model {
for (i in 1:N) {
for (j in 1:3) {
y[i,j] ~ dnorm(psi[i,j], inv.sigma.squared)
psi[i,j] <- alpha[i] + beta[i]*(t[i,j] - tbar)
+ gamma*(y0[i] - y0bar)
}
alpha[i] ~ dnorm(mu.alpha, inv.omega.alpha.squared)
beta[i] ~ dnorm(mu.beta, inv.omega.beta.squared)
}
inv.sigma.squared <- 1/sigma.squared
inv.omega.alpha.squared <- 1/omega.alpha.squared
inv.omega.beta.squared <- 1/omega.beta.squared
sigma.squared <- pow(sigma, 2)
omega.alpha.squared <- pow(omega.alpha, 2)
omega.beta.squared <- pow(omega.beta, 2)
log(sigma) <- log.sigma
log.sigma ~ dunif(-10, 10)
omega.alpha ~ dunif(0, 100)
omega.beta ~ dunif(0, 100)
mu.alpha ~ dnorm(0, 0.0001)
mu.beta ~ dnorm(0, 0.0001)
gamma ~ dnorm(0, 0.0001)

y0bar <- mean(y0[])
}

Inits:
list(log.sigma = 0, omega.alpha = 0.1, omega.beta = 0.1,
mu.alpha = 0, mu.beta = 0, gamma = 0)

Data...

Dbar = post.mean of -2logL; Dhat = -2LogL at post.mean of stochastic nodes
   Dbar   Dhat   pD   DIC   
y   813.563   715.127   98.436   911.998   
total   813.563   715.127   98.436   911.998   

   node   mean   sd   MC error   2.5%   median   97.5%   start   sample
   deviance   813.6   22.05   0.3335   769.1   813.7   856.8   1001   100000
   gamma   0.6711   0.08489   9.3E-4   0.5043   0.6712   0.8374   1001   100000
   mu.alpha   6.137   0.1504   5.448E-4   5.841   6.136   6.433   1001   100000
   mu.beta   -1.075   0.1403   0.003874   -1.343   -1.075   -0.7964   1001   100000
   omega.alpha   1.428   0.1197   5.379E-4   1.209   1.422   1.68   1001   100000
   omega.alpha.squared   2.053   0.3467   0.001557   1.462   2.022   2.822   1001   100000
   omega.beta   0.3042   0.2114   0.00826   0.01907   0.2695   0.7795   1001   100000
   omega.beta.squared   0.1373   0.1693   0.005531   3.635E-4   0.07264   0.6076   1001   100000
   sigma   0.9951   0.05628   5.927E-4   0.8895   0.9933   1.111   1001   100000
   sigma.squared   0.9933   0.1127   0.001173   0.7911   0.9866   1.234   1001   100000