I have a small data set of counts of bees.
I tried a simple Poisson model without random effects but it was very overdispersed (3.95). When I fit a GLMM with random effects (using glmer in lme4) it was then underdispersed (0.19). I need to include the random effects because of my set-up.
The data is distributed like this, with lots of zeroes and two large values:
In addition, most of the zeros occur in one level of the main predictor variable, like this:
with(SB, table(solbee, treatment) )
I tried a negative binomial mixed model (glmer.nb in lme4) which was a better fit but still not right, also ZIP and ZINB (using package glmmADMB) but none are a good fit. I tried a hurdle model in glmmADMB but got the following error message:
hurdle1 <- glmmadmb(solbee ~ treatment + landuse + snh + (1|site/dayfac), data=subset(SB,solbee>0), family="truncnbinom1")
Error in model.frame.default(formula = solbee ~ treatment + landuse + : variable lengths differ (found for 'treatment')
I think this is because most of the zero counts are in one level of the treatment factor.
Any advice as to what to try next would be very welcome, as would any info on why my data is overdispersed without random effects, and underdispersed with random effects.