model {
for (i in 1:nsearch) { # search for "just
pr.sd[i] <- start + i*step # significant" prior
pr.mean[i] <- 0
}
pr.mean[nsearch+1] <- -0.26
pr.sd[nsearch+1] <- 0.13 # clinical prior
pr.mean[nsearch+2] <- 0
pr.sd[nsearch+2] <- 0.35 # sceptical prior

# replicate data for each prior and specify likelihood...
for (i in 1:(nsearch+3)) {
for (j in 1:2) {
r.rep[i,j] <- r[j]
n.rep[i,j] <- n[j]
r.rep[i,j] ~ dbin(pi[i,j], n.rep[i,j])
}
}
delta.mle <- -0.753
delta.mle ~ dnorm(delta[nsearch+4], 7.40)

# define priors and link to log-odds...
for (i in 1:(nsearch+2)) {
logit(pi[i,1]) <- alpha[i] + delta[i]/2
logit(pi[i,2]) <- alpha[i] - delta[i]/2
alpha[i] ~ dnorm(0, 0.0001)
delta[i] ~ dnorm(pr.mean[i], pr.prec[i])
pr.prec[i] <- 1/pow(pr.sd[i], 2)
}
pi[nsearch+3,1] ~ dbeta(0.5, 0.5)
pi[nsearch+3,2] ~ dbeta(0.5, 0.5) # Jeffreys prior
delta[nsearch+3] <- logit(pi[nsearch+3,1])
- logit(pi[nsearch+3,2])
delta[nsearch+4] ~ dunif(-10, 10) # locally uniform prior

delta.plot[1] <- delta[25]
delta.plot[2] <- delta[41]
delta.plot[3] <- delta[42]
delta.plot[4] <- delta[43]
delta.plot[5] <- delta[44]
prior.plot[1] ~ dnorm(pr.mean[25], pr.prec[25])
prior.plot[2] ~ dnorm(pr.mean[41], pr.prec[41])
prior.plot[3] ~ dnorm(pr.mean[42], pr.prec[42])
}

Data:
list(r = c(13, 23), n = c(163, 148),
start = 0.8, step = 0.005, nsearch = 40)

   node   mean   sd   MC error   2.5%   median   97.5%   start   sample
   delta[1]   -0.6351   0.3342   4.823E-4   -1.3   -0.6326   0.01388   1001   500000
   delta[2]   -0.6362   0.3347   4.86E-4   -1.301   -0.633   0.01206   1001   500000
   delta[3]   -0.6377   0.3349   4.731E-4   -1.303   -0.6351   0.01166   1001   500000
   delta[4]   -0.6392   0.3352   5.097E-4   -1.304   -0.6361   0.01176   1001   500000
   delta[5]   -0.6391   0.3353   5.101E-4   -1.305   -0.6364   0.01028   1001   500000
   delta[6]   -0.6412   0.3359   5.034E-4   -1.308   -0.6381   0.008304   1001   500000
   delta[7]   -0.6424   0.3358   4.924E-4   -1.309   -0.6396   0.007906   1001   500000
   delta[8]   -0.6433   0.3368   4.899E-4   -1.311   -0.6402   0.00849   1001   500000
   delta[9]   -0.6446   0.3364   4.899E-4   -1.312   -0.6418   0.007047   1001   500000
   delta[10]   -0.6456   0.3374   4.779E-4   -1.316   -0.6433   0.009481   1001   500000
   delta[11]   -0.6472   0.338   4.869E-4   -1.32   -0.6436   0.009341   1001   500000
   delta[12]   -0.6476   0.3387   4.852E-4   -1.321   -0.6451   0.008901   1001   500000
   delta[13]   -0.65   0.3385   4.826E-4   -1.322   -0.6471   0.007652   1001   500000
   delta[14]   -0.6503   0.3393   5.042E-4   -1.323   -0.6474   0.007114   1001   500000
   delta[15]   -0.6526   0.339   5.023E-4   -1.326   -0.6498   0.004507   1001   500000
   delta[16]   -0.6528   0.3393   4.813E-4   -1.328   -0.6498   0.005786   1001   500000
   delta[17]   -0.6544   0.3393   4.955E-4   -1.33   -0.6511   8.954E-4   1001   500000
   delta[18]   -0.6552   0.3403   4.917E-4   -1.332   -0.6521   0.002135   1001   500000
   delta[19]   -0.6555   0.3397   4.9E-4   -1.331   -0.6523   0.001753   1001   500000
   delta[20]   -0.6567   0.3402   4.801E-4   -1.334   -0.6534   0.001299   1001   500000
   delta[21]   -0.6581   0.3412   4.793E-4   -1.339   -0.6543   0.001263   1001   500000
   delta[22]   -0.6591   0.3412   5.083E-4   -1.338   -0.6555   4.645E-4   1001   500000
   delta[23]   -0.66   0.3417   5.323E-4   -1.34   -0.6572   0.00112   1001   500000
   delta[24]   -0.6619   0.3418   4.832E-4   -1.341   -0.6589   1.396E-4   1001   500000
   delta[25]   -0.6635   0.3423   5.075E-4   -1.343   -0.6609   3.598E-4   1001   500000
   delta[26]   -0.6633   0.342   5.054E-4   -1.344   -0.6601   -0.001383   1001   500000
   delta[27]   -0.6641   0.3422   5.069E-4   -1.344   -0.6613   -4.922E-4   1001   500000
   delta[28]   -0.6655   0.3426   5.223E-4   -1.347   -0.6628   -0.001396   1001   500000
   delta[29]   -0.6662   0.3435   5.198E-4   -1.349   -0.6626   -0.002746   1001   500000
   delta[30]   -0.6676   0.3439   5.213E-4   -1.352   -0.6641   -0.002247   1001   500000
   delta[31]   -0.6684   0.3436   5.134E-4   -1.352   -0.6654   -0.002408   1001   500000
   delta[32]   -0.6684   0.344   5.066E-4   -1.352   -0.6655   -0.00158   1001   500000
   delta[33]   -0.6696   0.3443   4.926E-4   -1.352   -0.6664   -0.003633   1001   500000
   delta[34]   -0.6705   0.345   4.966E-4   -1.357   -0.668   -0.002591   1001   500000
   delta[35]   -0.6717   0.3444   5.075E-4   -1.356   -0.6688   -0.005456   1001   500000
   delta[36]   -0.6737   0.3454   5.048E-4   -1.36   -0.6708   -0.004817   1001   500000
   delta[37]   -0.6748   0.345   4.986E-4   -1.361   -0.6714   -0.006334   1001   500000
   delta[38]   -0.6743   0.3459   5.109E-4   -1.36   -0.6713   -0.004526   1001   500000
   delta[39]   -0.6752   0.3456   5.119E-4   -1.362   -0.6717   -0.005816   1001   500000
   delta[40]   -0.6756   0.3454   5.074E-4   -1.364   -0.6715   -0.008499   1001   500000
   delta[41]   -0.317   0.1223   1.741E-4   -0.5562   -0.317   -0.07745   1001   500000
   delta[42]   -0.3664   0.2509   3.497E-4   -0.8608   -0.366   0.1245   1001   500000
   delta[43]   -0.7523   0.367   5.342E-4   -1.487   -0.7479   -0.04719   1001   500000
   delta[44]   -0.7534   0.3673   5.432E-4   -1.475   -0.7529   -0.0334   1001   500000

[example-5_5_1-great0][example-5_5_1-great1]