[R-sig-ME] Interpretation of odd error variances in glmer

Daniel Ezra Johnson danielezrajohnson at gmail.com
Thu Nov 19 15:30:07 CET 2009

On Thu, Nov 19, 2009 at 9:24 AM, Brendan Halpin <brendan.halpin at ul.ie> wrote:
> On Thu, Nov 19 2009, Douglas Bates wrote:
>> It is quite legitimate for the ML estimates of a variance component to
>> be zero.  It simply means that there is not enough variability
>> accounted for by that term to warrant incorporating the term.
> Thanks for the swift response.
> Given that the estimate seems to be exactly zero, can I read your answer
> to say that the term has somehow been dropped by the algorithm?

For all intents and purposes, yes.

> I've investigated a bit more and find that the zero variance seems to
> occur with the combination of the inclusion of a department-level
> covariate (depfemr), and the cross-classifying individual-level random
> effect (ulid).

I wasn't quite sure of the structure of the data here, but it raised a
question for me. I understand that when a random effect is fully
nested within a fixed effect, the penalty on the random effect
resolves the singularity and allows estimation of both. (That is, if
appropriate, you could model depfemr as a fixed effect?)

But if/when two random effects are fully nested, as is frequently
modeled and I think is the case here, how does the algorithm know how
to assign the variance as between e.g. depfemr and ulid?


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