model {
for (i in 1:12) {
y[i] ~ dbin(theta, n[i])
res[i] <- (y[i] - n[i]*theta)/sqrt(n[i]*theta*(1-theta))
res2[i] <- res[i]*res[i]
}
theta ~ dunif(0, 1)
X2.obs <- sum(res2[]) # sum of squared stand. resids
}
Data:
list(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
X2.obs 59.12 2.834 0.0265 56.76 58.11 66.94 1001 10000
res[1] 6.156 0.3351 0.003195 5.515 6.153 6.826 1001 10000
res[2] 0.5903 0.2511 0.002396 0.1058 0.5894 1.088 1001 10000
res[3] -2.514 0.2626 0.002507 -3.023 -2.513 -1.996 1001 10000
res[4] 2.343 0.2411 0.002299 1.88 2.342 2.823 1001 10000
res[5] 1.293 0.2503 0.002388 0.8105 1.292 1.79 1001 10000
res[6] -0.9911 0.3314 0.003163 -1.632 -0.9917 -0.3355 1001 10000
res[7] -0.9179 0.2514 0.002399 -1.404 -0.9183 -0.4207 1001 10000
res[8] -0.658 0.3703 0.003533 -1.374 -0.6588 0.07486 1001 10000
res[9] 0.5875 0.2561 0.002443 0.09329 0.5865 1.095 1001 10000
res[10] 0.884 0.2507 0.002391 0.4007 0.883 1.381 1001 10000
res[11] -1.475 0.3911 0.003732 -2.232 -1.476 -0.7017 1001 10000
res[12] -0.8926 0.285 0.002719 -1.444 -0.8931 -0.329 1001 10000
res2[1] 38.01 4.138 0.03934 30.41 37.85 46.6 1001 10000
res2[2] 0.4115 0.312 0.002928 0.01257 0.3474 1.185 1001 10000
res2[3] 6.387 1.322 0.01269 3.984 6.317 9.147 1001 10000
res2[4] 5.55 1.136 0.01078 3.535 5.484 7.972 1001 10000
res2[5] 1.734 0.6563 0.006204 0.657 1.668 3.204 1001 10000
res2[6] 1.092 0.6711 0.006526 0.1126 0.9835 2.669 1001 10000
res2[7] 0.9057 0.4679 0.004534 0.1771 0.8433 1.975 1001 10000
res2[8] 0.57 0.5189 0.00511 0.003287 0.4341 1.891 1001 10000
res2[9] 0.4107 0.3174 0.002977 0.01053 0.344 1.2 1001 10000
res2[10] 0.8443 0.4547 0.004284 0.1606 0.7796 1.908 1001 10000
res2[11] 2.328 1.168 0.01132 0.4926 2.178 4.988 1001 10000
res2[12] 0.8779 0.5185 0.005038 0.1083 0.7977 2.088 1001 10000