You might want to rethink about getting model averaged coefficients. That is a bunch of nonsense if you have any multicollinearity among the predictors. Model averaged predictions might be useful.
Brian Brian S. Cade, PhD U. S. Geological Survey Fort Collins Science Center 2150 Centre Ave., Bldg. C Fort Collins, CO 80526-8818 email: ca...@usgs.gov <brian_c...@usgs.gov> tel: 970 226-9326 On Fri, Dec 13, 2013 at 10:37 AM, Stephen Jane <coachman7...@yahoo.com>wrote: > Hello, > > I am using a negative binomial distribution in glmmADMB to fit a mixed > model and then using the MuMIn package to get model averaged coefficients. > As far as I can tell, this approach gives no estimates for the variance of > the random effects. I have been taking these from the top model according > to AIC. Is there a preferred approach to getting these? > > I would be grateful for any insights. > > Stephen Jane > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
