[R-sig-ME] glmmadmb Negative binomial dispersion parameter

Paul Johnson paul.johnson at glasgow.ac.uk
Tue Feb 23 17:33:36 CET 2016

Hi Marte,

In answer to point 2 (adapted from a comment i made on the R-space FB group)...

My general approach is not to bother testing for overdispersion in GLMMs, but to assume it's there and model it as a matter of course, using either the OLRE approach with glmer (see Xavier Harrison's paper https://peerj.com/articles/616/) or negative binomial with glmmADMB (I've never used glmer.nb - the help file used to give a health warning, but I see that's gone). Modelling overdispersion is simply modelling unexplained variation at the observation level, so to fit a Poisson or binomial GLMM that doesn't allow for overdispersion is effectively assuming that the model explains all the variation, which is almost never a reasonable assumption (at least in biology). I don't agree with the approach of ignoring it if either it isn't significant or if the OD index you showed is low (< 1.3-1.4). This still seems to me a Ignoring a potentially substantial amount of variance and ignoring it could still have negative consequences such as an inflated false positive rate for tests and over-optimistic (narrow) CIs.

Including a term for overdispersion (when possible) in a GLMM is pretty much the same as including a residual error term in a simple OLS linear regression model, except that in the linear regression model there is no other source of random variation so we'd never consider leaving it out.

A caveat is that overdispersion can be caused by a mis-specified model so it's important to try to identify this before assuming that all the overdispersion is due to unexplained variation.

All the best,

On Tue, Feb 23, 2016 at 2:07 p.m., Marte Lilleeng <mlilleeng at gmail.com<mailto:mlilleeng at gmail.com>> wrote:

Hello everyone.
I wonder if you can help me with the following questions?

1) How is the Negative binomial dispersion parameter calculated in glmmadmb?

2) Is there,as for mixed effects poisson models (glmer), a *rule of thumb*
for when the dispersion parameter is representing trouble (overdispersion)?
I learned from Zuur and Ileno that for a mixed effects poisson mod the
dispersion is ok as long as it is not above 1.3-1.4(calculated this way;

E <- residuals (modelname)

pb <- length(fixef(modelname)+1) # +1 due to random intercept variance

overdisp <- sum(E^2)/(nrow(dataset)-pb)

3) How do you recommend to do the model validation for glmmadmb with
negative binomial error structure?

Best regards from
"New to mixed models with NB", Marte Lilleeng

[[alternative HTML version deleted]]

R-sig-mixed-models at r-project.org mailing list

	[[alternative HTML version deleted]]

More information about the R-sig-mixed-models mailing list