Hello Virgilio,

INLA looks like a great alternative to the MCMC methods.  Your right,
ultimately, I am interested in the marginals.  I'll take a look at the
course and let you know how it works out.  This is could be a huge time
saver.

Thanks,

-Ben

On Thu, Mar 12, 2009 at 3:46 AM, Virgilio Gomez Rubio <
Virgilio.Gomez@uclm.es> wrote:

> Hi Ben,
>
> > There are 31625 zipcodes which correspond to a geographic area in the
> > US.  I'm expecting it to take a while to run, as long as it takes less
>
> I know that some people in Spain were using similar models with around
> 8500 areas and it took 18 hours to run. So not sure if in your case
> WinBUGS will be able to give you an answer in a reasonable time. You may
> try to load your shapefiles using a GIS and then try to make the
> adjacency matrix with that. Depending on the software, you may get a
> better memory management.
>
> When fitting your models, I believe that you should try INLA if you have
> a large number of areas. It is a software for Approximate Bayesian
> Infenrece that we will give you the marginals of your parameters of
> interest. Basically, you do not MCMC any more but approximate the
> marginal distributions of your parameters of interest, which is faster.
> In disease mapping most of the time that is what you want (i.e.,
> posterior estimates of your relative risks).
>
> The code is very well written and I am sure that it will handle large
> data sets better than WinBUGS and you will get similar results. There is
> a full course on this here:
>
> http://www.bias-project.org.uk/GMRFCourse/
>
> There is an R interface (R-INLA) that works pretty well.  The
> installation is a bit tricky in Linux, but if you are using Windows I
> believe that it is also possible to have it installed.
>
> Another option is to parallelize the Gibss sampler. I did some work on
> this some time ago using package snow, but I found that, for very large
> problems, it is not very reliable. For some reason, the connection
> between the nodes was lost, although it could also be related to my
> (lack of) configuration of the cluster.
>
> Hope this helps.
>
> Virgilio
>
> PS: Fitting a spatial model on the NC Sids Data with R-INLA is something
> like
>
> library(spdep)
> library(INLA)
>
> source("nb2inla.R")
>
> #Read and display map
> xx <- readShapePoly(system.file("shapes/sids.shp",
> package="maptools")[1],
>      IDvar="FIPSNO", proj4string=CRS("+proj=longlat +ellps=clrk66"))
> nbsids<-poly2nb(xx)
>
> xx$region<-0:99
> xx$region.struct<-0:99
> xx$Exp<-xx$BIR74*sum(xx$SID74)/sum(xx$BIR74)
> xx$SMR<-xx$SID74/xx$Exp
>
> nb2inla(file="ncsids.graph", nbsids)
>
>
> formula  <-
> SID74~f(region.struct,model="besag",graph.file="ncsids.graph",
> param=c(1,0.00005),initial=2.8)+f(NWBIR74,model="rw2",param=c(1,0.05))+
> f(region,model="iid")
>
> mod  <-  inla(formula,family="poisson",data=xx,E=Exp,
>   control.inla=list(h=0.01),verbose=TRUE,
>   inla.call="/home/virgil/usr/bin/inla")
>
> xx$RR<-mod$summary.fitted.values[,1]
> spplot(xx, c("SMR", "RR"))
>



-- 
Benjamin Earl Fissel
Economics Graduate Student
University of California, San Diego
bfissel.googlepages.com/home
bfissel@ucsd.edu
bfissel@gmail.com

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