[R-sig-ME] Crossed random effects

Tue Mar 13 23:57:15 CET 2007

Do try glmm.admb() from library(glmmADMB)in R. It is in the development
phase with limited functionality for now. However, the results are very
fast with large data sets. One can try the base software ADMB
implemented in C which is claimed to be very fast.

Monica

-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Doran,
Harold
Sent: Tuesday, March 13, 2007 3:50 PM
To: MHH Stevens; Douglas Bates
Cc: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] Crossed random effects

I've missed some prior threads on this, please accept my apologies if
what I say below has already been noted. It is true that lme in the nlme
package and that HLM (stand-alone) can fit models with crossed random
effects. Now, I just dare you to try it. mlWin uses an MCMC
implementation for crossed random effects (if you want to go down that
road). 

I have some recent experiences fitting models in Stata and in R. Models
that took less than 2 minutes in R would take overnight in Stata. A few
years back, I also did some comparisons with HLM. For a small data set,
a model in lmer that could be fit in less than 1 minute took something
like 3 to 4 hours in HLM.

In the JSS special edition on psychometrics (forthcoming) Doug, Paul
Bliese, Maritza dowling and I estimate the 1PL for items and students
that are fully crossed using lmer. The estimates were resolved extremely
fast and the data set was rather large.  

I have simply not found another package that competes with lmer wrt to
computational speed for linear or generalized linear mixed models.

Harold

-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org on behalf of MHH Stevens
Sent: Tue 3/13/2007 5:14 PM
To: Douglas Bates
Cc: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] Crossed random effects

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 at 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
>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
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>>
>
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Dr. Hank Stevens, Assistant Professor
338 Pearson Hall
Botany Department
Miami University
Oxford, OH 45056

Office: (513) 529-4206
Lab: (513) 529-4262
FAX: (513) 529-4243
http://www.cas.muohio.edu/~stevenmh/
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http://www.muohio.edu/botany/

"If the stars should appear one night in a thousand years, how would men
believe and adore." -Ralph Waldo Emerson, writer and philosopher  
(1803-1882)

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