MRC Biostatistics Unit 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[,])
}
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 Sigma[1,1] 6.861 2.404 0.01689 3.626 6.393 12.85 1001 20000 Sigma[1,2] 6.497 2.356 0.01643 3.319 6.045 12.37 1001 20000 Sigma[1,3] 6.071 2.329 0.01634 2.929 5.614 11.89 1001 20000 Sigma[1,4] 5.833 2.343 0.01607 2.632 5.379 11.62 1001 20000 Sigma[2,1] 6.497 2.356 0.01643 3.319 6.045 12.37 1001 20000 Sigma[2,2] 6.973 2.456 0.01719 3.688 6.491 13.11 1001 20000 Sigma[2,3] 6.47 2.414 0.01734 3.207 5.985 12.45 1001 20000 Sigma[2,4] 6.247 2.43 0.01711 2.943 5.771 12.34 1001 20000 Sigma[3,1] 6.071 2.329 0.01634 2.929 5.614 11.89 1001 20000 Sigma[3,2] 6.47 2.414 0.01734 3.207 5.985 12.45 1001 20000 Sigma[3,3] 7.47 2.628 0.01968 3.931 6.95 14.08 1001 20000 Sigma[3,4] 7.308 2.653 0.01951 3.716 6.781 13.97 1001 20000 Sigma[4,1] 5.833 2.343 0.01607 2.632 5.379 11.62 1001 20000 Sigma[4,2] 6.247 2.43 0.01711 2.943 5.771 12.34 1001 20000 Sigma[4,3] 7.308 2.653 0.01951 3.716 6.781 13.97 1001 20000 Sigma[4,4] 8.079 2.839 0.02058 4.221 7.527 15.23 1001 20000 alpha 33.72 2.204 0.08798 29.41 33.71 38.02 1001 20000 beta 1.869 0.2515 0.01004 1.379 1.868 2.362 1001 20000 mu[1] 48.67 0.5575 0.008297 47.55 48.67 49.77 1001 20000 mu[2] 49.6 0.5435 0.004251 48.51 49.6 50.67 1001 20000 mu[3] 50.54 0.5583 0.004212 49.42 50.54 51.62 1001 20000 mu[4] 51.47 0.5997 0.008236 50.28 51.47 52.64 1001 20000