Here is one way to do it (the way I'm doing it currently)...
dmixnorm <- function(x,mu,sigma,prob) { ## x can be a vector
colSums(prob*dnorm(matrix(x,length(mu),length(x),byrow=TRUE),mu,sigma))
}
d <- 0
for(i in iter)
d <- d + dmixnorm(x, mcmc$mu[i,], mcmc$sigma[i,], mcmc$prob[i,])
d/length(iter)
my mcmc$mu, etc are yours simMeans, simVars and simWeights
This does not take that long time and uses little memory.
The trick is that for-loops are not always that bad (i.e., they are not
always the bottle neck)
Gardar
At 06:09 PM 10/16/2003 -0500, Giovanni Petris wrote:
>Hello,
>
>I am doing mcmc=10000 simulations from a posterior distribution of the
>parameters
>of a mixture of K=6 normal densities.
>I have mcmc by K matrices simMeans, simVars and simWeights containing
>the simulation output: one row for each simulation, one column for
>each normal component of the mixture.
>One thing I would like to do is a plot of the posterior predictive
>density. In order to do that I define a vector x of points at which I
>want to evaluate this density. And then I use the following commands:
>
>pred <- colMeans(apply(structure(dnorm(rep(x,each=mcmc*K), mean=simMeans,
>sd=sqrt(simVars))*
>
>rep(simWeights,length(x)),dim=c(mcmc,K,length(x))),c(1,3),sum))
>lines(x,pred)
>
>Everything works, but it is very slow. Does anybody have suggestions
>to do the same thing in a more efficient way?
>
>Thanks in advance,
>Giovanni Petris
>
>--
>
> __________________________________________________
>[ ]
>[ Giovanni Petris [EMAIL PROTECTED] ]
>[ Department of Mathematical Sciences ]
>[ University of Arkansas - Fayetteville, AR 72701 ]
>[ Ph: (479) 575-6324, 575-8630 (fax) ]
>[ http://definetti.uark.edu/~gpetris/ ]
>[__________________________________________________]
>
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___________________________________________________________________
Gardar Johannesson
Lawrence Livermore National Laboratory
7000 East Avenue, L-229
Livermore, CA 94550
[EMAIL PROTECTED]
Tel: (925) 422-3901, Fax: (925) 422-4141
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