model {
for (m in 1:3) {
for (i in 1:n) {
y.copy[m, i] <- y[i]
x.copy[m, i] <- x[i]
y.copy[m, i] ~ dnorm(mu[m, i], tau[m])
mu[m, i] <- a[m] + b[m]*z[m, i]
}
a[m] ~ dnorm(0, 0.0001)
b[m] ~ dnorm(0, 0.0001)
tau[m] <- 1/pow(sigma[m], 2)
sigma[m] ~ dunif(0, 100)
}
for (i in 1:n) {
z[1, i] <- x.copy[1, i]
x.copy[2, i] ~ dnorm(z[2, i], 0.4444)
z[2, i] ~ dunif(-100, 100)
z[3, i] <- cut(z.star[i])
x.copy[3, i] ~ dnorm(z.star[i], 0.4444)
z.star[i] ~ dunif(-100, 100)
}
}
Inits:
list(a = c(0, 0, 0), b = c(0, 0, 0), sigma = c(1, 1, 1))
Data:
list(n = 10,
x = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
y = c(1, 2, 3, 6, 3, 8, 5, 8, 9, 10)
)
node mean sd MC error 2.5% median 97.5% start sample
a[1] 0.2374 1.241 0.01048 -2.24 0.2262 2.726 10001 10000
a[2] 0.3745 1.206 0.03485 -2.121 0.4184 2.654 10001 10000
a[3] 1.148 2.532 0.02387 -2.931 1.208 4.737 10001 10000
b[1] 0.9555 0.2 0.001745 0.5589 0.9566 1.349 10001 10000
b[2] 0.9279 0.1865 0.005205 0.5713 0.9221 1.299 10001 10000
b[3] 0.7906 0.3934 0.003913 0.2255 0.7776 1.435 10001 10000
mu[1,1] 1.193 1.068 0.009095 -0.9557 1.184 3.319 10001 10000
mu[1,2] 2.148 0.9045 0.007859 0.3234 2.145 3.954 10001 10000
mu[1,3] 3.104 0.7593 0.00685 1.568 3.105 4.602 10001 10000
mu[1,4] 4.059 0.6442 0.006177 2.75 4.064 5.339 10001 10000
mu[1,5] 5.015 0.5775 0.005956 3.831 5.02 6.165 10001 10000
mu[1,6] 5.97 0.5763 0.006236 4.81 5.977 7.118 10001 10000
mu[1,7] 6.926 0.6409 0.006956 5.633 6.925 8.215 10001 10000
mu[1,8] 7.881 0.7546 0.007998 6.366 7.875 9.407 10001 10000
mu[1,9] 8.837 0.8989 0.009255 7.035 8.833 10.64 10001 10000
mu[1,10] 9.792 1.061 0.01065 7.673 9.788 11.93 10001 10000
mu[2,1] 1.186 0.9331 0.01355 -0.5542 1.074 3.369 10001 10000
mu[2,2] 2.161 0.8787 0.01241 0.4338 2.072 4.173 10001 10000
mu[2,3] 3.107 0.8295 0.009603 1.399 3.052 4.955 10001 10000
mu[2,4] 5.454 0.9059 0.02316 3.205 5.672 6.846 10001 10000
mu[2,5] 3.652 0.9592 0.02828 2.281 3.4 6.021 10001 10000
mu[2,6] 7.367 0.9662 0.02708 5.009 7.631 8.786 10001 10000
mu[2,7] 5.541 0.9072 0.0225 4.137 5.328 7.774 10001 10000
mu[2,8] 7.898 0.841 0.009888 6.002 7.955 9.606 10001 10000
mu[2,9] 8.859 0.866 0.01021 6.929 8.938 10.57 10001 10000
mu[2,10] 9.806 0.9153 0.01324 7.638 9.924 11.54 10001 10000
mu[3,1] 1.936 2.752 0.02437 -2.723 2.114 5.46 10001 10000
mu[3,2] 2.721 2.19 0.02024 -1.605 2.902 6.002 10001 10000
mu[3,3] 3.523 1.951 0.02001 -0.4597 3.665 6.611 10001 10000
mu[3,4] 4.305 1.769 0.01504 0.8424 4.402 7.395 10001 10000
mu[3,5] 5.111 1.716 0.01539 1.848 5.133 8.236 10001 10000
mu[3,6] 5.885 1.707 0.01515 2.849 5.849 9.211 10001 10000
mu[3,7] 6.679 1.714 0.01439 3.624 6.599 10.21 10001 10000
mu[3,8] 7.483 1.963 0.01869 4.361 7.341 11.47 10001 10000
mu[3,9] 8.278 2.092 0.01876 4.952 8.115 12.56 10001 10000
mu[3,10] 9.049 2.323 0.02366 5.491 8.886 13.7 10001 10000
sigma[1] 1.698 0.5509 0.0096 0.9975 1.585 3.069 10001 10000
sigma[2] 0.943 0.7207 0.0323 0.06208 0.7882 2.72 10001 10000
sigma[3] 2.533 2.147 0.0465 1.154 2.229 5.266 10001 10000
tau[1] 0.4391 0.2339 0.003203 0.1063 0.3981 1.005 10001 10000
tau[2] 32.69 350.8 9.981 0.1357 1.61 260.6 10001 10000
tau[3] 0.2503 0.1998 0.002557 0.03625 0.2014 0.7516 10001 10000
z[2,1] 0.7801 1.077 0.02566 -1.429 0.8151 2.803 10001 10000
z[2,2] 1.85 1.034 0.02413 -0.2893 1.876 3.854 10001 10000
z[2,3] 2.887 0.9741 0.02195 0.8444 2.902 4.81 10001 10000
z[2,4] 5.432 1.07 0.03269 2.852 5.593 7.099 10001 10000
z[2,5] 3.523 1.072 0.03073 1.702 3.401 5.978 10001 10000
z[2,6] 7.535 1.086 0.03262 5.03 7.661 9.354 10001 10000
z[2,7] 5.601 1.051 0.02907 3.887 5.468 8.107 10001 10000
z[2,8] 8.153 0.9645 0.02123 6.218 8.14 10.16 10001 10000
z[2,9] 9.211 0.9968 0.02264 7.252 9.197 11.25 10001 10000
z[2,10] 10.25 1.048 0.02485 8.222 10.24 12.36 10001 10000
z[3,1] 0.9933 1.509 0.01505 -1.968 1.002 3.971 10001 10000
z[3,2] 2.002 1.505 0.01599 -0.9598 2.009 4.945 10001 10000
z[3,3] 3.007 1.482 0.01547 0.05272 3.021 5.915 10001 10000
z[3,4] 4.002 1.485 0.01381 1.094 3.997 6.904 10001 10000
z[3,5] 5.018 1.498 0.01472 2.072 4.997 8.019 10001 10000
z[3,6] 6.001 1.507 0.01373 3.007 6.014 8.924 10001 10000
z[3,7] 7.001 1.505 0.01466 4.042 7.003 9.975 10001 10000
z[3,8] 8.0 1.5 0.01457 5.091 7.993 10.94 10001 10000
z[3,9] 9.02 1.527 0.01373 6.031 9.021 11.97 10001 10000
z[3,10] 9.997 1.508 0.01582 7.087 9.988 12.9 10001 10000
z.star[1] 0.9933 1.509 0.01505 -1.968 1.002 3.971 10001 10000
z.star[2] 2.002 1.505 0.01599 -0.9598 2.009 4.945 10001 10000
z.star[3] 3.007 1.482 0.01547 0.05272 3.021 5.915 10001 10000
z.star[4] 4.002 1.485 0.01381 1.094 3.997 6.904 10001 10000
z.star[5] 5.018 1.498 0.01472 2.072 4.997 8.019 10001 10000
z.star[6] 6.001 1.507 0.01373 3.007 6.014 8.924 10001 10000
z.star[7] 7.001 1.505 0.01466 4.042 7.003 9.975 10001 10000
z.star[8] 8.0 1.5 0.01457 5.091 7.993 10.94 10001 10000
z.star[9] 9.02 1.527 0.01373 6.031 9.021 11.97 10001 10000
z.star[10] 9.997 1.508 0.01582 7.087 9.988 12.9 10001 10000