random effects with lme() -- comparison with lm()
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