[R-sig-ME] GLMMs with Adaptive Gaussian Quadrature
d@rizopoulo@ @ending from er@@mu@mc@nl
Fri Jun 15 18:34:32 CEST 2018
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.
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.
> Professor of Biostatistics
> Erasmus University Medical Center
> The Netherlands
> [[alternative HTML version deleted]]
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