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

[Patrick Miller]

Thanks for that clarification.  In my situation, the effect of each
predictor in X was allowed to vary by a single grouping variable. The lmer
formula is something like the following:

y ~ 1 + X1 + X2 + X3 + ... + ( 1 + X1 + X2 + X3 + ... | id)

- Patrick

On Mon, Aug 8, 2016 at 6:08 PM, Douglas Bates <bates at stat.wisc.edu> wrote:

> 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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>>
>


-- 
Patrick Miller
Ph.D. Candidate, Quantitative Psychology
University of Notre Dame

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