[R-sig-ME] glm module in

[Ben Bolker]

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  I'm curious whether anyone knows whether, or how, the following model
can be made to use the 'glm' module in JAGS ... I followed the advice in
<https://stat.ethz.ch/pipermail/r-sig-mixed-models/2011q1/005780.html>
and looked at list.samplers() on the result, with the following outcome:

> unique(names(list.samplers(ooo$model)))
[1] "FiniteMethod"  "RealSlicer"    "ConjugateBeta"

  i.e. no glm module.

  I'm using
[1] R2jags_0.02-15   rjags_2.2.0-4    R2WinBUGS_2.1-18 coda_0.14-4

 which uses JAGS 2.2.0 (I haven't spent the time yet to figure out how
to upgrade), but a glance at the manual suggests that the glm module
hasn't changed radically since then ...

  For background, the model is a mixed zero-inflated Poisson, with the
model matrix constructed in R and passed into JAGS in the data.

  If I have time I will work to break this down to a simpler example,
but in the meantime if anyone happens to have any bright ideas ...

  sincerely
    Ben Bolker
============

model {

  ## PRIORS
  for (m in 1:5){
    beta[m] ~ dnorm(0, 0.01)           # Linear effects
  }
  alpha ~ dnorm(0, .01)
  sigma ~ dunif(0, 5)
  tau <- 1/(sigma*sigma)
  psi ~ dunif(0, 1)
  for(j in 1:nnests){
     a[j] ~ dnorm(0, tau)
   }

   for(i in 1:N){
       SibNeg[i] ~ dpois(mu[i])
       mu[i] <- lambda[i]*z[i]+0.00001
     ## hack required for Rjags -- otherwise 'incompatible'
       z[i] ~ dbern(psi)
       log(lambda[i]) <- offset[i] + alpha +
              inprod(X[i,],beta) + a[nest[i]]
   }
}
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