Dear R-list, May I ask for help in interpretating the output of 'lmer' (from the lme4 package) when dealing with negative binomial data ?
I'm using the functions glm.nb (from the MASS package) and lmer (from the lme4) to fit respectively fixed-effects and mixed-effects generalized linear models to data, generated from a negative binomial distribution : count ~ Neg.Bin (mu, theta). Here is the code: ============================================================================== #Generate the data frame set.seed(2153) mydf<-data.frame(subjs=seq(1:nsubjids), counts=rnbinom(nsubjids*ntimes, size=0.5, mu=1.8)) #Model require(MASS); require(lme4) summary(glm.nb(counts~1, data=mydf)) summary(lmer(counts~1+(1|subjs), family=negative.binomial(theta=fixed.nb0$theta), data=mydf)) ============================================================================== The glm.nb output gives : mu=exp(0.5306) and theta=0.513. I use the theta estimate from glm.nb as input into lmer, and I obtain, mu=exp(0.5306). The output from lmer gives the following for the Random effects: Random effects: Groups Name Variance Std.Dev. subjs (Intercept) 3.5577e-10 1.8862e-05 Residual 7.1155e-01 8.4353e-01 number of obs: 30, groups: subjs, 10 I interprete the "subjs" component as an individual error term "e" (so, that mu=exp(0.5306)*exp(e)) with e~N(0, 3.5577e-10) ? Is this correct ? What about the 'Residual' term ? Thanks for your help, Best regards, François ______________________________________________ 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.
