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

Ben Bolker bolker at ufl.edu
Wed Sep 23 19:21:54 CEST 2009

  Source *is* available:

Peter Dalgaard wrote:
> Ben Bolker wrote:
>>    As of sometime last year (? I think ?), ADMB is
>> free/gratis/libre/open source (BSD licensed). 
> As of _next_ year is more like it. Their downloads are still 
> binary-only. BSD does not imply Open Source when you don't have the 
> sources, and "open source project" in this case means a project to make 
> the software open source. The intention appears to be honourable, though.
>   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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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

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