However, see notes in the vignette about zero-inflation: when fitting GLMMs with variable dispersion, zero-inflation often shows up as underdispersion, so the most reliable test is usually to run a model selection with ZIP against standard model.Ī2: When running a Poisson GLMM with count data, you absolutely have to check for overdispersion!!! Fitting a poisson without check is a big no no. The DHARMa R package (disclaimer: I develop this package) has a zero-inflation test for GLMMs, including glmmTMB, that you can use to test your model. Without having fit a model, you can't know what to expect.
Zero-inflation means you have more zeros than you would expect, given your fitted model. How do I account for D: and E: (the unbalanced design)?Ī1: "All in all, I have about 33% of the dates having counts of zero, which makes me think the data is zero inflated." -> this is a common misconception - zero-inflation != lots of zeros.
See histogram:ī: Sites in localities might show variation in intercepts due to higher initial snail abundanceĬ: Sampling duration differed (5-33 minutes), which will most likely influence countsĭ: The number of sites per locality is unbalanced (one locality with only 1 site, overall range per locality: 1-9)Į: The total number of sampling dates per locality is unbalanced (overall range per locality: 19-35)Ī: Use glmmTMB to account for the zero inflationī: Include site as a random intercept, to account for variation in counts between the sitesĬ: Include sampling duration as an offset, to account for differences in sampling effort The current model: model <- glmmTMB(snail_count ~ (1|site) + locality + pH + locality*pH + offset(log(duration)), Which makes me think the data is zero inflated. water pH)Ī: All in all, I have about 33% of the dates having counts of zero, Test if snail counts differ between localities, and test influence of environmental factors (e.g. So, in each locality the snail counts come from several different sites, repeatedly sampled on different dates.
I have count data of snails per date, counted over many dates at sites, nested in localities.
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