[R] sorry, [WinBUGS] question

Kehl Dániel kehld at ktk.pte.hu
Mon Sep 5 16:08:16 CEST 2011

Dear Community,

I know this is not the place to ask WinBUGS questions, but I did not get 
any answers on other lists.
I am rather new to the BUGS language and to bayesian modeling, excuse me 
for probably simple questions.
I have to conduct a bayesian meta-analysis of some data. We have 
collected observational and randomized studies related to a certain 
field of interest.
The idea is to analyse the randomized studies with two different priors. 
One is non-informative, the other is calculated from the observational 
ones. We also want to use a sceptical prior.
The code I used for the non-informative prior analysis and to get the 
other prior is following:

        for( i in 1 : Num ) {
          rc[i] ~ dbin(pc[i], nc[i])
         rt[i] ~ dbin(pt[i], nt[i])
         log(pc[i]) <- mu[i]
         log(pt[i]) <- mu[i] + delta[i]
         mu[i] ~ dnorm(0,1.0E-5)
         delta[i] ~ dnorm(d, tau)
        d ~ dnorm(0,1.0E-6)
        tau ~ dgamma(0.001,0.001)
        sigma <- 1 / sqrt(tau)
        relr <- exp(d)

which appears to work fine after loading data and initials. (there was a 
study with 0 treated and 0 control cases, I had to exclude that one for 
some reasons, is there a solution for this?)
If I understand right, I can interpret the "relr" as bayesian estimate 
of relative risk, with credible interval etc.
I have some questions in connection with the informative prior analysis:
- after running this same code for the observational data, how do I 
change the specification of d and tau?
- how can I get posterior probabilities like relr>1?
- usually how many iterations, thin etc. do we use?
- can I get nice graphics with both priors and posteriors on it?

I do have to learn everything on my own, so any help is greatly 
I know R and the BUGS package are able to communicate, is anybody can 
help to solve the task through the R interface would be great.

Thank you for you answer or any kind of help:

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