GLMMs with Adaptive Gaussian Quadrature

Yes, this is in my plans to include in future versions of the package ? For now I?m focusing on finalizing/extending the methods for the standard generics. Most notably, including subject-specific (dynamic) predictions with standard errors in the predict() method. The development version of the package is on my dedicated GitHub repo.

Best,
Dimitris


From: Christopher Stanley <stanleychristopher1 at yahoo.com>
Sent: Saturday, June 16, 2018 10:09 AM
To: Sean MacEachern <sean.maceach at gmail.com>; D. Rizopoulos <d.rizopoulos at erasmusmc.nl>
Cc: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] GLMMs with Adaptive Gaussian Quadrature

Dimitris, the flexibility sounds great. Will this package allow users to specify zero-inflated (poisson/negative binomial) count data as well?

Best
Christopher

No, this is not currently possible. I will need to think if it can be ?easily? incorporated in the package?

Best,
Dimitris


From: Sean MacEachern <sean.maceach at gmail.com<mailto:sean.maceach at gmail.com>>
Sent: Friday, June 15, 2018 9:37 PM
To: D. Rizopoulos <d.rizopoulos at erasmusmc.nl<mailto:d.rizopoulos at erasmusmc.nl>>
Cc: bbolker at gmail.com<mailto:bbolker at gmail.com>; r-sig-mixed-models at r-project.org<mailto:r-sig-mixed-models at r-project.org>
Subject: Re: [R-sig-ME] GLMMs with Adaptive Gaussian Quadrature

Looks interesting. Would it be possible to fit a Numerator relationship matrix as a random effect similarly to MCMCglmm or Asreml for binary or categorical datasets?

Regards,

Sean MacEachern

Indeed! GLMMadaptive::mixed_model is also more flexible in allowing users to define their own mixed models by specifying the log-density of the repeated measurements outcome, i.e., something similar to what Proc NLMIXED in doing in SAS. More info in the vignette: https://cran.r-project.org/web/packages/GLMMadaptive/vignettes/Custom_Models.html

Best,
Dimitris


-----Original Message-----
From: Ben Bolker <bbolker at gmail.com<mailto:bbolker at gmail.com><mailto:bbolker at gmail.com<mailto:bbolker at gmail.com>>>
Sent: Friday, June 15, 2018 7:57 PM
To: D. Rizopoulos <d.rizopoulos at erasmusmc.nl<mailto:d.rizopoulos at erasmusmc.nl><mailto:d.rizopoulos at erasmusmc.nl<mailto:d.rizopoulos at erasmusmc.nl>>>
Cc: r-sig-mixed-models at r-project.org<mailto:r-sig-mixed-models at r-project.org><mailto:r-sig-mixed-models at r-project.org<mailto:r-sig-mixed-models at r-project.org>>
Subject: Re: [R-sig-ME] GLMMs with Adaptive Gaussian Quadrature

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

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 at gmail.com<mailto:bbolker at gmail.com><mailto:bbolker at gmail.com<mailto:bbolker at gmail.com>>>
Sent: Friday, June 15, 2018 5:07 PM
To: D. Rizopoulos <d.rizopoulos at erasmusmc.nl<mailto:d.rizopoulos at erasmusmc.nl><mailto:d.rizopoulos at erasmusmc.nl<mailto:d.rizopoulos at erasmusmc.nl>>>
Cc: r-sig-mixed-models at r-project.org<mailto:r-sig-mixed-models at r-project.org><mailto:r-sig-mixed-models at r-project.org<mailto:r-sig-mixed-models at 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 at erasmusmc.nl<mailto:d.rizopoulos at erasmusmc.nl><mailto:d.rizopoulos at erasmusmc.nl<mailto:d.rizopoulos at 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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