[R-sig-ME] model estimates in glmmTMB

[Thomas Merkling]

Hi Stephanie,

First, I think you should try to simplify your model (unless you have 
good reasons to keep the non-significant variables in your model), this 
will likely influence the other estimates. Also, I wouldn't rely on the 
p-values from the summary to assess the significance of your variables. 
As for your question about back-transforming to interpret the effect of 
a variable, I don't think you can just use exp(estimate)  as the 
estimate is part of a model equation involving all the parameters (e.g. 
intercept + beta1 * Grass + beta2 * AllFlowers ...) and the estimates 
are also influenced by the scale of the response variable. I usually use 
the predict function to graphically plot the effect of a given variable. 
If you're interested in knowing if a given variable has a stronger 
effect than another one, I'd recommend standardising your continuous 
variables, so that they are all on the same scale.

A negative intercept would mean that the baseline level is somewhere 
between 0 and 1 (but still positive!), as exp(-1) = 0.36.. for example.

Hope that helps,
Thomas

-- 
Dr Thomas Merkling
Website <https://sites.google.com/site/merklingthomas/>
LinkedIn Profile <https://www.linkedin.com/in/thomas-merkling-a286b6a1>

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