On Mon, Mar 25, 2013 at 12:35 PM, Kevin Wright <kw.stat@gmail.com> wrote:

> Here is an example.  It is simulated data, but simulated in such a way as
> to be quite close in structure to my actual data.
>
>
> https://stat.ethz.ch/pipermail/r-sig-mixed-models/attachments/20090806/c933f190/attachment.pl
>
>
That's a rather special case.  All you are showing is that using a sparse
fixed-effects model matrix for a factor of length 24000 with 6101 levels is
more compact than using a dense model matrix, which is not terribly
surprising.  If you look at the error messages you will see that the error
occurs early on when first attempting to build the fixed-effects model
matrix.


>
>
> On Mon, Mar 25, 2013 at 12:23 PM, Douglas Bates <bates@stat.wisc.edu>wrote:
>
>> I am still working on improving the computational methods for linear mixed
>> models, especially with regard to fitting models to large data sets.
>>  Obviously I will compare the performance of different approaches on
>> simulated data sets but it is usually much more informative to compare the
>> methods on real data.
>>
>> If you have used lmer to fit linear mixed models to large data sets and
>> you
>> can release your data I would very much appreciate your contacting me.
>>  Because the purpose is to benchmark various computational methods rather
>> than demonstrating analysis strategies anonymized data is fine.
>>
>> All I need is the fixed-effects model matrix, enough information to
>> generate the random-effects model matrix and the response.  If the random
>> effects have the form
>>
>> (1|f) + (1|g) + ...
>>
>> which I refer to as "simple scalar random effects only",  then I just need
>> to grouping factors f, g, etc.  If there are vector-valued random-effects
>> terms, like
>>
>> (year|student)
>>
>> then I will need the vectors on the left-hand side as well as those on the
>> right-hand side.
>>
>> The simplest form to transfer is a saved R data set, preferrably using the
>> 'xz' compression but whatever works for you is fine by me.  I don't think
>> I
>> will make these into a package because such a package could be very large.
>>  I will, however, arrange for some form of public availability of the
>> data.
>>
>> To me, a "large data set" for linear mixed models is at least in the
>> hundreds of thousands of responses.  Cases with partially crossed grouping
>> factors for random effects would be especially appreciated.
>>
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>>
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>>
>
>
>
> --
> Kevin Wright
>

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