model
{
#  X[,1]  = 1 if bush vote >= 50%
#  X[,2]  = 1 if Gore vote >= 50%
#  X[,3]  = Bush Percentage in CD
#  X[,4]  = Gore Percentage in CD
#  X[,5]  = Black Percentage in CD
#  X[,6]  = 1 if Southern State (11 states of Confederacy + OK + KY
#  X[,7]  = Hispanic Percentage in CD
#  X[,8]  = Median Family Income (in thousands) in CD
#  X[,9]  = Percent Owner-Occupied Housing
#  X[,10] = DW-NOMINATE 1st Dimension 
#  X[,11] = DW-NOMINATE 2nd Dimension
#  
#  PRIORS
#
         for (k in 1 : 8) { beta[k] ~ dnorm(0,0.001)} # vague priors
#	  
# LIKELIHOOD	
#
    for (i in 1 : 432)  # loop over congressional districts
    {     
#
         X[i,1] ~ dbern(p[i]);
         probit(p[i]) <- delta[i]
         delta[i] ~ dnorm(mu[i], 1.0)I(-4, 4)
         mu[i] <- beta[1]+X[i,5]*beta[2]+X[i,6]*beta[3]+X[i,7]*beta[4]+X[i,8]*beta[5]+X[i,9]*beta[6]+X[i,10]*beta[7]+X[i,11]*beta[8]
#
#  Borrowed From Simon Jackman
#
         llh[i] <- X[i,1]*log(p[i]) + (1-X[i,1])*log(1-p[i]);
    }  
   sumllh <- sum(llh[]);
#
 }