[R] random effects with lme() -- comparison with lm()

[Jerome Asselin]

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