[R-sig-ME] Slight differences in fitted coefficients in lme4_1.0-6 compared to lme4_0.999999-2

[Steve Walker]

The development version of lme4 on github now has the default optimizer 
switched back to bobyqa.  To explicitly set the optimizer, use something 
like:

fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy,
             control = lmerControl(optimizer = "bobyqa"))


Cheers,
Steve


On 2/7/2014, 5:31 PM, Tom Wenseleers wrote:
> One thing I noticed is that the intercepts of my models seem quite different from what I got before. Is the new version not using dummy coding by default or something? Or where could that come from? @Jake: how do I specify they bobyqa optimizer actually?
>
> Cheers,
> Tom
>
> -----Original Message-----
> From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Jake Westfall
> Sent: 07 February 2014 20:10
> To: r-sig-mixed-models at r-project.org
> Subject: Re: [R-sig-ME] Slight differences in fitted coefficients in lme4_1.0-6 compared to lme4_0.999999-2
>
> Not sure if this thread is the time/place for me to bring this up, but here goes... I *routinely* find that the new Nelder-Mead optimizer in lme4 >= 1.0 provides worse solutions than the old bobyqa optimizer -- "worse" in the sense that, comparing the same model fitted to the same dataset using NM vs. bobyqa, the coefficients are noticeably different and deviance for the former model is noticeably higher. When I switch to bobyqa I pretty much reproduce the results of my models fitted under lme4 < 1.0... and bobyqa is faster too! At this point, I've gotten to where I just always instruct lme4 to use bobyqa and don't even check anymore to see what Nelder-Mead comes up with. One very important thing to mention here is that the overwhelming majority of models that I fit involve crossed random effects. So maybe the new Nelder-Mead optimizer fairly consistently outperforms bobyqa for nested random effects models, and this is the motivation for making it the new lme4 default, but!
  i!
>   n my experience, for the kind of models that I fit, bobyqa pretty much always does better.
>
> Jake
>
>> Date: Fri, 7 Feb 2014 15:51:44 -0500
>> From: bbolker at gmail.com
>> To: r-sig-mixed-models at r-project.org
>> Subject: Re: [R-sig-ME] Slight differences in fitted coefficients in
>> lme4_1.0-6 compared to lme4_0.999999-2
>>
>> On 14-02-07 03:34 PM, Tom Wenseleers wrote:
>>> Dear all, I noticed that I get very slight differences in my current
>>> lme4 1.0-6 models compared to the old ones I obtained earlier using
>>> lme4_0.999999-2. I was just wondering whether it would somehow still
>>> be possible to reproduce the output of the old lme4_0.999999-2, by
>>> setting appropriate options of the optimizer to use etc? Or is this
>>> not possible? I also tried installing the old lme4 version using
>>> install_url in package devel, but if I try this I get a complaint
>>> that the old version doesn't work with R.0.2. Any easy way to go
>>> back to the old version (I need this to be able to fully reproduce
>>> published results)?
>>>
>>
>>    I think you should be able to install lme4.0 from
>> http://lme4.r-forge.r-project.org/repos/  to reproduce previous outputs.
>>   You *might* be able to reproduce previous results by setting
>> control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")),
>> but I don't think we could guarantee that -- too much of the internal
>> machinery has changed too radically.
>>
>>    Ben Bolker
>>
>>
>>
>>> Cheers, Tom
>>>
>>>
>>>
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>>>
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>>
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