I am struggling with a ZIGLMM in R. I have a data set on freshwater plant propagules (response variable) and the relation with the ecological state of ponds. Pond state is a categorical factor with three levels. I have three measurements (propagule counts per liter sediment) per pond, hence the mixed model with pond as a random factor. Based on histograms and the assumption of dispersal barriers creating excess zeros, I suspect a zero-inflated model suits my data best.
I am using package
glmmadmb as described by Bolker et al. 2012 (“Owls example”):
Propagules <- glmmadmb(formula = Propagules ~ Category + (1|Pond), data = `ZIGLMM.input`, family = "nbinom", zeroInflation = TRUE) AIC: 483.9 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 3.355 0.237 14.13 <2e-16 *** CategoryClear/low SM -2.292 0.648 -3.54 0.0004 *** CategoryTurbid -17.116 316.930 -0.05 0.9569 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
I have a number of questions:
- Could AIC be used to compare different models with identical factor complexity? I mean, to select between e.g. a Poisson or Negative Binomial distributed model? Or is AIC only relevant when shopping through models with different complexity in terms of number of included factors? Also, would QAIC be a better choice?
- Graphically (see below), I am not convinced there is no significant difference between ecological state ‘Clear/High SM’ (i.e. my reference) and ‘Turbid’ state. Am I seeing this wrong? I guess the correction for zero-inflation makes the use of box plots less recommended? I have the impression that the large Standard Error for ‘Turbid’ results from a few (ecologically relevant) outliers.
- How can I change my reference level in order to compare ‘Clear/low SM’ with ‘Turbid’? Should I use Tukey tests as included in the
multcomppackage? I’m guessing this is not a valid approach for ZIGLMM. I tried adjusting the category names to switch the alphabetical order, but it didn’t work.
Thank you very much for any kind advice.