Have you read the help page?
Initially, there is no seed; a new one is created from the
current time when one is required. Hence, different sessions will
give different simulation results, by default.
Thus if you choose to launch processes on different machines at the
same time you will get the same random number stream.
Running random number streams for parallel computation is a (very)
specialized topic and you need to be aware of the literature. I will
point out packages rsprng and accuracy (function runifS).
On Mon, 30 Jul 2007, Jiqiu Cheng wrote:
Dear sir,
I want to submit R batch jobs (e.g. 5) under the linux cluster by
the script file do_mul.
The script file do_mul
#!/bin/bash
export var
for var in $(seq 1 5)
do
qsub -v var do_test
done
exit 0
Through do_mul, 5 do_test script files are submitted to the cluster.
The script file do_test:
#!/bin/bash -l
#PBS -l ncpus=1
#PBS -l walltime=0:05:00
cd $PBS_O_WORKDIR
mkdir test$var
cd test$var
module load R/2.5.0
R --vanilla test
exit 0
The content in R file test is :
rm(list=ls(all=TRUE))
sample(10)
I expect to have different samples each time. However, for these 5
replications, the first 3 jobs giving me the same samples and the last
2 are the same. I'm confused because I already used R --vanilla to
avoid loading same workspace each time and rm(list=ls(all=TRUE)) to
remove the same random seed each time. Why do same samples still
happen among 5 replications? Does anybody have some ideas to solve
this problem? Looking forward to your reply, thanks.
Regards,
Jiqiu
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Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
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