[R-sig-ME] GLMMs with Adaptive Gaussian Quadrature

[Ben Bolker]

Good point.  Extending AGQ to more complex models in lme4 is something
that's been on my list for a long time, but it's great to see someone
meeting the need.  Even if I or someone does eventually get it working
in lme4, two implementations are always better than one ...

  For those interested in this topic, there are a few other approaches
to improved frequentist estimates (i.e. without going full-Bayesian)
that are implemented in R:  Helen Ogden's glmmsr package implements
sequential reduction and importance sampling methods, The glmm and
bernor packages use other flavors of importance sampling/MC likelihood
approximations. glmmADMB has importance sampling; TMB (the engine
underlying glmmTMB) has an importance-sampling method, but it hasn't
(yet) been integrated in glmmTMB ...

  cheers
    Ben Bolker


On Fri, Jun 15, 2018 at 12:34 PM, D. Rizopoulos
<d.rizopoulos using erasmusmc.nl> wrote:
> AFAIK, lme4::glmer with nAGQ>1 *only* works for scalar random effects. At least, when I try setting nAGQ > 1 for a random intercepts and random slopes model in lme4::glmer (lme4_1.1-17)  I get the error message:
>
> Error in updateGlmerDevfun(devfun, glmod$reTrms, nAGQ = nAGQ) :
>   nAGQ > 1 is only available for models with a single, scalar random-effects term
>
> GLMMadaptive::mixed_model implements the AGQ in such settings.
>
> My main motivation to create this package is the longitudinal data analysis setting in which including something more than random intercepts is very typical. At least the students in my Repeated Measurements course (https://github.com/drizopoulos/Repeated_Measurements) have had some difficult times getting lme4::glmer() with a Laplace approximation to work in such cases.
>
>
> -----Original Message-----
> From: Ben Bolker <bbolker using gmail.com>
> Sent: Friday, June 15, 2018 5:07 PM
> To: D. Rizopoulos <d.rizopoulos using erasmusmc.nl>
> Cc: r-sig-mixed-models using r-project.org
> Subject: Re: [R-sig-ME] GLMMs with Adaptive Gaussian Quadrature
>
> It looks interesting (at an admittedly *very* quick initial glance).
> Can you clarify how it differs from using lme4::glmer with nAGQ>1 ?
>
> On Fri, Jun 15, 2018 at 10:26 AM, D. Rizopoulos <d.rizopoulos using erasmusmc.nl> wrote:
>> Dear R mixed-model users,
>>
>> I’d like to announce the release of my new package GLMMadaptive for
>> fitting generalized linear mixed models using adaptive Gaussian
>> quadrature. You may read more about it here: https://goo.gl/7pi8Sh
>>
>> Any comments or suggestions are more than welcome.
>>
>> Best,
>> Dimitris
>>
>>
>> Professor of Biostatistics
>> Erasmus University Medical Center
>> The Netherlands
>>
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>>
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