On Thu, 2004-01-15 at 16:30, Douglas Bates wrote: <...snip...> > (BTW, I wouldn't say that this is equivalent to a fixed effects > model. It is still a random effects model with two variance > components. It just doesn't have well-defined estimates for those two > variance components.)
Agreed. <...snip...> > You should find that intervals() applied to your fitted model produces > huge intervals on the variance components, which is one way of > diagnosing an ill-defined or nearly ill-defined model. Following your suggestion, I got: > intervals(lme(Y~1,data=simdat,random=~1|A)) Error in intervals.lme(lme(Y ~ 1, data = simdat, random = ~1 | A)) : Cannot get confidence intervals on var-cov components: Non-positive definite approximate variance-covariance This led me to: > lme(Y~1,data=simdat,random=~1|A)$apVar [1] "Non-positive definite approximate variance-covariance" As a new feature suggestion for lme(), would it be appropriate to use "apVar" as a warning flag in this case? Sincerely, Jerome Asselin ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html