How is for loop in JAGS parsed when the upper bound is zero?

Question

I'm interested in how the for loop sequence in "for (i in 1:n)" is parsed when n = 0. Does 1:n give c(1, 0), so the for loop run for the indices 1 and 0, or is the for loop then skipped over?

An example: Say I have observations from 10 groups, and I have default and special observations which I model with a normal and t-distribution respectively. Each group can have both types of observations, or only default, or only special observations. Is it possible to set up the model in this way:

model {
  for (g in 1:ngroups) {
    for (i in 1:ndefault.g[g]) { # ndefault.g[g] may be 0 for certain groups
      y.gi[g, i] ~ dnorm(yhat.gi[g, i], tauy.gi[g, i])
    }
    for (i in 1:nspecial.g[g]) { # nspecial.g[g] may be 0 for certain groups
      y.gi[g, i] ~ dt(yhat.gi[g, i], tauy.gi[g, i], dft)
    }
  }
...
}

Thanks!

Cross-posted at: https://sourceforge.net/p/mcmc-jags/discussion/610037/thread/d13fd9a2/

Answer 1

Easy to test:

for (i in 1:0) print(i)
[1] 1
[1] 0