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>
Sent: Friday, June 15, 2018 7:57 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
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 at 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 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 ?
On Fri, Jun 15, 2018 at 10:26 AM, D. Rizopoulos <
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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