# checking collinearity in a glm

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

• What is your interest in multicollinearity or even in building this model? – Dave Jul 31 '20 at 12:23
• I'm trying to see if any of those 4 predictors influence the number of birds being present. So i wanted to make sure those 4 predictors did not have any collinearity – andy Jul 31 '20 at 12:25
• Well then why not test the full model against a null model with just an intercept? – Dave Jul 31 '20 at 12:40

So I don't see much reason to omit dtn from your model.