model {
for (j in 1:3) {
y[j] ~ dbin(p[j], n[j])
logit(p[j]) <- theta[1] + theta[2]*z[j]
z[j] ~ dnorm(mu[j], 0.01232)
mu[j] <- alpha + beta*x[j]
}
theta[1] ~ dnorm(0, 0.0001)
theta[2] ~ dnorm(0, 0.0001)
}
Data:
list(y = c(21, 20, 15), n = c(48, 34, 21), x = c(10, 30, 50), alpha = 4.48, beta = 0.76)
Inits:
list(theta = c(0.0, 0.0), z = c(0.0, 0.0, 0.0))
node mean sd MC error 2.5% median 97.5% start sample
theta[1] -0.8096 0.8559 0.03736 -2.889 -0.6557 0.3502 12001 20000
theta[2] 0.04207 0.03144 0.001313 -0.002226 0.03667 0.1212 12001 20000
z[1] 12.8 8.299 0.2011 -3.881 12.98 28.81 12001 20000
z[2] 27.43 7.474 0.08438 12.95 27.37 42.39 12001 20000
z[3] 41.43 8.56 0.1437 25.35 41.23 58.56 12001 20000