[R] Random seed problem in MCMC coupling of chains

[Dimitris Rizopoulos]

do you want something like this:

niter <- 3
nchain <- 2
rs <- sample(500, niter, TRUE)
for (i in 1:niter) { # iterations
  for (j in 1:nchain) { # chains
    set.seed(rs[i])
    a <- runif(1)
    cat("iter:", i, "chain:", j, "runif:", a, "\n")
  }
}


I hope it helps.

Best,
Dimitris

----
Dimitris Rizopoulos
Ph.D. Student
Biostatistical Centre
School of Public Health
Catholic University of Leuven

Address: Kapucijnenvoer 35, Leuven, Belgium
Tel: +32/16/336899
Fax: +32/16/337015
Web: http://www.med.kuleuven.ac.be/biostat/
     http://www.student.kuleuven.ac.be/~m0390867/dimitris.htm


----- Original Message ----- 
From: "Gorjanc Gregor" <Gregor.Gorjanc at bfro.uni-lj.si>
To: <r-help at stat.math.ethz.ch>
Sent: Wednesday, June 08, 2005 3:27 PM
Subject: [R] Random seed problem in MCMC coupling of chains


> Hello!
>
> I am performing coupling of chains in MCMC and I need the same value
> of seed for two chains. I will show demo of what I want:
>
> R code, which might show my example is:
> niter <- 3
> nchain <- 2
> tmpSeed <- 123
> for (i in 1:niter) { # iterations
>  for (j in 1:nchain) { # chains
>    set.seed(tmpSeed)
>    a <- runif(1)
>    cat("iter:", i, "chain:", j, "runif:", a, "\n")
>    tmpSeed <- .Random.seed
>  }
> }
>
> I get this:
>
> iter: 1 chain: 1 runif: 0.43588
> iter: 1 chain: 2 runif: 0.43588
> iter: 2 chain: 1 runif: 0.43588
> iter: 2 chain: 2 runif: 0.43588
> iter: 3 chain: 1 runif: 0.43588
> iter: 3 chain: 2 runif: 0.43588
>
> but I would like to get:
>
> iter: 1 chain: 1 runif: 0.43588
> iter: 1 chain: 2 runif: 0.43588
> iter: 2 chain: 1 runif: 0.67676
> iter: 2 chain: 2 runif: 0.67676
> iter: 3 chain: 1 runif: 0.12368
> iter: 3 chain: 2 runif: 0.12368
>
> Note that seed value is of course changing, but it is parallel
> between chains.
>
> I am able to do only this, since setting seed at the beginning
> of chain i.e iteration is not a problem, but I want an upper
> scheme, since I compare chains and stop one if some condition is
> satisfied.
>
> tmpSeed <- 123
> for (i in 1:nchain) { # chains
>  set.seed(tmpSeed)
>  for (j in 1:niter) { # iterations
>    a <- runif(1)
>    cat("iter:", j, "chain:", i, "runif:", a, "\n")
>  }
> }
> iter: 1 chain: 1 runif: 0.28758
> iter: 2 chain: 1 runif: 0.7883
> iter: 3 chain: 1 runif: 0.40898
> iter: 1 chain: 2 runif: 0.28758
> iter: 2 chain: 2 runif: 0.7883
> iter: 3 chain: 2 runif: 0.40898
> iter: 1 chain: 3 runif: 0.28758
> iter: 2 chain: 3 runif: 0.7883
> iter: 3 chain: 3 runif: 0.40898
>
> I was looking in 'rlecuyer', 'rsprng' and 'setRNG', but did not find
> anything usable for me.  From reading on http://sprng.cs.fsu.edu/
> 'rsprng' provides just opposite of what I want, 'rlecuyer' is a bit
> to technical for me, but I think it also doesn't give identical
> seed for parallels. 'setRNG', especially it's function 'getRNG()'
> looks nice but its arguments should have seed stored. How can one
> do that?
>
>
> Thanks in advance!
>
> Lep pozdrav / With regards,
>    Gregor Gorjanc
>
> ----------------------------------------------------------------------
> University of Ljubljana
> Biotechnical Faculty        URI: 
> http://www.bfro.uni-lj.si/MR/ggorjan
> Zootechnical Department     mail: gregor.gorjanc <at> bfro.uni-lj.si
> Groblje 3                   tel: +386 (0)1 72 17 861
> SI-1230 Domzale             fax: +386 (0)1 72 17 888
> Slovenia, Europe
> ----------------------------------------------------------------------
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> it,
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