[R] Model averaging with (and without) interaction terms

[Leslie Young]

I’ve used logistic regression to create models to assess the effect of
3 variables on the presence or absence of a species, including the
interaction terms between variables and model averaging using MuMI:
model.avg

The top models (delta<4) include several models with interaction terms
and some models without; model weights are quite low for all models
(<0.25). My problem is that the models with interactions have negative
coefficients on the variables with a positive interaction term whereas
the same model without an interaction has positive coefficients.
MuMIn: model.avg averages all these models together, so the
relationship is washed out (CI overlaps 0).


Eg.

mod1<-glm(presence ~ x1*x2, family=”binomial”)
coefficients: -0.661 x1, -0.043 x2, 0.02 x1:x2

mod2 <- glm(presence ~ x1 + x2, family=”binomial”)
coefficients: 0.245 x1, 0.021 x2

I’ve read that it is difficult to compare models with and without
interaction terms, but nothing regarding how one might go about doing
so. Should interaction models be averaged differently or separately
than models without interaction terms?  Is there another way to
approach this?

Thanks in advance,
Leslie