 slaves. This was required for snowfall, but I think doSNOW is similar. Hope it helps mario On 16-Dec-10 23:09, Marius Hofert wrote: > Dear expeRts, > > I try to get a minimal example for parallel computing via "foreach" + "doSNOW" to run on a computer cluster (Brutus from

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

Hello, I was using snow to parallel-process some code in R-2.10 (32-bit windows. ). The code is as follows: require(foreach) require(doSNOW) cl <- makeCluster(6, type='SOCK') registerDoSNOW(cl) bl2 <- foreach(i=icount(length(unqmrno))) %dopar% { (some code here) } stopCluster(cl) When I run the same code in Windows R-2.11 (either 32-bit or 64-bit), R hangs at

Hello group, I am experimenting with parallel processing on my quad core Win 7 32 bit machine. Using these packages for the first time. I can see all my processor running at full performance when I use a smaller dataset require(snow) require(doSNOW) require(foreach) #change the 8 to however many cores\phys processors you have on your machine cl.tmp = makeCluster(rep("localhost",4),

Dear expeRts, I struggle with the following problem using snow clusters for parallel computing: I would like to specify l'Ecuyer's random number generator. Base R creates a .Random.seed of length 7, the first value indicating the kind fo random number generator. I would thus like to use the components 2 to 7 as the seed for l'Ecuyer's random number generator. By doing so, I

Hello, I'm running some code that requires untaring many files in the first step. This takes a lot of time and I'd like to do this in parallel, if possible. If it's the disk reading speed that is the bottleneck I guess I should not expect an improvement, but perhaps it's the processor. So I want to try this out. I'm working on windows 7 with R 2.15.1 and the latest foreach

The CRAN package snow is superseded by the parallel package which is distributed with R since version 2.14.0. Here are the release notes \item There is a new package \pkg{parallel}. It incorporates (slightly revised) copies of packages \CRANpkg{multicore} and \CRANpkg{snow} (excluding MPI, PVM and NWS clusters). Code written to use the higher-level API functions in those packages should

Dear expeRts, This is a similar post as on R-SIG-MAC [I didn't receive an answer there; not sure if it was the right place to post either]. I'm running R version 2.12.1 (Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)) on a MacBook Pro under Mac OS X 10.6.6. The following minimal example runs fine under this setup. However, if I am connected to a server via a VPN client [Cisco

Dear Tomas, thank you very much. I installed r-devel r75201 and tested. The machine with 88 cores has NUMA disabled. It therefore has 2 processor groups with 64 and 24 processors each. require(parallel) detectCores() # [1] 88 This is great! Then I went on to test with a simple 'foreach()' loop. I started with 64 processors (max limit of 1 processor group). I ran with a simple function

Hello there, I have a linear regression model for which I estimated heteroskedasticity-robust (Huber-White) standard errors using the coeftest function in the lmtest-package. Now I would like to inspect the predicted values of the dependent variable for particular groups and include a confidence interval for this prediction. My question: is it possible to estimate confidence intervals for the

Hi, I have a simple linear regression for which I want to obtain HAC corrected standard errors, since I have significant serial/auto correlation in my residuals, and also potential heteroskedasticity. Would anyone be able to direct me to the function that implements this in R? It's a basic question and I'm sure I'm missing something obvious here. I looked up this post:

Can anyone please tell me how to get the r-squared output from a piecewise (segmented) regression using the strucchange package? Here is the R code I have tried thus far. library(lmtest) library(strucchange) data <- ts(c(rnorm(30), runif(30)), frequency = 12, start = c(2005, 01)) bpts <- breakpoints(data ~ 1) print(bpts) summary(bpts) coeftest(bpts) [[alternative HTML version

Hi, I am relatively new to R; when creating functions, I run into problems with missing values. I would like my functions to ignore rows with missing values for arguments of my function) in the analysis (as for example is the case in STATA). Note that I don't want my function to drop rows if there are missing arguments elsewhere in a row, ie for variables that are not arguments of my

A summary for reference: the new detectCores() for Windows in R-devel seems to be working both for logical and physical cores on systems with >64 logical processors? (thanks to Arun for testing!). If the feature is important for anyone particularly using an older version of Windows and/or on a system with >64 logical processors, it would be nice if you could test and report any

Stephen, thanks for your effort. The more appropriate list for this discussion is probably R-devel (as far as I understand it) so I've moved the discussion there. Related topics have already been discussed in the past. Specifically, I remember contributions by Paul Johnson ("rockchalk" package) and John Fox ("effects" and "car" package) as their packages