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

Peter Dalgaard p.dalgaard at biostat.ku.dk
Wed Sep 23 19:22:26 CEST 2009

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
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    O__  ---- Peter Dalgaard             Øster Farimagsgade 5, Entr.B
   c/ /'_ --- Dept. of Biostatistics     PO Box 2099, 1014 Cph. K
  (*) \(*) -- University of Copenhagen   Denmark      Ph:  (+45) 35327918
~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk)              FAX: (+45) 35327907

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