Dear Folks,
What about specialized stand alone mixed model software, such as HLM?
-Hank
On Mar 13, 2007, at 3:31 PM, Douglas Bates wrote:

> On 3/13/07, Kevin Wright <kw.statr@gmail.com> wrote:
>> I am confused by some apparent contradictions about fitting crossed
>> random effects in software.  Consider this quote from
>> http://www.mpi.nl/world/persons/private/baayen/publications/ 
>> baayenDavidsonBates.pdf
>> "To our knowledge, the only software currently available for fitting
>> mixed-effects models with crossed random effects is the lme4 package"
>
> That statement should have been more carefully worded.  It is in
> reference to the types of experimental situations described in that
> paper where random effects are associated with subject and item,
> subjects are crossed with item and the numbers of both the subjects
> and the items can be very large.
>
>> Yet, nlme and GLIMMIX appear to claim that crossed-random effects can
>> be fit by those respective tools:
>>
>> In Mixed Effects Models in S and S-Plus:
>> "The crossed random-effects structure is represented in lme by a
>> combination of pdBlocke3d and pdIdent objects" (page 163)
>
> It is possible to fit a model with crossed random effects with lme
> provided that the number of levels of both of the crossed factors is
> small.  Otherwise you end up with huge, sparse model matrices that are
> being treated as dense matrices and you quickly run out of memory or
> time or both.
>
> Really, doesn't a random effects specification like
> pdBlocked(list(pdIdent(~ rows - 1), pdIdent(~ columns - 1))) smell
> like a kludge to you?
>
>> http://support.sas.com/rnd/app/papers/glimmix.pdf
>> "The GLIMMIX procedure, on the other hand, determines by default the
>> marginal log likelihood as that of an approximate linear mixed model.
>> This allows multiple random effects, nested and crossed random
>> effects, multiple cluster types, and R-side random  
>> components."  [and]
>>  "Example 2. Mating Experiment with Crossed Random Effects"
>
> I think that several readers of this list could tell you war stories
> of trying to fit models with crossed random effects using SAS PROC
> MIXED or SAS PROC NLMIXED versus fitting the same model in lmer or
> lmer2.  You are correct that one can specify a model with crossed
> random effects in SAS PROC MIXED and that we overstated the uniqueness
> of the capabilities of lmer to fit such models.  However, if you want
> to try to fit such a model in SAS PROC MIXED when you have large
> numbers of subjects and large numbers of items you had better be
> prepared to wait for a long time.
>
>
>> Are these three quotes using different definitions of "crossed random
>> effects"?  Have I taken the quotes out of context?  Any  
>> clarifications
>> would be appreciated.
>>
>> Thanks,
>>
>> K Wright
>>
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>>
>
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