# WinBUGS truncated normal distribution [duplicate]

I am estimating a stochastic frontier with a mixed model. So far the half normal distribution worked good but I need a truncated normal distribution. It does not work, and I receive the error „Expected collection operator c“. I am using R2WinBUGS and as you can see in the model I have tried OpenBUGS and WinBUGS. Any suggestion? My model looks like:

for (i in 1:N)
{

mu[i] <- alpha + x[i,1]*beta[1] + x[i,2]*beta[2] + x[i,3]*beta[3] +
x[i,4]*beta[4] + x[i,5]*beta[5] + u0[county[i]] + u1[county[i]]*x[i,1] +
u2[county[i]]*x[i,2] + u3[county[i]]*x[i,3] + u4[county[i]]*x[i,4] +
u5[county[i]]*x[i,5] - z[ID[i]]

y[i] ~ dnorm(mu[i], tau)

}

for (i in 1:220) {
z[i] ~ djl.dnorm.trunc(rho,lambda,0,1000)

z[i] ~ dnorm(rho,lambda)T(0,100)  #For openbugs

eff[i] <- exp(-z[i])
}


prior for rho~dnorm(0,0.027)

I would appreciate your help! Regards, Daniel

-

## marked as duplicate by David, Peter Flom♦, whuber♦Jun 4 '13 at 12:29

Is this question only about how to get something done in R / WinBUGS? If so, it would be off-topic for CV (see our FAQ), but on topic for Stack Overflow. If you have a substantive statistical question, please edit to clarify it, if not, you can flag your Q for migration (please don't cross-post, though). –  gung Feb 3 '13 at 19:38
The function djl.dnorm.trunc looks suspicious to me. Why don't you use the dnorm(..)I(..) here also? –  Curious Feb 3 '13 at 19:53
Thanks Thomas, I tried that one too and it doesn't work –  Daniel C Feb 4 '13 at 8:38

For truncating, I use the operator I i.e.:
x ~ dnorm(mean, tau)I(low, high)