Dear all,

I'm starting to use rmpi for a lme (nlme) model which is very memory
intensive, and I want to run the lme call therefore on a linux cluster.
However, I haven't exactly found a proper example how to accomplish this
and I'm not really familiar with rmpi. Hence, I hope somebody might help
with this I guess rather trivial problem...
Here is the code I wanted to use, but I'm not sure if the lme call like it
is written here, will actually be spanned across several hosts within the
cluster to come by the memory problem?


#Load libraries
library(nlme)
library(mgcv)

# Load the R MPI package if it is not already loaded.
if  (!is.loaded("mpi_initialize")) {
  library("Rmpi")
}

# In case R exits unexpectedly, have it automatically clean up
# resources taken up by Rmpi (slaves, memory, etc...)
.Last <- function(){
  if (is.loaded("mpi_initialize")){
    if (mpi.comm.size(1) > 0){
      print("Please use mpi.close.Rslaves() to close slaves.")
      mpi.close.Rslaves()
    }
    print("Please use mpi.quit() to quit R")
    .Call("mpi_finalize")
  }
}

#actual call
xylemRohTimeBoth41.lme <- mpi.remote.exec(lme(sapflow ~ NthSampling,
random= ~NthSampling|site/sensor, data=xylemRoh2011, method="REML",
na.action=na.omit,  control=lmeControl(opt = "optim")) simplify = TRUE,
comm = 1, ret = TRUE)

Any help is very much appreciated and sorry for this I guess very basic
question...

Many thanks.

           Katharina

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