model {
for(i in 1:N) {
y[i] ~ dnorm(mu[i], inv.sigma2)
mu[i] <- alpha - beta*pow(gamma, x[i])
}
alpha ~ dunif(0, 100)
beta ~ dunif(0, 100)
gamma ~ dunif(0, 1)
inv.sigma2 <- 1/pow(sigma, 2)
log(sigma) <- log.sigma
log.sigma ~ dunif(-10, 10)
}
Inits:
list(alpha = 3, beta = 2, gamma = 0.9, log.sigma = -5)
Data:
list(x = c(1.0, 1.5, 1.5, 1.5, 2.5, 4.0, 5.0, 5.0, 7.0,
8.0, 8.5, 9.0, 9.5, 9.5, 10.0, 12.0, 12.0, 13.0,
13.0, 14.5, 15.5, 15.5, 16.5, 17.0, 22.5, 29.0, 31.5, 35, 40),
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, NA, NA),
N = 29)
node mean sd MC error 2.5% median 97.5% start sample
alpha 2.647 0.06908 0.003293 2.528 2.641 2.798 1001 10000
beta 0.9706 0.07514 0.001881 0.8241 0.9699 1.119 1001 10000
gamma 0.8605 0.03191 0.00137 0.788 0.8642 0.913 1001 10000
mu[1] 1.812 0.0527 0.001215 1.706 1.813 1.911 1001 10000
mu[2] 1.872 0.04352 8.199E-4 1.786 1.872 1.956 1001 10000
mu[3] 1.872 0.04352 8.199E-4 1.786 1.872 1.956 1001 10000
mu[4] 1.872 0.04352 8.199E-4 1.786 1.872 1.956 1001 10000
mu[5] 1.979 0.03358 5.403E-4 1.913 1.978 2.046 1001 10000
mu[6] 2.111 0.03153 8.477E-4 2.053 2.11 2.177 1001 10000
mu[7] 2.183 0.03232 9.835E-4 2.126 2.181 2.253 1001 10000
mu[8] 2.183 0.03232 9.835E-4 2.126 2.181 2.253 1001 10000
mu[9] 2.299 0.0319 9.772E-4 2.241 2.297 2.366 1001 10000
mu[10] 2.345 0.03052 8.782E-4 2.287 2.344 2.407 1001 10000
mu[11] 2.365 0.02965 8.139E-4 2.309 2.364 2.425 1001 10000
mu[12] 2.384 0.02873 7.427E-4 2.329 2.384 2.441 1001 10000
mu[13] 2.402 0.02778 6.672E-4 2.348 2.402 2.457 1001 10000
mu[14] 2.402 0.02778 6.672E-4 2.348 2.402 2.457 1001 10000
mu[15] 2.418 0.02685 5.898E-4 2.366 2.418 2.471 1001 10000
mu[16] 2.473 0.02407 3.455E-4 2.426 2.474 2.52 1001 10000
mu[17] 2.473 0.02407 3.455E-4 2.426 2.474 2.52 1001 10000
mu[18] 2.496 0.02364 3.557E-4 2.449 2.496 2.541 1001 10000
mu[19] 2.496 0.02364 3.557E-4 2.449 2.496 2.541 1001 10000
mu[20] 2.523 0.02443 5.438E-4 2.474 2.524 2.571 1001 10000
mu[21] 2.539 0.02579 7.108E-4 2.486 2.539 2.589 1001 10000
mu[22] 2.539 0.02579 7.108E-4 2.486 2.539 2.589 1001 10000
mu[23] 2.552 0.02762 8.836E-4 2.496 2.553 2.605 1001 10000
mu[24] 2.558 0.02866 9.694E-4 2.5 2.559 2.613 1001 10000
mu[25] 2.604 0.04133 0.00179 2.52 2.604 2.684 1001 10000
mu[26] 2.628 0.0529 0.002428 2.526 2.627 2.735 1001 10000
mu[27] 2.633 0.05605 0.002596 2.527 2.631 2.748 1001 10000
mu[28] 2.638 0.05949 0.002778 2.528 2.635 2.762 1001 10000
mu[29] 2.642 0.06291 0.002959 2.528 2.638 2.775 1001 10000
y[28] 2.637 0.1153 0.002654 2.408 2.638 2.865 1001 10000
y[29] 2.642 0.1179 0.003107 2.413 2.64 2.881 1001 10000