model {
for(i in 1:N) {
y[i] ~ dnorm(mu[i], inv.sigma2)
mu[i] <- alpha - beta*pow(gamma, x[i])
y.pred[i] ~ dnorm(mu[i], inv.sigma2)
P.pred[i] <- step(y[i] - y.pred[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),
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),
N = 27)
node mean sd MC error 2.5% median 97.5% start sample
P.pred[1] 0.4499 0.4975 0.003081 0.0 0.0 1.0 1001 50000
P.pred[2] 0.4151 0.4927 0.002432 0.0 0.0 1.0 1001 50000
P.pred[3] 0.4887 0.4999 0.002739 0.0 0.0 1.0 1001 50000
P.pred[4] 0.1653 0.3714 0.00187 0.0 0.0 1.0 1001 50000
P.pred[5] 0.6573 0.4746 0.002341 0.0 1.0 1.0 1001 50000
P.pred[6] 0.937 0.2429 0.001193 0.0 1.0 1.0 1001 50000
P.pred[7] 0.3794 0.4852 0.002595 0.0 0.0 1.0 1001 50000
P.pred[8] 0.7783 0.4154 0.002521 0.0 1.0 1.0 1001 50000
P.pred[9] 0.9523 0.2132 0.001087 0.0 1.0 1.0 1001 50000
P.pred[10] 0.06888 0.2532 0.001249 0.0 0.0 1.0 1001 50000
P.pred[11] 0.151 0.358 0.00187 0.0 0.0 1.0 1001 50000
P.pred[12] 0.5701 0.4951 0.002482 0.0 1.0 1.0 1001 50000
P.pred[13] 0.4588 0.4983 0.002407 0.0 0.0 1.0 1001 50000
P.pred[14] 0.5358 0.4987 0.002658 0.0 1.0 1.0 1001 50000
P.pred[15] 0.8003 0.3998 0.001986 0.0 1.0 1.0 1001 50000
P.pred[16] 0.06532 0.2471 0.001193 0.0 0.0 1.0 1001 50000
P.pred[17] 0.06478 0.2461 0.001117 0.0 0.0 1.0 1001 50000
P.pred[18] 0.2531 0.4348 0.001892 0.0 0.0 1.0 1001 50000
P.pred[19] 0.3998 0.4899 0.002054 0.0 0.0 1.0 1001 50000
P.pred[20] 0.6431 0.4791 0.002174 0.0 1.0 1.0 1001 50000
P.pred[21] 0.8647 0.342 0.001588 0.0 1.0 1.0 1001 50000
P.pred[22] 0.242 0.4283 0.002056 0.0 0.0 1.0 1001 50000
P.pred[23] 0.8049 0.3963 0.001827 0.0 1.0 1.0 1001 50000
P.pred[24] 0.5037 0.5 0.002814 0.0 1.0 1.0 1001 50000
P.pred[25] 0.8127 0.3901 0.002707 0.0 1.0 1.0 1001 50000
P.pred[26] 0.7864 0.4099 0.003481 0.0 1.0 1.0 1001 50000
P.pred[27] 0.2727 0.4453 0.003858 0.0 0.0 1.0 1001 50000
alpha 2.653 0.07254 0.001689 2.532 2.647 2.809 1001 50000
beta 0.9738 0.07676 8.046E-4 0.8255 0.9718 1.128 1001 50000
gamma 0.8626 0.03237 6.79E-4 0.7885 0.8663 0.9145 1001 50000
sigma 0.09846 0.01484 9.315E-5 0.07464 0.09666 0.1325 1001 50000
y.pred[1] 1.813 0.1128 8.0E-4 1.591 1.814 2.035 1001 50000
y.pred[2] 1.873 0.1086 5.817E-4 1.658 1.873 2.087 1001 50000
y.pred[3] 1.873 0.1089 6.529E-4 1.654 1.873 2.085 1001 50000
y.pred[4] 1.873 0.1083 5.998E-4 1.658 1.874 2.086 1001 50000
y.pred[5] 1.979 0.1056 5.259E-4 1.771 1.979 2.19 1001 50000
y.pred[6] 2.109 0.1045 6.068E-4 1.905 2.109 2.318 1001 50000
y.pred[7] 2.182 0.1046 6.546E-4 1.98 2.181 2.392 1001 50000
y.pred[8] 2.182 0.1041 6.673E-4 1.977 2.181 2.389 1001 50000
y.pred[9] 2.297 0.104 6.949E-4 2.093 2.297 2.504 1001 50000
y.pred[10] 2.343 0.1038 6.566E-4 2.138 2.342 2.547 1001 50000
y.pred[11] 2.364 0.1034 6.262E-4 2.161 2.364 2.571 1001 50000
y.pred[12] 2.382 0.1035 6.023E-4 2.179 2.382 2.586 1001 50000
y.pred[13] 2.401 0.103 5.403E-4 2.198 2.4 2.605 1001 50000
y.pred[14] 2.401 0.1035 5.937E-4 2.198 2.401 2.607 1001 50000
y.pred[15] 2.417 0.1027 5.381E-4 2.213 2.417 2.62 1001 50000
y.pred[16] 2.473 0.1022 4.908E-4 2.271 2.472 2.676 1001 50000
y.pred[17] 2.474 0.1024 4.866E-4 2.273 2.475 2.675 1001 50000
y.pred[18] 2.496 0.1021 4.489E-4 2.294 2.496 2.697 1001 50000
y.pred[19] 2.495 0.102 4.35E-4 2.294 2.495 2.696 1001 50000
y.pred[20] 2.524 0.1028 4.818E-4 2.321 2.524 2.727 1001 50000
y.pred[21] 2.539 0.1027 5.595E-4 2.336 2.539 2.741 1001 50000
y.pred[22] 2.54 0.1025 5.394E-4 2.336 2.54 2.742 1001 50000
y.pred[23] 2.553 0.1032 5.23E-4 2.35 2.554 2.756 1001 50000
y.pred[24] 2.559 0.1036 6.13E-4 2.353 2.559 2.764 1001 50000
y.pred[25] 2.606 0.1078 9.769E-4 2.394 2.606 2.817 1001 50000
y.pred[26] 2.632 0.1134 0.001207 2.407 2.632 2.857 1001 50000
y.pred[27] 2.637 0.1146 0.001356 2.412 2.638 2.864 1001 50000