model {
for (i in 1:5) {
y[i] ~ dnorm(mu[i], tau)
mu[i] <- alpha + beta*(x[i] - mean(x[]))
}
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))
Inits:
list(alpha=250,beta=0,log.sigma=0)
node mean sd MC error 2.5% median 97.5% start sample
alpha 290.3 7.029 0.06709 279.4 290.4 300.8 4001 10000
beta 8.104 0.8881 0.007908 6.79 8.11 9.355 4001 10000
mu[1] 176.8 11.3 0.0925 161.2 176.8 193.2 4001 10000
mu[2] 233.6 7.063 0.05886 223.1 233.6 243.9 4001 10000
mu[3] 290.3 7.029 0.06709 279.4 290.4 300.8 4001 10000
mu[4] 347.0 11.24 0.108 329.4 347.2 363.0 4001 10000
mu[5] 403.8 16.74 0.158 377.6 404.0 427.7 4001 10000
sigma2 188.1 2915.0 58.29 5.72 29.08 926.0 4001 10000
y[5] 403.6 20.52 0.2122 371.5 404.0 434.2 4001 10000