> -----Original Message-----
> From: r-help-boun...@r-project.org
> [mailto:r-help-boun...@r-project.org] On Behalf Of Jens Malmros
> Sent: Sunday, November 08, 2009 11:11 AM
> To: r-help@r-project.org
> Subject: [R] MCMC gradually slows down
>
> Hello,
>
> I have written a simple Metropolis-Hastings MCMC algorithm for a
> binomial parameter:
>
> MHastings = function(n,p0,d){
> theta = c()
Change that theta <- c() to
theta <- numeric(n)
and it will go faster. Growing datasets
can result in a lot of unnecessary memory
management. The original had an obvious
quadratic quality in the plot of n vs. MHastings(n,.2,2)
and preallocating theta to its final length
made it look linear up to n=100000 and at n=100000
the time for the original was 35 seconds vs 3.75
for the preallocated version.
Bill Dunlap
Spotfire, TIBCO Software
wdunlap tibco.com
> theta[1] = p0
> t =1
> while(t<=n){
> phi = log(theta[t]/(1-theta[t]))
> phisim = phi + rnorm(1,0,d)
> thetasim = exp(phisim)/(1+exp(phisim))
> r =
> (thetasim)^4*(1-thetasim)^8/(theta[t]^4*(1-theta[t])^8)
> if(runif(1,0,1)<r){
> theta[t+1] = thetasim
> } else {
> theta[t+1] = theta[t]
> }
> t = t+1
> if(t%%1000==0) print(t) # diagnostic
> }
> data.frame(theta)
> }
>
> The problem is that it gradually slows down. It is very fast in the
> beginning, but slows down and gets very slow as you reach about 50000
> iterations and I need do to plenty more.
>
> I know there are more fancy MCMC routines available, but I am really
> just interested in this to work.
>
> Thank you for your help,
> Jens Malmros
>
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>
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