[R-sig-ME] Making lme4 faster for specific case of sparse x

[Douglas Bates]

If X == Z don't you have problems with estimability?  It seems that mle
would always correspond to all random effects being zero.

Perhaps I misunderstand the situation.  Could you provide a bit more detail
on how it comes about that X == Z?

On Mon, Aug 8, 2016 at 5:01 PM Patrick Miller <pmille13 at nd.edu> wrote:

> Hello,
>
> For my dissertation, I'm working on extending boosted decision trees to
> clustered data.
>
> In one of the approaches I'm considering, I use *lmer* to estimate random
> effects within each gradient descent iteration in boosting. As you might
> expect, this is computationally intensive. However, my intuition is that
> this step could be made faster because my use case is very specific.
> Namely, in each iteration, *X = Z*, and *X* is a sparse matrix of 0s and 1s
> (with an intercept).
>
> I was wondering if anyone had suggestions or (theoretical) guidance on this
> problem. For instance, is it possible that this special case permits faster
> optimization via specific derivatives? I'm not expecting this to be
> implemented in lmer or anything, and I'm happy to work out a basic
> implementation myself for this case.
>
> I've read the vignette on speeding up the performance of lmer, and
> setting calc.derivs
> = FALSE resulted in about a 15% performance improvement for free, which was
> great. I was just wondering if it was possible to go further.
>
> Thanks in advance,
>
> - Patrick
>
> --
> Patrick Miller
> Ph.D. Candidate, Quantitative Psychology
> University of Notre Dame
>
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>
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