GLMMs with Adaptive Gaussian Quadrature
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 [[alternative HTML version deleted]]
_______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models