Jerome Asselin <[EMAIL PROTECTED]> writes: > 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?
Certainly. You may know that we are doing a major revision of the lme computational methods based on the ability to calculate both the gradient and the Hessian of the profiled log-likelihood, as described in http://www.stat.wisc.edu/~bates/reports/MixedComp.pdf I think that when we have both the gradient and the Hessian we will be in a much better situation to diagnose ill-defined estimates. The apVar component in the current lme objects is an approximate variance-covariance matrix from numerical derivatives. Working with an exact Hessian should be much more reliable. ______________________________________________ [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