model {
for (i in 1:20) {Y[i, 1:4] ~ dmnorm(mu[], Sigma.inv[,])}
for (j in 1:4) {mu[j] <- alpha + beta*x[j]}
alpha ~ dnorm(0, 0.0001)
beta ~ dnorm(0, 0.0001)
Sigma.inv[1:4, 1:4] ~ dwish(R[,], 4)
Sigma[1:4, 1:4] <- inverse(Sigma.inv[,])
for (i in 1:20) {
for (j in 1:4) {
res[i, j] <- Y[i, j] - mu[j]
temp[i, j] <- inprod(Sigma.inv[j, 1:4], res[i, 1:4])
}
M.squared[i] <- inprod(res[i, 1:4], temp[i, 1:4])
M[i] <- sqrt(M.squared[i])
}
}
Inits:
list(alpha = 40, beta = 1)
Data:
list(Y = structure(
.Data = c(47.8, 48.8, 49.0, 49.7,
46.4, 47.3, 47.7, 48.4,
46.3, 46.8, 47.8, 48.5,
45.1, 45.3, 46.1, 47.2,
47.6, 48.5, 48.9, 49.3,
52.5, 53.2, 53.3, 53.7,
51.2, 53.0, 54.3, 54.5,
49.8, 50.0, 50.3, 52.7,
48.1, 50.8, 52.3, 54.4,
45.0, 47.0, 47.3, 48.3,
51.2, 51.4, 51.6, 51.9,
48.5, 49.2, 53.0, 55.5,
52.1, 52.8, 53.7, 55.0,
48.2, 48.9, 49.3, 49.8,
49.6, 50.4, 51.2, 51.8,
50.7, 51.7, 52.7, 53.3,
47.2, 47.7, 48.4, 49.5,
53.3, 54.6, 55.1, 55.3,
46.2, 47.5, 48.1, 48.4,
46.3, 47.6, 51.3, 51.8),
.Dim = c(20, 4)),
x = c(8.0, 8.5, 9.0, 9.5),
R = structure(
.Data = c(4, 0, 0, 0,
0, 4, 0, 0,
0, 0, 4, 0,
0, 0, 0, 4),
.Dim = c(4, 4)))
node mean sd MC error 2.5% median 97.5% start sample
M[1] 0.9006 0.2042 0.003727 0.5496 0.8879 1.339 1001 20000
M[2] 1.244 0.2774 0.00285 0.7163 1.239 1.808 1001 20000
M[3] 1.304 0.2855 0.003097 0.7603 1.297 1.876 1001 20000
M[4] 1.964 0.3581 0.003068 1.277 1.956 2.689 1001 20000
M[5] 1.004 0.2353 0.004763 0.5713 0.9973 1.494 1001 20000
M[6] 1.794 0.3361 0.005918 1.156 1.792 2.462 1001 20000
M[7] 1.922 0.3085 0.002256 1.35 1.91 2.562 1001 20000
M[8] 2.126 0.315 0.002612 1.533 2.12 2.767 1001 20000
M[9] 2.614 0.4389 0.008536 1.785 2.611 3.493 1001 20000
M[10] 2.033 0.3371 0.003482 1.408 2.021 2.723 1001 20000
M[11] 1.578 0.32 0.008209 0.9595 1.575 2.215 1001 20000
M[12] 3.139 0.486 0.006977 2.235 3.129 4.129 1001 20000
M[13] 1.593 0.3147 0.002348 1.004 1.586 2.223 1001 20000
M[14] 0.8656 0.2412 0.006638 0.4093 0.8598 1.351 1001 20000
M[15] 0.598 0.2061 0.006055 0.2356 0.5885 1.025 1001 20000
M[16] 0.9955 0.2381 0.002775 0.5536 0.9892 1.482 1001 20000
M[17] 1.005 0.2351 0.002945 0.5872 0.9932 1.502 1001 20000
M[18] 2.225 0.3745 0.003656 1.519 2.22 2.98 1001 20000
M[19] 1.449 0.2668 0.002044 0.9618 1.437 1.993 1001 20000
M[20] 2.756 0.4142 0.00507 1.986 2.743 3.607 1001 20000
M.squared[1] 0.8529 0.3891 0.006984 0.302 0.7883 1.793 1001 20000
M.squared[2] 1.625 0.7074 0.00714 0.5131 1.534 3.268 1001 20000
M.squared[3] 1.781 0.7617 0.00811 0.5781 1.683 3.518 1001 20000
M.squared[4] 3.986 1.435 0.01215 1.631 3.827 7.233 1001 20000
M.squared[5] 1.064 0.4921 0.009701 0.3264 0.9947 2.233 1001 20000
M.squared[6] 3.33 1.225 0.02095 1.337 3.212 6.061 1001 20000
M.squared[7] 3.788 1.213 0.008815 1.822 3.649 6.565 1001 20000
M.squared[8] 4.618 1.363 0.01132 2.349 4.495 7.656 1001 20000
M.squared[9] 7.028 2.333 0.0441 3.186 6.815 12.2 1001 20000
M.squared[10] 4.248 1.405 0.0143 1.982 4.083 7.414 1001 20000
M.squared[11] 2.591 1.026 0.02548 0.9207 2.48 4.908 1001 20000
M.squared[12] 10.09 3.114 0.0443 4.993 9.79 17.04 1001 20000
M.squared[13] 2.637 1.026 0.007626 1.008 2.515 4.943 1001 20000
M.squared[14] 0.8075 0.4338 0.01153 0.1676 0.7393 1.825 1001 20000
M.squared[15] 0.4001 0.266 0.007637 0.05551 0.3463 1.051 1001 20000
M.squared[16] 1.048 0.4915 0.005652 0.3065 0.9784 2.197 1001 20000
M.squared[17] 1.066 0.4957 0.00613 0.3448 0.9865 2.257 1001 20000
M.squared[18] 5.092 1.698 0.01636 2.307 4.927 8.88 1001 20000
M.squared[19] 2.17 0.797 0.006078 0.925 2.066 3.973 1001 20000
M.squared[20] 7.767 2.33 0.02873 3.944 7.526 13.01 1001 20000
res[1,1] -0.8668 0.5575 0.008297 -1.966 -0.8678 0.2458 1001 20000
res[1,2] -0.8011 0.5435 0.004251 -1.868 -0.8043 0.2858 1001 20000
res[1,3] -1.535 0.5583 0.004212 -2.624 -1.537 -0.4202 1001 20000
res[1,4] -1.77 0.5997 0.008236 -2.944 -1.773 -0.5774 1001 20000
res[2,1] -2.267 0.5575 0.008297 -3.366 -2.268 -1.154 1001 20000
res[2,2] -2.301 0.5435 0.004251 -3.368 -2.304 -1.214 1001 20000
res[2,3] -2.835 0.5583 0.004212 -3.924 -2.837 -1.72 1001 20000
res[2,4] -3.07 0.5997 0.008236 -4.244 -3.073 -1.877 1001 20000
res[3,1] -2.367 0.5575 0.008297 -3.466 -2.368 -1.254 1001 20000
res[3,2] -2.801 0.5435 0.004251 -3.868 -2.804 -1.714 1001 20000
res[3,3] -2.735 0.5583 0.004212 -3.824 -2.737 -1.62 1001 20000
res[3,4] -2.97 0.5997 0.008236 -4.144 -2.973 -1.777 1001 20000
res[4,1] -3.567 0.5575 0.008297 -4.666 -3.568 -2.454 1001 20000
res[4,2] -4.301 0.5435 0.004251 -5.368 -4.304 -3.214 1001 20000
res[4,3] -4.435 0.5583 0.004212 -5.524 -4.437 -3.32 1001 20000
res[4,4] -4.27 0.5997 0.008236 -5.444 -4.273 -3.077 1001 20000
res[5,1] -1.067 0.5575 0.008297 -2.166 -1.068 0.04577 1001 20000
res[5,2] -1.101 0.5435 0.004251 -2.168 -1.104 -0.01421 1001 20000
res[5,3] -1.635 0.5583 0.004212 -2.724 -1.637 -0.5202 1001 20000
res[5,4] -2.17 0.5997 0.008236 -3.344 -2.173 -0.9774 1001 20000
res[6,1] 3.833 0.5575 0.008297 2.734 3.832 4.946 1001 20000
res[6,2] 3.599 0.5435 0.004251 2.532 3.596 4.686 1001 20000
res[6,3] 2.765 0.5583 0.004212 1.676 2.763 3.88 1001 20000
res[6,4] 2.23 0.5997 0.008236 1.056 2.227 3.423 1001 20000
res[7,1] 2.533 0.5575 0.008297 1.434 2.532 3.646 1001 20000
res[7,2] 3.399 0.5435 0.004251 2.332 3.396 4.486 1001 20000
res[7,3] 3.765 0.5583 0.004212 2.676 3.763 4.88 1001 20000
res[7,4] 3.03 0.5997 0.008236 1.856 3.027 4.223 1001 20000
res[8,1] 1.133 0.5575 0.008297 0.03366 1.132 2.246 1001 20000
res[8,2] 0.3989 0.5435 0.004251 -0.6677 0.3957 1.486 1001 20000
res[8,3] -0.2354 0.5583 0.004212 -1.324 -0.2371 0.8798 1001 20000
res[8,4] 1.23 0.5997 0.008236 0.05627 1.227 2.423 1001 20000
res[9,1] -0.5668 0.5575 0.008297 -1.666 -0.5678 0.5458 1001 20000
res[9,2] 1.199 0.5435 0.004251 0.1323 1.196 2.286 1001 20000
res[9,3] 1.765 0.5583 0.004212 0.6765 1.763 2.88 1001 20000
res[9,4] 2.93 0.5997 0.008236 1.756 2.927 4.123 1001 20000
res[10,1] -3.667 0.5575 0.008297 -4.766 -3.668 -2.554 1001 20000
res[10,2] -2.601 0.5435 0.004251 -3.668 -2.604 -1.514 1001 20000
res[10,3] -3.235 0.5583 0.004212 -4.324 -3.237 -2.12 1001 20000
res[10,4] -3.17 0.5997 0.008236 -4.344 -3.173 -1.977 1001 20000
res[11,1] 2.533 0.5575 0.008297 1.434 2.532 3.646 1001 20000
res[11,2] 1.799 0.5435 0.004251 0.7323 1.796 2.886 1001 20000
res[11,3] 1.065 0.5583 0.004212 -0.02354 1.063 2.18 1001 20000
res[11,4] 0.4303 0.5997 0.008236 -0.7437 0.4272 1.623 1001 20000
res[12,1] -0.1668 0.5575 0.008297 -1.266 -0.1678 0.9458 1001 20000
res[12,2] -0.4011 0.5435 0.004251 -1.468 -0.4043 0.6858 1001 20000
res[12,3] 2.465 0.5583 0.004212 1.376 2.463 3.58 1001 20000
res[12,4] 4.03 0.5997 0.008236 2.856 4.027 5.223 1001 20000
res[13,1] 3.433 0.5575 0.008297 2.334 3.432 4.546 1001 20000
res[13,2] 3.199 0.5435 0.004251 2.132 3.196 4.286 1001 20000
res[13,3] 3.165 0.5583 0.004212 2.076 3.163 4.28 1001 20000
res[13,4] 3.53 0.5997 0.008236 2.356 3.527 4.723 1001 20000
res[14,1] -0.4668 0.5575 0.008297 -1.566 -0.4678 0.6458 1001 20000
res[14,2] -0.7011 0.5435 0.004251 -1.768 -0.7043 0.3858 1001 20000
res[14,3] -1.235 0.5583 0.004212 -2.324 -1.237 -0.1202 1001 20000
res[14,4] -1.67 0.5997 0.008236 -2.844 -1.673 -0.4774 1001 20000
res[15,1] 0.9332 0.5575 0.008297 -0.1663 0.9322 2.046 1001 20000
res[15,2] 0.7989 0.5435 0.004251 -0.2677 0.7957 1.886 1001 20000
res[15,3] 0.6646 0.5583 0.004212 -0.4235 0.6629 1.78 1001 20000
res[15,4] 0.3303 0.5997 0.008236 -0.8437 0.3272 1.523 1001 20000
res[16,1] 2.033 0.5575 0.008297 0.9337 2.032 3.146 1001 20000
res[16,2] 2.099 0.5435 0.004251 1.032 2.096 3.186 1001 20000
res[16,3] 2.165 0.5583 0.004212 1.076 2.163 3.28 1001 20000
res[16,4] 1.83 0.5997 0.008236 0.6563 1.827 3.023 1001 20000
res[17,1] -1.467 0.5575 0.008297 -2.566 -1.468 -0.3542 1001 20000
res[17,2] -1.901 0.5435 0.004251 -2.968 -1.904 -0.8142 1001 20000
res[17,3] -2.135 0.5583 0.004212 -3.224 -2.137 -1.02 1001 20000
res[17,4] -1.97 0.5997 0.008236 -3.144 -1.973 -0.7774 1001 20000
res[18,1] 4.633 0.5575 0.008297 3.534 4.632 5.746 1001 20000
res[18,2] 4.999 0.5435 0.004251 3.932 4.996 6.086 1001 20000
res[18,3] 4.565 0.5583 0.004212 3.476 4.563 5.68 1001 20000
res[18,4] 3.83 0.5997 0.008236 2.656 3.827 5.023 1001 20000
res[19,1] -2.467 0.5575 0.008297 -3.566 -2.468 -1.354 1001 20000
res[19,2] -2.101 0.5435 0.004251 -3.168 -2.104 -1.014 1001 20000
res[19,3] -2.435 0.5583 0.004212 -3.524 -2.437 -1.32 1001 20000
res[19,4] -3.07 0.5997 0.008236 -4.244 -3.073 -1.877 1001 20000
res[20,1] -2.367 0.5575 0.008297 -3.466 -2.368 -1.254 1001 20000
res[20,2] -2.001 0.5435 0.004251 -3.068 -2.004 -0.9142 1001 20000
res[20,3] 0.7646 0.5583 0.004212 -0.3235 0.7629 1.88 1001 20000
res[20,4] 0.3303 0.5997 0.008236 -0.8437 0.3272 1.523 1001 20000