First of all, allocate 'theta' to be the final size you need. Every
time through your loop you are extending it by one, meaning you are
spending a lot of time copying the data each time. Do something like:
theta <- numeric(n)
and then see how fast it works.
On Sun, Nov 8, 2009 at 2:11 PM, Jens Malmros <jens.malm...@gmail.com> wrote:
> Hello,
>
> I have written a simple Metropolis-Hastings MCMC algorithm for a
> binomial parameter:
>
> MHastings = function(n,p0,d){
> theta = c()
> 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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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
--
Jim Holtman
Cincinnati, OH
+1 513 646 9390
What is the problem that you are trying to solve?
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