Bernoulli data: via probit/logit or via latent variable
Finney's vasocostriction data (1947, Biometrika, 34)

Probit Link Model 
model
	{
	  for( i in 1 : n) {
	   y[i] ~ dbern(p[i])
		
 #	   probit(p[i]) <-  mu[i], or alternatively
           p[i]<-phi(mu[i])
 #Another link:  
 #logit(p[i]) <-  mu[i]
  
         x1[i] <- log(10*v[i])
         x2[i] <- log(10*r[i])
         mu[i] <-b[1] + b[2] *x1[i] + b[3]*x2[i]
	   }
	   b[1] ~ dnorm(0.0,0.001)
	   b[2] ~ dnorm(0.0,0.25)	
  	   b[3] ~ dnorm(0.0,0.25)	
	}
	
Probit Model via Latent Variable
model {  
            for (i in 1:n) {
             z[i] ~ dnorm(mu[i],1)I(low[y[i]+1],high[y[i]+1]
             x1[i] <- log(10*v[i])
             x2[i] <- log(10*r[i])
             mu[i] <-b[1] + b[2]*x1[i] + b[3]*x2[i]
             
#            probit(p[i]) <- mu[i]           
#            res[i] <- z[i]-mu[i]
#            lowest.res[i] <- equals(res[i],min39)
             }
#            min39 <- ranked(res[],1)

	   b[1] ~ dnorm(0.0,0.001)
	   b[2] ~ dnorm(0.0,0.001)	
  	   b[3] ~ dnorm(0.0,0.001)}

Data for latent variable model
list(n=39,y=c(1,1,1,1,1,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1,0,0,0,0,1,0,1,0,1,0,1,0,0,1,1,1,0,0,1),
v=c(3.7,3.5,1.25,.75,.8,.7,.6,1.1,.9,.9,.8,.55,.6,1.4,.75,2.3,3.2,.85,1.7,1.8,.4,.95,1.35,1.5,
1.6,.6,1.8,.95,1.9,1.6,2.7,2.35,1.1,1.1,1.2,.8,.95,.75,1.3),
r=c(.825,1.09,2.5,1.5,3.2,3.5,.75,1.7,.75,.45,.57,2.75,3,2.33,3.75,1.64,1.6,1.415,1.06,
1.8,2,1.36,1.35,1.36,1.78,1.5,1.5,1.9,.95,.4,.75,.03,1.83,2.2,2,3.33,1.9,1.9,1.625),
low=c(-20,0),high=c(0,20))

Data for probit link model 
list(n=39,y=c(1,1,1,1,1,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1,0,0,0,0,1,0,1,0,1,0,1,0,0,1,1,1,0,0,1),
x1=c(3.7,3.5,1.25,.75,.8,.7,.6,1.1,.9,.9,.8,.55,.6,1.4,.75,2.3,3.2,.85,1.7,1.8,.4,.95,1.35,1.5,
1.6,.6,1.8,.95,1.9,1.6,2.7,2.35,1.1,1.1,1.2,.8,.95,.75,1.3),
x2=c(.825,1.09,2.5,1.5,3.2,3.5,.75,1.7,.75,.45,.57,2.75,3,2.33,3.75,1.64,1.6,1.415,1.06,
1.8,2,1.36,1.35,1.36,1.78,1.5,1.5,1.9,.95,.4,.75,.03,1.83,2.2,2,3.33,1.9,1.9,1.625))

Inits
list(b=c(0,0,0))