model {
for (i in 1:N) {
y[i] ~ dbin(theta[i], n[i])
logit(theta[i]) <- alpha + beta[i]
beta[i] <- b[i] - mean(b[])
b[i] ~ dunif(-10,10)
}
alpha ~ dunif(-10,10)
}
Data:
list(N = 12, y=c(41,25,24,23,25,42,24,53,26,25,58,31),
n=c(143,187,323,122,164,405,239,482,195,177,581,301))
node mean sd MC error 2.5% median 97.5% start sample
alpha -1.927 0.05755 6.053E-4 -2.041 -1.927 -1.817 1001 10000
b[1] 2.744 2.567 0.2535 -2.519 2.735 7.587 1001 10000
b[2] 1.782 2.567 0.2533 -3.471 1.78 6.625 1001 10000
b[3] 1.12 2.569 0.2535 -4.142 1.088 5.939 1001 10000
b[4] 2.19 2.568 0.2532 -3.065 2.174 7.021 1001 10000
b[5] 1.932 2.569 0.2534 -3.342 1.922 6.805 1001 10000
b[6] 1.495 2.567 0.2535 -3.819 1.482 6.331 1001 10000
b[7] 1.452 2.569 0.2534 -3.805 1.439 6.269 1001 10000
b[8] 1.564 2.562 0.2532 -3.712 1.569 6.354 1001 10000
b[9] 1.778 2.567 0.2533 -3.524 1.766 6.609 1001 10000
b[10] 1.84 2.565 0.2529 -3.459 1.83 6.665 1001 10000
b[11] 1.457 2.564 0.2534 -3.827 1.451 6.272 1001 10000
b[12] 1.487 2.568 0.2535 -3.777 1.485 6.291 1001 10000
beta[1] 1.007 0.178 0.001906 0.6519 1.01 1.352 1001 10000
beta[2] 0.04551 0.2046 0.001708 -0.3664 0.04888 0.4285 1001 10000
beta[3] -0.6166 0.2038 0.002277 -1.031 -0.6106 -0.2337 1001 10000
beta[4] 0.4527 0.2191 0.001998 0.009193 0.4557 0.87 1001 10000
beta[5] 0.1954 0.2073 0.001987 -0.2187 0.1993 0.5846 1001 10000
beta[6] -0.2422 0.1593 0.001476 -0.5561 -0.2401 0.06291 1001 10000
beta[7] -0.2847 0.2076 0.001793 -0.7059 -0.2765 0.1021 1001 10000
beta[8] -0.1725 0.1432 0.00139 -0.4597 -0.1705 0.1024 1001 10000
beta[9] 0.04117 0.2001 0.001789 -0.3645 0.04358 0.4219 1001 10000
beta[10] 0.1032 0.2076 0.001941 -0.3158 0.108 0.4988 1001 10000
beta[11] -0.2797 0.139 0.001382 -0.5566 -0.2774 -0.01437 1001 10000
beta[12] -0.2496 0.1827 0.001699 -0.6214 -0.2454 0.09373 1001 10000