model {
for(j in 1:27) {
y[j] ~ dnorm(mu[j], tau)
mu[j] <- alpha - beta*pow(gamma, x[j])
# prior on covariate
x[j] ~ dlnorm(mu.x, p.x)
}
alpha ~ dunif(0, 100)
beta ~ dunif(0, 100)
gamma ~ dunif(0, 1)
tau <- 1/pow(sigma, 2)
log(sigma) <- log.sigma
log.sigma ~ dunif(-10, 10)
# priors on mean and precision of covariate model
mu.x ~ dunif(-10, 10)
p.x <- 1/pow(sd.x, 2)
sd.x ~ dunif(0, 10)
}
Inits:
list(alpha = 3, beta = 2, gamma = 0.9, log.sigma = -5, mu.x = 2, sd.x = 0.01)
Data:
list(x = c(1.0,1.5,1.5,
NA
,2.5,4.0,5.0,5.0,NA,8.0,8.5,9.0,
9.5,9.5,10.0,12.0,12.0,13.0,NA,14.5,15.5,15.5,
16.5,17.0,NA,29.0,31.5),
y = c(1.80,1.85,1.87,1.77,2.02,2.27,2.15,2.26,2.47,
2.19,2.26,2.40,2.39,2.41,2.50,2.32,2.32,2.43,
2.47,2.56,2.65,2.47,2.64,2.56,2.70, 2.72,2.57))
node mean sd MC error 2.5% median 97.5% start sample
alpha 2.649 0.06845 0.001528 2.527 2.645 2.797 4001 50000
beta 0.9604 0.07883 9.338E-4 0.8136 0.9584 1.116 4001 50000
gamma 0.8661 0.03251 7.006E-4 0.7926 0.8707 0.9152 4001 50000
mu.x 2.096 0.2087 9.875E-4 1.689 2.094 2.512 4001 50000
sd.x 1.048 0.1722 0.001139 0.7735 1.027 1.444 4001 50000
sigma 0.0955 0.01605 1.407E-4 0.07019 0.09341 0.1325 4001 50000
x[4] 1.395 0.7006 0.004034 0.3363 1.299 3.035 4001 50000
x[9] 16.7 18.08 0.1805 6.831 12.77 54.61 4001 50000
x[19] 17.52 22.87 0.2601 6.821 12.88 59.72 4001 50000
x[25] 38.5 39.62 0.3918 12.3 28.05 127.9 4001 50000