ModCrit.txt

model {
  # Binomial likelihood:
  for (i in 1:3) {
   r[i] ~ dbin(p[i], n[i])
  }
  for (i in 5:12) {
  r[i] ~ dbin(p[i], n[i])
  }
  
 # Declared relationships
 # between basic and
 # functional parameters:
 p[1] <- a
 p[2] <- b
 p[3] <- c
 p[4] <- d
 p[5] <- (b * d + (1 - a - b) * e / (1 - a))
 p[6] <- (a * c + b * d + (1 - a - b) * e)
 p[7] <- (a * c * f) / ((a * c * f) + (b * d * g) + (e * h * (1 - a - b)))
 p[8] <- (b * d * g) / ((b * d * g) + (e * h * (1 - a - b)))
 p[9] <- ((a * c * d) + (b * d * g) + (e * h * (1 - a - b)) /
             ((a * c) + (b * d) + e * (1 - a - b)))
 p[10] <- g
 p[11] <- w
 p[12] <- ((b * d / ((b * d) + e * (1 - a - b)))) + (e * w) * (1 - a - b) / 
             ((b * d) + e * (1 - a - b))
 
# Estimated basic
# prior parameters:
a ~ dbeta(1, 2)
c ~ dbeta(1, 1)
d ~ dbeta(1, 1)
e ~ dbeta(1, 1)
f ~ dbeta(1, 1)
g ~ dbeta(1, 1)
h ~ dbeta(1, 1)
w ~ dbeta(1, 1)
z ~ dbeta(1, 1)
# Estiamted functional
# prior parameters:
b <- z * (1 - a)

r.rep ~ dbin(p[4], n[4])
p.xval <- step(r.rep - r[4]) - 0.5 * equals(r.rep, r[4])
dev[4] <- 0

}