Hi all,

I am modeling association patterns among a community of chimpanzees using a
logistic mixed effects model in the MCMCglmm package. When I run my model,
I get the following warning message:

Warning message:
In summary.glm(glm(cbind(MCMC_y, MCMC_y.additional) ~ 1, family =
"quasibinomial",  :
  observations with zero weight not used for calculating dispersion

I can't tell if this warning is related to zero-inflation, overdispersion,
or something else. Also, I'm not sure 1) if this is a serious concern, or
2) how to deal with this in the MCMCglmm framework.

Below is the code for my model:

#priors:
priors1<- list(R = list(V = 1, nu = 0.002), G = list(G1 = list(V =1, nu =
0.002), G2 = list(V =1, nu = 0.002)))
#model:
m1<- MCMCglmm(cbind(Suc_5,Fa_5)~
Related+Sex.Estrus+FamilyDiff+RankDiffNew+EstrusAd3*Age, random =
~idv(mult.memb(~Actor1+Actor2))+PID, family="multinomial2", data=dat,
verbose = FALSE, prior=priors1, saveX=TRUE, saveZ=TRUE, pr=TRUE)

*The response variable uses Suc_5 (two IDs observed together) and Fa_5 (two
IDs not observed together) to calculate pairwise association indices.
*The fixed effect predictor variables are all categorical and measure
pair-wise attributes about two individuals in a pair (with the exception of
EstrusAd3, which is a temporal variable - the number of females in
estrus/month).
*Random effects include animal ID (i.e., the multi-member component) and
pair ID (PID).

Thanks for your feedback!
Julie

-- 
Julie Rushmore
DVM/PhD Candidate
Odum School of Ecology
University of Georgia

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