Dear Eric,

It looks like you are not getting your data into the model properly.
This would explain why the posteriors look like the priors. It also
explains why the run time is almost independent of the number of
samples. If a model has no data, then every node is forward-sampled from
its prior. This forward sampling is extremely efficient, so the run time
is dominated by the (relatively slow) compilation of the model.

You can extract the data from a jags model with the member function
data(), e.g.

modelInit$data()

Try this and see what it gives you.

Martyn


to see whether the 
On Mon, 2011-10-03 at 18:54 -0500, eric magar wrote:
> Dear BUGS users:
> 
> I am having trouble fitting an IRT model in Linux with JAGS and wonder
> if this is the appropriate forum to seek help. Suggestions will be
> much appreciated.
> 
> I have used winBUGS (via R2winBUGS) for some time with success, but am
> now confronting a model with thousands of parameters and a fairly
> large dataset. Updating takes very long --- 5k iterations with 3
> chains can take up to four hours, and I need many many more. Attempts
> to use snow for parallel computation in the quad core processor have
> failed beyond my understanding, so I abandoned that route.
> 
> I am now attempting to fit in Linux to compare speed using R's
> system.time, and for this I migrated from BUGS to JAGS. My first Linux
> trial run with 20 iterations appeared very promising --- the model
> reports to run much much faster:
> 
> > Compiling model graph
> >    Resolving undeclared variables
> >    Allocating nodes
> >    Graph Size: 838547
> >
> > Initializing model
> >
> >    user  system elapsed
> >  97.310   0.440  97.806
> 
> The equivalent triad in winBugs was about 241, .32, and 242, a
> substantial improvement --- apparently. I then attempted a more
> serious 10k burnin with 10k iterations. To my astonishment, the
> reported system.time was virtually identical and the posterior sample
> looks unmodified compared to the priors. There is, evidently, a
> problem that is going unreported.
> 
> Can anyone offer clues about what may be going on and how I can
> attempt to fix it? The model and key commands appear below for
> reference.
> 
> Many thanks!
> 
> Eric Magar
> Ciencia Política, ITAM
> Mexico City
> +52 55 5628 4079
> @emagar
> 
> 
> ###########
> ## MODEL ##
> ###########
> # rjags version
> cat("
> model {
> for (j in 1:J){
>     for (i in 1:I){
>       v.star[j,i] ~ dnorm(mu[j,i],1)T(lo.v[j,i],hi.v[j,i]);   ##
> truncated normal sampling (BUGS uses I)
>       mu[j,i] <- beta[i]*x[j] - alpha[i] + delta[i]*y[j]
>                   }
>                 }
>   ## priors ################
> for (j in 1:J){
>     x[j] ~  dnorm(0, 1)
>     y[j] ~  dnorm(0, 1)
>     }
> for(i in 1:(west-1)){
>     alpha[i] ~ dnorm( 0, 1)
>     beta[i]  ~ dnorm( 0, 1)
>     delta[i] ~ dnorm( 0, 1)
>     }
> alpha[west] ~ dnorm( 0, 1)
> beta[west]  ~ dnorm(-4, 20)  ## informative
> delta[west] ~ dnorm(-4, 20)  ## informative
> for(i in (west+1):(east-1)){
>     alpha[i] ~ dnorm( 0, 1)
>     beta[i]  ~ dnorm( 0, 1)
>     delta[i] ~ dnorm( 0, 1)
>     }
> alpha[east] ~ dnorm( 0, 1)
> beta[east]  ~ dnorm(-4, 20)  ## informative
> delta[east] ~ dnorm( 4, 20)  ## informative
> for(i in (east+1):(north-1)){
>     alpha[i] ~ dnorm( 0, 1)
>     beta[i]  ~ dnorm( 0, 1)
>     delta[i] ~ dnorm( 0, 1)
>     }
> alpha[north] ~ dnorm( 0, 1)
> beta[north]  ~ dnorm( 4, 20)  ## informative
> delta[north] ~ dnorm( 4, 20)  ## informative
> for(i in (north+1):I){
>     alpha[i] ~ dnorm( 0, 1)
>     beta[i]  ~ dnorm( 0, 1)
>     delta[i] ~ dnorm( 0, 1)
>     }
> }"
> , file="model.jag")
> #end model##############
> ##
> dip.parameters <- c("delta","beta", "alpha", "x", "y")
> dip.inits<-function() {
>   list(v.star=vstar, x = rnorm(J), y = rnorm(J), alpha = rnorm(I),
> delta = rnorm(I), beta = rnorm(I))
>   }
> dip.data <- list ("J"=J, "I"=I, "lo.v"=lo.v, "hi.v"=hi.v, "west"=west,
> "east"=east, "north"=north)
> ##
> #test ride to see program works
> myWrap <- function(){
>     modelInit <- jags.model (
>          "model.jag",
>          inits=dip.inits,
>          data=dip.data,
>          n.chains=3,
>          n.adapt=10)
>     dip.60 <- coda.samples (modelInit,
>          dip.parameters,
>          n.iter=10,
>          thin=2)
>     return(dip.60)
> }
> print(system.time(myWrap()))


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