I sent this a few days ago but it looks like the text may have been hidden.
Apologies if you saw it and ignored!

I have a glmer model with potential collinearity problems, so I'd like to
estimate the Variance Inflation Factor for each of my fixed effects.
Technically this doesn't seem challenging, but there are choices to make
and it's not clear to me which one is best.

I can calculating VIF for each fixed effect as 1/(1-R^2_i), where R^2_i is
the R^2 value from a model with the the ith fixed effect as the dependent
variable and all others as independent variables.

Since the original model is mixed effects, I feel it would be appropriate
to include the random effects in each of the VIF models. Fortunately it's
now easy to get R^2 values from mer objects using the formulae in Nakegawa
& Schielzeth (2012), as implemented by the r.squaredGLMM() function in the
MuMIn package.

The question is whether it would be more appropriate to use the marginal or
conditional R^2 value (the latter incorporates variance attributable to
random effects). Any opinions?

Best wishes, Nick

-- 
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