model {
for(j in 1:n) {
y[j] ~ dnorm(mu[j], tau)
mu[j] <- alpha - beta*pow(gamma, z[j])
x[j] ~ dnorm(z[j], 1)
z[j] ~ dunif(0, 100)
}
alpha ~ dunif(0, 100)
beta ~ dunif(0, 100)
gamma ~ dunif(0, 1)
tau <- 1/sigma2
log(sigma2) <- 2*log.sigma
log.sigma ~ dunif(-10, 10)
for (j in 1:n) {resx[j] <- x[j] - z[j]}
}
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
alpha 2.677 0.06941 0.001649 2.561 2.671 2.829 1001 50000
beta 0.9783 0.08888 0.00124 0.8162 0.9745 1.166 1001 50000
gamma 0.875 0.02599 5.318E-4 0.8157 0.8776 0.9195 1001 50000
mu[1] 1.827 0.0699 4.962E-4 1.688 1.827 1.964 1001 50000
mu[2] 1.87 0.07043 4.15E-4 1.729 1.87 2.008 1001 50000
mu[3] 1.881 0.07 4.048E-4 1.742 1.882 2.017 1001 50000
mu[4] 1.829 0.073 5.335E-4 1.686 1.828 1.973 1001 50000
mu[5] 2.001 0.06468 3.245E-4 1.869 2.003 2.123 1001 50000
mu[6] 2.165 0.05607 3.615E-4 2.05 2.167 2.269 1001 50000
mu[7] 2.164 0.05711 3.384E-4 2.048 2.165 2.273 1001 50000
mu[8] 2.202 0.05406 3.525E-4 2.091 2.203 2.303 1001 50000
mu[9] 2.325 0.04587 3.479E-4 2.23 2.327 2.409 1001 50000
mu[10] 2.3 0.05029 3.9E-4 2.198 2.3 2.395 1001 50000
mu[11] 2.335 0.04633 3.685E-4 2.242 2.335 2.423 1001 50000
mu[12] 2.377 0.04158 3.262E-4 2.292 2.379 2.455 1001 50000
mu[13] 2.391 0.04076 3.12E-4 2.307 2.393 2.467 1001 50000
mu[14] 2.394 0.03973 3.003E-4 2.312 2.395 2.468 1001 50000
mu[15] 2.42 0.03759 2.671E-4 2.342 2.422 2.49 1001 50000
mu[16] 2.454 0.03571 2.326E-4 2.381 2.455 2.521 1001 50000
mu[17] 2.454 0.03541 2.435E-4 2.382 2.455 2.521 1001 50000
mu[18] 2.488 0.03163 1.907E-4 2.423 2.488 2.548 1001 50000
mu[19] 2.49 0.03098 1.807E-4 2.427 2.491 2.55 1001 50000
mu[20] 2.525 0.02903 2.034E-4 2.467 2.525 2.581 1001 50000
mu[21] 2.545 0.0287 2.668E-4 2.488 2.545 2.601 1001 50000
mu[22] 2.538 0.0285 2.346E-4 2.481 2.538 2.594 1001 50000
mu[23] 2.559 0.02889 3.303E-4 2.502 2.559 2.615 1001 50000
mu[24] 2.563 0.02887 3.369E-4 2.506 2.563 2.62 1001 50000
mu[25] 2.618 0.03747 7.102E-4 2.544 2.619 2.692 1001 50000
mu[26] 2.649 0.04855 0.001035 2.556 2.648 2.747 1001 50000
mu[27] 2.656 0.05181 0.001125 2.558 2.655 2.761 1001 50000
resx[1] -0.03285 0.5917 0.003768 -1.293 0.01502 0.9141 1001 50000
resx[2] 0.06939 0.6658 0.004321 -1.304 0.09208 1.275 1001 50000
resx[3] -0.03885 0.6792 0.00433 -1.426 -0.02011 1.221 1001 50000
resx[4] 0.4418 0.6083 0.003986 -0.8752 0.4953 1.412 1001 50000
resx[5] -0.2942 0.7379 0.003878 -1.763 -0.29 1.132 1001 50000
resx[6] -0.943 0.8117 0.004696 -2.534 -0.9415 0.6376 1001 50000
resx[7] 0.06991 0.8183 0.004193 -1.575 0.08112 1.636 1001 50000
resx[8] -0.5239 0.827 0.004366 -2.143 -0.5204 1.087 1001 50000
resx[9] -0.8755 0.8988 0.00526 -2.645 -0.8719 0.8852 1001 50000
resx[10] 0.6626 0.9254 0.004734 -1.191 0.6792 2.424 1001 50000
resx[11] 0.4006 0.923 0.004227 -1.446 0.4199 2.158 1001 50000
resx[12] -0.1408 0.9185 0.004512 -1.944 -0.1356 1.653 1001 50000
resx[13] -0.01732 0.9357 0.004296 -1.885 -0.006942 1.796 1001 50000
resx[14] -0.09688 0.9172 0.00402 -1.916 -0.0825 1.673 1001 50000
resx[15] -0.3707 0.9305 0.004791 -2.208 -0.366 1.445 1001 50000
resx[16] 0.4875 0.9968 0.004519 -1.484 0.4937 2.42 1001 50000
resx[17] 0.4813 0.9922 0.004708 -1.475 0.4923 2.415 1001 50000
resx[18] 0.1646 0.9796 0.004655 -1.767 0.1719 2.086 1001 50000
resx[19] 0.05686 0.9595 0.00397 -1.831 0.06347 1.924 1001 50000
resx[20] -0.09315 0.9783 0.004608 -2.034 -0.0913 1.817 1001 50000
resx[21] -0.2378 0.9778 0.004468 -2.148 -0.2331 1.679 1001 50000
resx[22] 0.1552 0.9976 0.004687 -1.824 0.1618 2.098 1001 50000
resx[23] -0.1577 0.9807 0.004556 -2.081 -0.1529 1.748 1001 50000
resx[24] 0.0118 0.9828 0.00429 -1.931 0.01596 1.94 1001 50000
resx[25] -0.06687 0.9951 0.004305 -2.011 -0.06867 1.891 1001 50000
resx[26] -0.02521 0.996 0.004398 -1.975 -0.02161 1.929 1001 50000
resx[27] 0.02966 0.9988 0.004687 -1.921 0.02928 1.989 1001 50000
sigma2 0.008374 0.003165 2.783E-5 0.004036 0.007777 0.01622 1001 50000
z[1] 1.033 0.5917 0.003768 0.08602 0.985 2.293 1001 50000
z[2] 1.431 0.6658 0.004321 0.225 1.408 2.805 1001 50000
z[3] 1.539 0.6792 0.00433 0.2792 1.52 2.926 1001 50000
z[4] 1.058 0.6083 0.003986 0.08852 1.005 2.375 1001 50000
z[5] 2.794 0.7379 0.003878 1.368 2.79 4.263 1001 50000
z[6] 4.943 0.8117 0.004696 3.363 4.942 6.534 1001 50000
z[7] 4.93 0.8183 0.004193 3.364 4.919 6.575 1001 50000
z[8] 5.524 0.827 0.004366 3.913 5.52 7.143 1001 50000
z[9] 7.875 0.8988 0.00526 6.116 7.872 9.645 1001 50000
z[10] 7.337 0.9254 0.004734 5.577 7.321 9.191 1001 50000
z[11] 8.099 0.923 0.004227 6.342 8.08 9.946 1001 50000
z[12] 9.141 0.9185 0.004512 7.347 9.136 10.94 1001 50000
z[13] 9.517 0.9357 0.004296 7.705 9.507 11.39 1001 50000
z[14] 9.597 0.9172 0.00402 7.828 9.583 11.42 1001 50000
z[15] 10.37 0.9305 0.004791 8.555 10.37 12.21 1001 50000
z[16] 11.51 0.9968 0.004519 9.58 11.51 13.48 1001 50000
z[17] 11.52 0.9922 0.004708 9.585 11.51 13.47 1001 50000
z[18] 12.84 0.9796 0.004655 10.91 12.83 14.77 1001 50000
z[19] 12.94 0.9595 0.00397 11.08 12.94 14.83 1001 50000
z[20] 14.59 0.9783 0.004608 12.68 14.59 16.53 1001 50000
z[21] 15.74 0.9778 0.004468 13.82 15.73 17.65 1001 50000
z[22] 15.34 0.9976 0.004687 13.4 15.34 17.32 1001 50000
z[23] 16.66 0.9807 0.004556 14.75 16.65 18.58 1001 50000
z[24] 16.99 0.9828 0.00429 15.06 16.98 18.93 1001 50000
z[25] 22.57 0.9951 0.004305 20.61 22.57 24.51 1001 50000
z[26] 29.03 0.996 0.004398 27.07 29.02 30.98 1001 50000
z[27] 31.47 0.9988 0.004687 29.51 31.47 33.42 1001 50000