[R] Memory distribution using foreach

Dear R users, I am struggling with memory issues and try to understand a few things. I am using an LSF cluster with PGI compiler and parallel mpi2 computing (whatever does that means..) and i submit a job like: bsub -R rusage[mem=3] -q queue -n 24 R CMD BATCH arguments.. myjob.r ..log

Re: [R] Memory distribution using foreach

Thank you Simon for your quick response. Now I understand a lot of things, and how to use them. Well they should offer some lessons my department how to work with the cluster but unfortunately they didnt and I am trying to find alone everything. Thanks again for the great advise Best wishes Panos

[R] foreach returns null first object in the list

Dear all, I am sending you a copy of a demo that i am trying to make that work and embedded in a more complicated structure. The problem is that i want to get a list of 2 objects (matrices in specific, and thats why i am not specifing a .combine argument if i am not wrong..), but the first

Re: [R] foreach returns null first object in the list

This is the output with the debugging message. I don't really understand what is the problem. post.ls - foreach(i =1:2, .verbose=T) %dopar% { + + fun - func.list[[i]] + if(i==1){fun(Xa,Sa)} + if(i==2){fun(Ta,Sa)} + + } numValues: 2,

Re: [R] foreach returns null first object in the list

OOhhh I am sorry I didnt notice the return before the function. Everything works. Thank you :) Best regards. -- View this message in context: http://r.789695.n4.nabble.com/foreach-returns-null-first-object-in-the-list-tp4676303p4676344.html Sent from the R help mailing list archive at

[R] nls singular gradient ..as always..

Hi all. Sorry for posting again such a topic but I went through previous posts but couldn't find a solution. I use the following code to fit an exponential model to my data. I have 4 different datasets. For 3 datasets nls seems to work fine and I have no error messages. But for 1 dataset I am

Re: [R] nls singular gradient ..as always..

Yes it should look like that... what i am doing is a variogram fit . But the data of course are spread almost all over.. I would guess might be problem with the data only? -- View this message in context: http://r.789695.n4.nabble.com/nls-singular-gradient-as-always-tp4669859p4669898.html