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I am trying to analyze data from an experiment in which I measured the learning of a colour preference in birds under two treatments. 40 Individuals were organized into 8 groups, and 4 groups were assigned to each treatment (i.e. individuals experienced only one treatment). I ran 70 trials which gave me 14 repeated observations (trials) on each individual (individuals were not measured in every trial but were focals once in every 5 trials).

I’m using glmer with a binomial distribution, where the response variable is the proportion of visits to feeders of the correct colour. I include Treatment and z-corrected trial number as fixed effects and group and individual as random effects, i.e.: GLMMHA12z <- glmer(cbind(Nb.correct.vis, Nb.vis.total) ~ Treat + TrialZ + (1|Group) + (1|Ind), data = d, family = binomial)

A histogram of the proportion looks like this: histogram of the proportion correct

which would suggest over dispersion, but when we look for over dispersion there seems to be under dispersion:

overdisp_fun(GLMMHA12z)

chisq ratio rdf p

155.2787661 0.2870217 541.0000000 1.0000000

So, my questions are: 1) Could the fact than we have under dispersion when we might have expected over dispersion be due to the weight given to the observations? Many of the 0 and 1 proportions are cases where the bird only made 1 visit during the trial(82% when proportion = 0 and 72% when proportion = 1), so they should receive a lower weight in the model (if I understand correctly).

2) How should I deal with the under dispersion? I have tried to add an observation level random effect, but it doesn’t change anything:

d$obs<-as.factor(1:dim(d)1) GLMMHA12zObs <- glmer(cbind(Nb.correct.vis, Nb.vis.total) ~ Treat + TrialZ + (1|Group) + (1|Ind)+(1|obs), data = d, family = binomial)

overdisp_fun(GLMMHA12zObs) chisq ratio rdf p 155.2787657 0.2875533 540.0000000 1.0000000

anova(GLMMHA12z,GLMMHA12zObs) Data: TestGroupeEssai Models: GLMMHA12z: cbind(Nb.correct.vis, Nb.vis.total) ~ Treat + TrialZ + (1 | Group) + (1 | Ind) GLMMHA12zObs: cbind(Nb.correct.vis, Nb.vis.total) ~ Treat + TrialZ + (1 | Group) + (1 | Ind) + (1 | obs) Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq) GLMMHA12z 5 985.03 1006.5 -487.51 975.03
GLMMHA12zObs 6 987.03 1012.9 -487.51 975.03 0 1 1

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This is by all means not a direct answer, I am new to GLMM and have similar problems to yourself. However according to http://ase.tufts.edu/gsc/gradresources/guidetomixedmodelsinr/mixed%20model%20guide.html which mention the overdisp_fun function overdispersion is indicated by a p-value smaller than 0.05. So according to your overdisp_fun output p-value your data is not overdispersed.

I have the opposite, a ratio which (if I am interpreting it correctly) should mean that the model is ok but a p-value smaller than 0.05 indicating overdispersion.

overdisp_fun(M2) chisq ratio rdf p 4.510896e+04 1.016242e+00 4.438800e+04 7.998600e-03

That said I am not sure if this overdispersion test is the correct way to go about checking binomial glmm models. If you got any clearer idea after posting this question, I would appreciate knowing what you found out.

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