[R-sig-ME] Why do lme() models runs so fast and converge as compared to lmer()?

[Ben Bolker]

    I'm still a little surprised that lme4 is so slow.  Is there a 
possibility that you're using a parallelized BLAS and it's 
over-parallelizing (i.e., hogging lots of memory)?  What is the memory 
usage and CPU performance during your runs (i.e. does it look like 
memory is getting overcommitted and/or lots of threads are running 
simultaneously)?

  e.g. see https://github.com/lme4/lme4/issues/627 ; 
https://github.com/lme4/lme4/issues/726

    lme, lmer, and glmmTMB are all well-respected as far as I know (the 
least-cited and newest of these, glmmTMB, currently has more than 5000 
Google Scholar citations ...)

On 2023-09-18 7:33 a.m., Santosh Srinivas wrote:
> Thank you, Dr. Bolker. The advice to set calc.derivs = FALSE is much 
> appreciated.
> 
> We tried glmmTMB() for the same model and found it quite fast. Models 
> converged with default optimizers.
> 
> Following this, we also tried Julia and were surprised by the 
> orders-of-magnitude improvement in speed.
> 
> We are tending to stick to R and use glmmTMB(). We hope that academic 
> journals are generally receptive to using this package for hypothesis 
> testing. From a cursory Google scholar search, it appears that glmmTMB() 
> is used/cited less often (and Julia's MixedModels far and few) as 
> compared to lme() and lmer().
> 
> Assuming that models are specified identically, I am hoping the results 
> for hypothesis testing (e.g., significance of a coefficient of 
> theoretical importance) should be identical regardless of whether one 
> uses R's lme(), lmer(), or glmmTMB(), or Julia's MixedModels().

   Yes, they should. There are some details such as whether a 
finite-size correction (e.g. Satterthwaite/Kenward-Roger) is applied, 
but given the size of your data set I doubt that's much of an issue)

> 
> On this note, anything that you suggest we state explicitly in the 
> journal manuscript when presenting the results using glmmTMB() as 
> opposed to lme() or lmer()?
> 
> Thanks!
> sbs
> ------------------------------------------------------------------------
> *From:* R-sig-mixed-models <r-sig-mixed-models-bounces using r-project.org> on 
> behalf of Ben Bolker <bbolker using gmail.com>
> *Sent:* Monday, September 18, 2023 2:11 AM
> *To:* r-sig-mixed-models using r-project.org <r-sig-mixed-models using r-project.org>
> *Subject:* Re: [R-sig-ME] Why do lme() models runs so fast and converge 
> as compared to lmer()?
>      This is mildly surprising but not impossible. I haven't looked into
> it/run any experiments yet, but:
> 
>    * lme handles a more restricted range of model types, so it's possible
> that its algorithm is faster on simple examples;
> 
>    * 'doesn't converge' may not be the issue you think it is.  I would
> improve performance and turn off convergence checking that is known to
> be dodgy for large data sets via control = lmerControl(calc.derivs =
> FALSE)), and more generally try these suggestions:
> https://rdrr.io/cran/lme4/f/vignettes/lmerperf.Rmd 
> <https://rdrr.io/cran/lme4/f/vignettes/lmerperf.Rmd>
> 
>     cheers
>      Ben Bolker
> 
> On 2023-09-17 5:21 a.m., Santosh Srinivas wrote:
>> Hi, I am running the following similar models using lme() and lmer():
>> 
>> f = y ~ x + year * post_event + (year|user_id)
>> 
>> where,
>> 
>>    *   year (integer) ranges from 0 (for the year 2010) to 10 (for the year2020);
>>    *   post_event (factor variable) is 1 for years 2013 onwards, and 0 otherwise;
>>    *   Number of Observations: 3586633; and
>>    *   Number of Groups: 1109.
>> 
>> The lme4's lmer() runs for several minutes and never succeeds to converge with any optimizer I try, whereas the former seems to converge in a few minutes.
>> 
>> Not sure if I am doing anything wrong, or whether such convergence and performances are generally expected of lme().
>> 
>> Request your help.
>> 
>> Thanks & regards,
>> sbs
>> 
>> 
>> 
>> 
>>        [[alternative HTML version deleted]]
>> 
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> -- 
> Dr. Benjamin Bolker
> Professor, Mathematics & Statistics and Biology, McMaster University
> Director, School of Computational Science and Engineering
> (Acting) Graduate chair, Mathematics & Statistics
>   > E-mail is sent at my convenience; I don't expect replies outside of
> working hours.
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-- 
Dr. Benjamin Bolker
Professor, Mathematics & Statistics and Biology, McMaster University
Director, School of Computational Science and Engineering
(Acting) Graduate chair, Mathematics & Statistics
 > E-mail is sent at my convenience; I don't expect replies outside of 
working hours.