[精彩WinBUGS答问]WinBUGS truncated normal distribution

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

    y ~ dnorm(mu, tau)

}

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

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

    eff <- exp(-z)
}
prior for rho~dnorm(0,0.027)

I would appreciate your help! Regards

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关键词：distribution truncated winbugs WINBUG normal collection receive normal error

Openbugs (so I would assume the same in winbugs, but could be wrong) you can do a truncated distribution using the T function, something like:

y[i] ~ dnorm(x[i],tau)T(0,10)

Unlike OpenBUGS, WinBUGS doesn't provide the T() operation

Actually, WinBUGS has I() which is a censoring and truncation function. WinBUGS manual page 12.mrc-bsu.cam.ac.uk/bugs/winbugs/manual14.pdf

According to http://www.unc.edu/courses/2010fall/ecol/563/001/docs/solutions/assign10.htm, two gotchas that may apply are that

the upper truncation point may need to be adjusted (e.g., from 10 to 10.0001)
initial values that seed the chains need to be specified explicitly, because WinBUGS may pick ones that lie outside of (α,β).

what is skew-normal distribution?