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

Douglas Bates bates at stat.wisc.edu
Wed Sep 23 18:31:37 CEST 2009

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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