# Post hoc test in generalized linear mixed models: how to do it?

I am working with a mixed model (glmmadmb) in R for data counting. I have one random factor (locality) and one fixed factor (Habitat). The fixed rate has two levels and the random rate has seven levels. I want to make comparisons between two levels of a fixed factor in each of the seven levels of a random factor. But I don't know how to do this in R. I am very new to R. Can anyone help me? Many thanks.

This is my glmm formula for more disaggregated data:

``````    model<-glmmadmb(Species.abundance~Habitat(1|Locality:Habitat),
data=data,family='nbinom1')
```

```

I only tried this with Habitat, but it clearly doesn't account for the terrain:

``````    summary(glht(model,linfct=mcp(Habitat='Tukey')))

Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts

Fit: glmmadmb(formula = Species.abundance ~ Habitat + (1 | Locality:Habitat),
data = data, family = "nbinom1")

Linear Hypotheses:
Estimate Std. Error z value Pr(>|z|)
Fynbos - Forest == 0  -0.2614     0.2010  -1.301    0.193
(Adjusted p values reported -- single-step method)
```

```
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I would probably just do separate tests in each location and, if you like, make corrections for several comparisons. The functions from `plyr`

are handy, but not needed to do this, something like

``````library(plyr)
formula=Species.abundance~Habitat,
family="nbinom1")
p.vals <- laply(model.list,function(x) coef(summary(x))[2,"Pr(>|z|)"])
```

```

(I can't guarantee that this actually works, since you haven't given a reproducible example, and I can't think of one ...)

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