GLMMs with Adaptive Gaussian Quadrature

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> 
Sent: Friday, June 15, 2018 5:07 PM
To: 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

It looks interesting (at an admittedly *very* quick initial glance).
Can you clarify how it differs from using lme4::glmer with nAGQ>1 ?

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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