Hi. I want to try block sampling to improve convergence with a dataset with unequal samples. Here is a simple first model that I wanted to test, but I seem to have trouble going from the vector to the scalar nodes and receive "Attempt to redefine node c[1]". I tried all kinds of things, but either my syntax is off or my approach is faulty. Please suggest how I can go about fixing it. If you have any other strategies in mind, or article references that would be much appreciated, too.

for (i in 1:subj) { HR[i] ~ dbin(h[i],S) FA[i] ~ dbin(f[i],N) h[i] <- phi(d[i]/2-c[i]) f[i] <- phi(-d[i]/2-c[i]) } for (j in 1:9) { for (i in 1:subj) { c[i] ~ dnorm(grp.muC.muD[j],tauC[conds[i]]) d[i] ~ dnorm(grp.muC.muD[j],tauD[conds[i]]) } } for (cond in 1:(allcond)) { tauC[cond] ~ dgamma(.01,.01) tauD[cond] ~ dgamma(.01,.01) } grp.muC.muD[1:18] ~ dmnorm(grp.means[],grp.prc[,]) #Passed as data grp.means[1:18] <- zers grp.prc[1:18,1:18] <- iden