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

[Sean MacEachern]

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

On Fri, Jun 15, 2018 at 12:09 PM D. Rizopoulos <d.rizopoulos using erasmusmc.nl>
wrote:

> 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 using gmail.com>
> Sent: Friday, June 15, 2018 7:57 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
>
> 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
> >>
> >>         [[alternative HTML version deleted]]
> >>
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