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 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.