similar to: Exporting a method to a cluster object

Hi all, I am trying to use snow package to do a parallel MCMC. I have read a few guides and articles, the following is that I came up with. When I run it I got the error message: Error in checkForRemoteErrors(lapply(cl, recvResult)) : 4 nodes produced errors; first error: could not find function "ui.Next" The data is a longitudinal data with few repeated readings on a number of

R Version : 2.14.1 x64 Running on Windows 7 Connecting to a database on a remote Microsoft SQL Server 2012 The short form of my problem is the following. I have an unordered vectors of names, say: names<-c("A", "B", "A", "C","C") each of which have an id in a table in my db. I need to convert the names to their corresponding ids. I

Here is the code, assuming 8 cores in the cpu. library('modeest') library('snow') cl = makeCluster(rep('localhost', 8), 'SOCK') x = vector(length=50) x = sapply(x, function(i) i=sample(c(1,0), 1)) pastK = function(n, x, k) { if (n>k) { return(x[(n-k):(n-1)]) } else {return(NA)} } predR = function(x, k) { pastList = lapply(1:length(x), function(n)

Hi, The following function use to work, but now it doesn't giving the error "> CallSnow(, 100) Using snow package, asking for 2 nodes 2 slaves are spawned successfully. 0 failed. Error in checkForRemoteErrors(val) : 2 nodes produced errors; first error: no applicable method for 'lapply' applied to an object of class "list" ". Where this is the

Thank you, everybody, who replied! I appreciate your valuable advise! I will move the location of the set.seed() command to after all packages have been installed and loaded. Best regards, Gary Sent from my iPad > On Mar 4, 2018, at 12:18 PM, Paul Gilbert <pgilbert902 at gmail.com> wrote: > > On Mon, Feb 26, 2018 at 3:25 PM, Gary Black <gwblack001 at sbcglobal.net> >

Dear R Users, I am attempting to use snow with rmpi. My configuration is: - R Version 2.7.2, rmpi - RMPI 0.5-6 - DeinoMPI 1.1.0 - Windows Xp SP2 I can sucessfully create a cluster and execute simple commands but for some reason, I cannot change the working directory in my nodes. > noclusters<-2 > cl <- makeCluster(noclusters, type = "MPI") 2 slaves are spawned

On Mon, Feb 26, 2018 at 3:25 PM, Gary Black <gwblack001 at sbcglobal.net> wrote: (Sorry to be a bit slow responding.) You have not supplied a complete example, which would be good in this case because what you are suggesting could be a serious bug in R or a package. Serious journals require reproducibility these days. For example, JSS is very clear on this point. To your question >

On Sun, Mar 4, 2018 at 3:23 PM, Duncan Murdoch <murdoch.duncan at gmail.com> wrote: > On 04/03/2018 5:54 PM, Henrik Bengtsson wrote: >> >> The following helps identify when .GlobalEnv$.Random.seed has changed: >> >> rng_tracker <- local({ >> last <- .GlobalEnv$.Random.seed >> function(...) { >> curr <- .GlobalEnv$.Random.seed

On 04/03/2018 5:54 PM, Henrik Bengtsson wrote: > The following helps identify when .GlobalEnv$.Random.seed has changed: > > rng_tracker <- local({ > last <- .GlobalEnv$.Random.seed > function(...) { > curr <- .GlobalEnv$.Random.seed > if (!identical(curr, last)) { > warning(".Random.seed changed") > last <<- curr

The following helps identify when .GlobalEnv$.Random.seed has changed: rng_tracker <- local({ last <- .GlobalEnv$.Random.seed function(...) { curr <- .GlobalEnv$.Random.seed if (!identical(curr, last)) { warning(".Random.seed changed") last <<- curr } TRUE } }) addTaskCallback(rng_tracker, name = "RNG tracker") EXAMPLE: >

Hello all, I am re-posting my previous question with a simpler, more transparent, commented code. I have been ramming my head against this problem, and I wondered if anyone could lend a hand. I want to make parallel a bootstrap of a linear mixed model on my 8-core mac. Below is the process that I want to make parallel (namely, the boot.out<-boot(dat.res,boot.fun, R = nboot) command).

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

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

Dear all, I am doing a social network analysis using ergm. I import a rectangular matrix of data and then I build a network using the following command: ng <-network(mat,directed=FALSE) I then attach vertex names using commands like this: set.vertex.attribute(ng,"SchoolType",mSchType) where SchoolType can take six different/unique values. Then, I run ergm models successfully using

Greetings, and many thanks for any insight you can offer. I have a network object on which I'd like to simulate an ergm() using {statnet}. Since the network object is large, I have not copied it here. Each vertex has an attribute "language" which can take on integer values greater than or equal to 1. Is there a way to use the nodematch argument of ergm(), such that it searches for

Hi, how can I parse Google search results? The following code returns "integer(0)" instead of "1" although the results of the query clearly contain the regex "cran". #### address <- url("http://www.google.com/search?q=cran") open(address) lines <- readLines(address) grep("cran", lines[3]) #### Thanks Philip -- Philip Leifeld Max

Dear all, how can I convert an m x n incidence matrix into an m x m adjacency matrix or an n x n adjacency matrix? The current matrix contains binary data, hence the new matrix would contain counts of common occurrences. Thank you for your help. Phil

Dear all, I am new to network analysis, but since I have good data I started to read about it and learned how to use the ergm and related packages. I generally get interesting results, but when I run a model including sociality and selective mixing effects for different groups, the model runs (and converges) but I get a warning as follows: mod <- ergm(network ~ edges + gwesp(0, fixed=T) +

I am trying to run a monte carlo process using snow with a MPI cluster. I have ~thirty processors to run the algorithm on and I want to run it 5000 times and take the average of the output. A very simple way to do this is to divide 5000 by the number of processors to get a number n and tell each processor to run the algorithm n times. I realize there are more efficient ways to manage the

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