My study has a complicated design and I am not sure if I am modeling my zero-inflated data correctly. I have seed abundances and seedling abundances for 11 species. I have one main "treatment" with four levels (C, P, I, R). For the sampling design: we sampled at 5 sites. At each site, each of the 4 treatments were replicated 4 times (e.g. PA, PB, PC, PD). So there are 4 replicates of 4 treatments nested in 5 sites (so Site is my random factor). The response variable (one species' seed abundances) are zero-inflated (count data). I checked for overdispersion by running a
glm and dividing the residual deviance by the degrees of freedom (I believe this is the right approach?). All variables were overdispersed (ratio greater than 1); so I went with negative binomial, instead of Poisson, distributions. I've tried two ways of modeling this:
glmmadmb. But after learning
zeroinfl is not useful for mixed models, I am trying to model with glmmADMB:
glmmNB <- glmmadmb(CON_XAL~Treatment+(1|Site), data = SR.year.raw, zeroInflation=TRUE, family="nbinom")
My outcome looks like this:
Summary of my response variable:
summary(SR.year.raw$CON_XAL) Min. 1st Qu. Median Mean 3rd Qu. Max. (0.0) (0.0) (0.0) (232.1) (7.5) (6245.0)
My questions are:
- Am I specifying the random effect correctly in the glmmadmb model?
- What post-hoc tests can I do with glmmADMB to look at how treatments differ from one another? For example, I have a treatment called "control". But in the model output, there is no significance value for the control. So I suppose the output is saying that the "island" treatment is significantly different from the control. But how do I know if the "Island" treatment is significantly different from the "plantation" treatment?
- Can you recommend some ways of graphing the results, i.e., something I ultimately could include in my paper? I ask this because from everything I have read, all of the example graphs are for comparing different models (Poisson vs ngbinom), but I can't seem to find code for a good final conclusions graph.
- Is the warning message a problem? Can I ignore it?