model {
for (i in 1:5) {
y[i] ~ dnorm(mu[i], tau)
mu[i] <- alpha + beta*(x[i] - mean(x[]))
# selection model for missing data mechanism
miss[i] ~ dbern(p[i])
logit(p[i]) <- a + b*(y[i]-250)
}
a ~ dlogis(0, 1)
b <- log(1.02)
alpha ~ dflat()
beta ~ dflat()
tau <- 1/sigma2
log(sigma2) <- 2*log.sigma
log.sigma ~ dflat()
}
Data:
list(y = c(177,236,285,350,NA), x = c(8,15,22,29,36), miss = c(0,0,0,0,1))
Inits:
list(alpha=290,beta=8,log.sigma=0,a=-1)
node mean sd MC error 2.5% median 97.5% start sample
a -1.973 1.082 0.01081 -4.262 -1.913 0.01912 4001 10000
alpha 291.4 13.13 0.3402 280.5 290.6 303.5 4001 10000
beta 8.228 1.314 0.03709 7.035 8.133 9.677 4001 10000
mu[1] 176.2 15.61 0.2245 158.2 176.8 192.0 4001 10000
mu[2] 233.8 11.11 0.1251 222.9 233.7 244.3 4001 10000
mu[3] 291.4 13.13 0.3402 280.5 290.6 303.5 4001 10000
mu[4] 349.0 19.76 0.5921 332.5 347.6 368.9 4001 10000
mu[5] 406.6 27.89 0.8487 382.7 404.6 435.0 4001 10000
sigma2 433.0 4537.0 177.5 5.755 30.73 1063.0 4001 10000
y[5] 408.4 38.23 1.368 378.6 404.9 447.1 4001 10000