[R-sig-ME] hurdle negative binomial models with MCMCglmm

[Zelda Van der Waal]

Many thanks Dave for this link, this is very useful!

However I can only see zero-altered models using Poisson distribution.
Is there a way to use negative binomial distribution in zero-altered models (zero-inflated and hurdle) with mixed effects in MCMCglmm?

thanks again to everyone

.zelda.

________________________________________
From: r-sig-mixed-models-bounces at r-project.org [r-sig-mixed-models-bounces at r-project.org] On Behalf Of David Atkins [datkins at u.washington.edu]
Sent: 09 December 2011 18:57
To: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] hurdle negative binomial models with MCMCglmm

Zelda--

You might take a look at a tutorial paper we have written on mixed model
count regression; it also includes R code and two datasets:

http://depts.washington.edu/cshrb/newweb/statstutorials.html

[last ms on the page]

The R code provides some details on using MCMCglmm for zero-altered
models (including zero-inflated and hurdle mixed models).

Hope it helps.

cheers, Dave


Dear all,

I am very new at using MCMCglmm, and would like to fit a hurdle negative
binomial distribution model with this package. Does anyone know whether
this is possible? as this family is not amongst the distribution
families described in the package vignette.

thanks a lot

.zelda.

--
Dave Atkins, PhD
Research Associate Professor
Department of Psychiatry and Behavioral Science
University of Washington
datkins at u.washington.edu

Center for the Study of Health and Risk Behaviors (CSHRB)
1100 NE 45th Street, Suite 300
Seattle, WA  98105
206-616-3879
http://depts.washington.edu/cshrb/
(Mon-Wed)

Center for Healthcare Improvement, for Addictions, Mental Illness,
   Medically Vulnerable Populations (CHAMMP)
325 9th Avenue, 2HH-15
Box 359911
Seattle, WA 98104
http://www.chammp.org
(Thurs)

_______________________________________________
R-sig-mixed-models at r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models