# R: testing for interactions and significance in GLM model

I fit a model by using fit<-glm(...). Then I perform test to see if I can exclude any variables: drop1(fit, test="Chisq))

I base my conclusion only on the p values that is returned in the drop1 function. If some variable have large PR(>Chi) values, I will let them out of the model.

I do something similar when I am testing if there are interaction. I use add1(fit.~.^2, test="Chisq"). If there any small values for PR(>Chi) I include them in my model.

However, I do those things as a routine. What is the theoretical explanation? how does the tests look mathematically?

• Without knowing your scientific question it is hard to see whether this is a productive strategy. Feb 19 '17 at 22:02