[R-sig-ME] More naive questions: Speed comparisons? what is a "stack imbalance" in lmer? does lmer center variables?

[Ben Bolker]

   As of sometime last year (? I think ?), ADMB is
free/gratis/libre/open source (BSD licensed).  Even before that,
glmmADMB (which was an R package with a binary component, available for
download) was "free as in beer". In its current status, I think of ADMB
in the same category as WinBUGS -- a powerful, albeit sometimes
unwieldy, tool that can be used through an R interface to solve general
problems by writing model descriptions in a non-R language.
  I have to agree with Kevin that the diversity of mixed model software
tools is a good thing.

  cheers
    Ben Bolker

Douglas Bates wrote:
> Got to disagree with you, Kevin.  admb and asreml are not part of R,
> even in the general sense of R packages.  R is Open Source - they are
> not. Tacking on an R interface to proprietary software and saying it
> is available in R is misleading and dishonest.
> 
> On Wed, Sep 23, 2009 at 8:54 AM, Kevin Wright <kw.stat at gmail.com> wrote:
>> Paul,
>>
>> It appears to me that the published timings you reference are
>> comparing the __nlme__ package with other software.  So the answer is
>> yes, nlme really is that slow for some models.  You are probably aware
>> that the __lme4__ package has faster algorithms.
>>
>> There are many ways to fit mixed models in R including nlme, lme4,
>> MCMCglmm, admb asreml, BUGS, etc.  If I was teaching a course, I would
>> try to expose students to at least two of those in some detail and
>> touch briefly on the others: nlme can fit a variety of complex
>> varaiance structures, lme4 has faster algorithms, asreml is the only
>> choice of animal/plant breeders and has commercial support, MCMCglmm
>> has some Bayesian aspects and can fit some heteroskedastic variance
>> structures, admb is used in Fish & Wildlife, etc.
>>
>> Mixed model fitting in R is definitely not a case of "one size fits all".
>>
>> Kevin Wright
>>
>>
>> On Wed, Sep 23, 2009 at 1:36 AM, Paul Johnson <pauljohn32 at gmail.com> wrote:
>>> Sent this to r-sig-debian by mistake the first time.  Depressing.
>>>
>>> 1.  One general question for general discussion:
>>>
>>> Is HLM6 faster than lmer? If so, why?
>>>
>>> I'm always advocating R to students, but some faculty members are
>>> skeptical.  A colleague compared the commercial HLM6 software to lmer.
>>>  HLM6 seems to fit the model in 1 second, but lmer takes 60 seconds.
>>>
>>> If you have HLM6 (I don't), can you tell me if you see similar differences?
>>>
>>> My first thought was that LM6 uses PQL by default, and it would be
>>> faster.  However, in the output, HLM6 says:
>>>
>>> Method of estimation: restricted maximum likelihood
>>>
>>> But that doesn't tell me what quadrature approach they use, does it?
>>>
>>> Another explanation for the difference in time might be the way HLM6
>>> saves the results of some matrix calculations and re-uses them behind
>>> the scenes.  If every call to lmer is re-calculating some big matrix
>>> results, I suppose that could explain it.
>>>
>>> There are comparisons from 2006 here
>>>
>>> http://www.cmm.bristol.ac.uk/learning-training/multilevel-m-software/tables.shtml
>>>
>>> that indicate that lme was much slower than HLM, but that doesn't help
>>> me understand *why* there is a difference.
>>>
>>> 2. What does "stack imbalance in .Call" mean in lmer?
>>>
>>> Here's why I ask.  Searching for comparisons of lmer and HLM,  I went
>>> to CRAN &  I checked this document:
>>>
>>> http://cran.r-project.org/web/packages/mlmRev/vignettes/MlmSoftRev.pdf
>>>
>>> I *think* these things are automatically generated.  The version
>>> that's up there at this moment  (mlmRev edition 0.99875-1)  has pages
>>> full of the error message:
>>>
>>> stack imbalance in .Call,
>>>
>>> Were those always there?  I don't think so.   What do they mean?
>>>
>>> 3. In the HLM6 output, there is a message at the end of the variable list:
>>>
>>> '%' - This level-1 predictor has been centered around its grand mean.
>>> '$' - This level-2 predictor has been centered around its grand mean.
>>>
>>> What effect does that have on the estimates?  I believe it should have
>>> no effect on the fixed effect slope estimates, but it seems to me the
>>> estimates of the variances of random parameters would be
>>> changed.  In order to make the estimates from lmer as directly
>>> comparable as possible, should I manually center all of the variables
>>> before fitting the model?   I'm a little stumped on how to center a
>>> multi-category factor before feeding it to lmer.  Know what I mean?
>>>
>>> pj
>>>
>>> --
>>> Paul E. Johnson
>>> Professor, Political Science
>>> 1541 Lilac Lane, Room 504
>>> University of Kansas
>>>
>>> _______________________________________________
>>> R-sig-mixed-models at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
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> 
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-- 
Ben Bolker
Associate professor, Biology Dep't, Univ. of Florida
bolker at ufl.edu / www.zoology.ufl.edu/bolker
GPG key: www.zoology.ufl.edu/bolker/benbolker-publickey.asc