model
{
#  
#  X[,1] = DW-NOMINATE 1st Dimension
#  X[,2] = DW-NOMINATE 2nd Dimension
#  X[,3] = 1 if Republican, 0 otherwise
#  X[,4] = 1 if South (CQ def.), 0 otherwise
#  
#  PRIORS
#
         for (k in 1 : 3) { beta[k] ~ dnorm(0,0.001)} # vague priors
#	  
# LIKELIHOOD	
#
    for (i in 1 : 434)  # loop over congressional districts
    {     
#
         X[i,3] ~ dbern(p[i]);
         probit(p[i]) <- mu[i];
         mu[i] <- beta[1]+X[i,2]*beta[2]+X[i,4]*beta[3]
#
#  Borrowed From Simon Jackman
#
         llh[i] <- X[i,3]*log(p[i]) + (1-X[i,3])*log(1-p[i]);
    }  
    sumllh <- sum(llh[]);
#
 }