On Sat, 3 Apr 2004, Gabor Grothendieck wrote:
>
> 2. Another possibility is to create a giant regression that does
> all the usergroup specific regressions at once and then repeat
> it without the usergroup variable to get the rest.
>
> df2 is a new data frame that strings out all the x variables into
> a single long column and adds a new factor i that identifies
> which x variable it is. y and u are repeated three times to bring
> them into line with x. (
>
<snip>
>
> 3. Note that the giant regression approach works as long as you are only
> interested in the coefficients, however, if you were interested in the
> variances then this would not work since each of the two regressions uses a
> pooled estimate of variance.
>
> QUESTION: As a matter of interest, would someone that is familiar with random
> effects models show what the corresponding giant model is with separate
> variances for each regression.
There are actually two answers to this. The first is that if you use the
White/Huber robust/sandwich/model-agnostic variances you get the right
variances automatically. This is useful when you what to compare
coefficients across models.
On the other hand, I don't think you can get the answer you are looking
for. The problem is that the giant regression estimates are not MLEs for
anything, and so I think you can't get lme() to simultaneously get the
right coefficients and the right variances.
-thomas
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