[R-sig-ME] GLMMs with Adaptive Gaussian Quadrature - GLMMadaptive 0.2-0
@t@nleychri@topher1 @ending from y@hoo@com
Wed Jul 4 10:48:21 CEST 2018
I can't wait to check flavors of the new features.
On Wednesday, July 4, 2018, 10:44:23 AM GMT+2, D. Rizopoulos <d.rizopoulos using erasmusmc.nl> wrote:
Dear R mixed-model users,
A new version of GLMMadaptive (0.2-0) has been rolled out on CRAN.
Summary: GLMMadaptive can fit mixed effects models using adaptive
Gaussian quadrature to approximate the integrals over the random
effects, allowing also for user-specified models.
- Zero-inflated Poisson and negative binomial models are now implemented
using the family objects zi.poisson() and zi.negative.binomial(),
respectively. In addition, taking into advantage of the fact that users
can specify their own log density functions for the outcome, two-part /
hurdle model can also be implemented. For examples, check the vignette:
- The predict() method is now fully available. It calculates
predictions, and standard errors for models returned by mixed_model() at
o "mean subject": only the fixed effects part corresponding to
predictions for the average subject (but not population averaged
predictions in case of nonlinear link functions).
o "marginal": predictions using the marginalized coefficients that
correspond to population averaged predictions.
o "subject specific": predictions at the subject level. These can be
also calculated for subjects not originally in the dataset (i.e.,
estimates of the random effects are internally obtained).
- The simulate() method is available to simulate data from fitted mixed
models. This can be used for instance to perform replication / posterior
predictive checks. More info in the vignette: https://goo.gl/nKK8kt
As always, any kind of feedback is more than welcome.
Professor of Biostatistics
Department of Biostatistics
Erasmus University Medical Center
Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands
Web (personal): http://www.drizopoulos.com/
Web (work): http://www.erasmusmc.nl/biostatistiek/
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