I'm new to checking the VIF value for a glm model so I just want to make sure i"m understanding this correctly.
I have 4 predictors for my count model and the model looks like this:
model1<-glm(Number~dts+dss+dtn+dsn, family=poisson, data=birds)
I then checked the collinearity on the model using the car::vif function and got this output;
dts dss dtn dsn 2.261840 2.281326 2.016644 2.073556
so from my understanding and reading online, due to all 4 being below vif of 3, then there is no multicollinearity and I can now proceed with finding the "simplest" model.
After I did this, i then performed the "summary" function, and found 3 out of the 4 to be significant, the model would then become;
dts, dss, dsn
Would that therefore mean that my best model would be;
glm(Numer~dts+dss+dsn, family=poisson, data=bird)
or am i completely misunderstanding the car:vif function