#  8.3  HOES meta-analyssi
## Monitor  theta, phi, mu, tau, beta, break, theta.pred    for 
# independent and exchangeable baselines

# warning, occasional very high value of phi0 causes 'break'
# to not give statistics

model
{
for(i in 1:12){
rt[i]~dpois(mt[i])
rc[i]~dpois(mc[i])
log(mt[i]) <-log(nt[i]/1000) + phi[i] + theta[i]
log(mc[i]) <-log(nc[i]/1000) + phi[i]
theta[i] <- theta.adj[i] + beta*(phi[i]-mean(phi[1:12]))
theta.adj[i]  ~ dnorm(mu,tau2.inv)
#phi[i]   ~ dunif(-10,10)   #   independence prior 1
  phi[i] ~ dnorm(mu.phi, tau2.phi.inv)
log(rr[i])<-  theta[i]
log(rbase[i])<-phi[i]
}
mu  ~ dunif(-10,10)
tau2.inv <- 1/(tau*tau)
tau ~ dunif(0,10)

mu.phi  ~ dunif(-10,10)
tau2.phi.inv <- 1/(tau.phi*tau.phi)
tau.phi ~ dunif(0,10)
beta  ~ dunif(-10,10)
phi0 <- -mu/beta + mean(phi[1:12])
break<-exp(phi0)
#            predict theta for phi = -1,-.5,0,...., 3.5
for(j in 1:10){
theta.adj.pred[j]  ~ dnorm(mu,tau2.inv)
theta.pred[j] <- theta.adj.pred[j] + beta*( (j-3)/2  -mean(phi[1:12]))
}

}