model {
for (i in 1:N) {
numbers1toN[i] <- i
p[i] ~ dbeta(0.5, 0.5)
r[i] ~ dbin(p[i], n[i])
hosp.rank[i] <- rank(p[], i) # rank of hospital i
prob.lowest[i] <- equals(hosp.rank[i], 1) # =1 if hosp i is lowest
prob.highest[i] <- equals(hosp.rank[i], N) # =1 if hosp i is highest
}
hosp.lowest <- inprod(numbers1toN[], prob.lowest[])
# index of lowest hosp
hosp.highest <- inprod(numbers1toN[], prob.highest[])
# index of highest hosp
}
Data:
list(N = 11, r=c(25,24,23,25,42,24,53,26,25,58,31),
n=c(187,323,122,164,405,239,482,195,177,581,301))
node mean sd MC error 2.5% median 97.5% start sample
hosp.highest 3.745 1.918 0.02205 1.0 3.0 9.0 1001 10000
hosp.lowest 3.631 2.899 0.02966 2.0 2.0 11.0 1001 10000
hosp.rank[1] 7.573 2.168 0.0222 2.0 8.0 11.0 1001 10000
hosp.rank[2] 1.521 1.035 0.01015 1.0 1.0 5.0 1001 10000
hosp.rank[3] 10.43 1.082 0.01121 7.0 11.0 11.0 1001 10000
hosp.rank[4] 8.901 1.773 0.01718 4.0 9.0 11.0 1001 10000
hosp.rank[5] 4.37 1.942 0.02142 1.0 4.0 8.0 1001 10000
hosp.rank[6] 4.127 2.225 0.02064 1.0 4.0 9.0 1001 10000
hosp.rank[7] 5.175 1.855 0.02055 2.0 5.0 9.0 1001 10000
hosp.rank[8] 7.547 2.154 0.02353 2.0 8.0 11.0 1001 10000
hosp.rank[9] 8.178 1.987 0.01974 3.0 9.0 11.0 1001 10000
hosp.rank[10] 3.851 1.692 0.01861 1.0 4.0 8.0 1001 10000
hosp.rank[11] 4.333 2.096 0.0212 1.0 4.0 9.0 1001 10000
p[1] 0.1355 0.02512 2.489E-4 0.08978 0.1343 0.1877 1001 10000
p[2] 0.07573 0.0146 1.374E-4 0.04982 0.07485 0.1064 1001 10000
p[3] 0.1914 0.03556 3.603E-4 0.1274 0.1894 0.2652 1001 10000
p[4] 0.1542 0.02814 2.576E-4 0.1031 0.1528 0.2138 1001 10000
p[5] 0.1045 0.01548 1.73E-4 0.07638 0.1038 0.1364 1001 10000
p[6] 0.1021 0.01964 1.832E-4 0.06684 0.1011 0.1446 1001 10000
p[7] 0.1108 0.01424 1.625E-4 0.08441 0.1104 0.1403 1001 10000
p[8] 0.1351 0.02485 2.506E-4 0.09094 0.1336 0.1885 1001 10000
p[9] 0.1432 0.02597 2.752E-4 0.09638 0.1419 0.1979 1001 10000
p[10] 0.1006 0.01247 1.283E-4 0.07741 0.1002 0.1264 1001 10000
p[11] 0.1042 0.01746 1.782E-4 0.07281 0.1034 0.1406 1001 10000
prob.highest[1] 0.0458 0.2091 0.002026 0.0 0.0 1.0 1001 10000
prob.highest[2] 0.0 0.0 1.0E-12 0.0 0.0 0.0 1001 10000
prob.highest[3] 0.6821 0.4657 0.005048 0.0 1.0 1.0 1001 10000
prob.highest[4] 0.1499 0.357 0.003289 0.0 0.0 1.0 1001 10000
prob.highest[5] 2.0E-4 0.01414 1.407E-4 0.0 0.0 0.0 1001 10000
prob.highest[6] 0.0012 0.03462 3.835E-4 0.0 0.0 0.0 1001 10000
prob.highest[7] 3.0E-4 0.01732 1.714E-4 0.0 0.0 0.0 1001 10000
prob.highest[8] 0.0436 0.2042 0.002091 0.0 0.0 1.0 1001 10000
prob.highest[9] 0.0759 0.2648 0.00285 0.0 0.0 1.0 1001 10000
prob.highest[10] 1.0E-4 0.009999 1.0E-4 0.0 0.0 0.0 1001 10000
prob.highest[11] 9.0E-4 0.02999 3.208E-4 0.0 0.0 0.0 1001 10000
prob.lowest[1] 0.0066 0.08097 8.671E-4 0.0 0.0 0.0 1001 10000
prob.lowest[2] 0.7099 0.4538 0.00462 0.0 1.0 1.0 1001 10000
prob.lowest[3] 0.0 0.0 1.0E-12 0.0 0.0 0.0 1001 10000
prob.lowest[4] 0.0013 0.03603 3.667E-4 0.0 0.0 0.0 1001 10000
prob.lowest[5] 0.0447 0.2066 0.002258 0.0 0.0 1.0 1001 10000
prob.lowest[6] 0.1012 0.3016 0.002965 0.0 0.0 1.0 1001 10000
prob.lowest[7] 0.0138 0.1167 0.00127 0.0 0.0 0.0 1001 10000
prob.lowest[8] 0.007 0.08337 8.469E-4 0.0 0.0 0.0 1001 10000
prob.lowest[9] 0.0034 0.05821 5.545E-4 0.0 0.0 0.0 1001 10000
prob.lowest[10] 0.048 0.2138 0.002015 0.0 0.0 1.0 1001 10000
prob.lowest[11] 0.0641 0.2449 0.002288 0.0 0.0 1.0 1001 10000