On 11/17/05, Doran, Harold <[EMAIL PROTECTED]> wrote: > I think the authors are mistaken. Sigma is random error, and due to its > randomness it cannot be systematically related to anything. It is this > ind. assumption that allows for the likelihood to be expressed as > described in Pinhiero and Bates p.62.
I think not. The issue is dependence between the _estimates_ of sigma, tao, etc, and that may well be present. Presumably, if one can compute the likelihood surface as a function of the 3 parameters, the hessian at the MLE's would give the estimated covariance. However, I don't think nlme does this. A different approach you might want to consider is using mcmcsamp in the lme4 package (or more precisely, the Matrix package) to get samples from the joint posterior distribution. This is likely to be better than the asymptotic normal approximation in any case. Deepayan ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
