[R-sig-ME] More naive questions: Speed comparisons? what is a "stack imbalance" in lmer? does lmer center variables?
kw.stat at gmail.com
Wed Sep 23 15:54:32 CEST 2009
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".
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
> 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:
> 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?
> Paul E. Johnson
> Professor, Political Science
> 1541 Lilac Lane, Room 504
> University of Kansas
> R-sig-mixed-models at r-project.org mailing list
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