I fit a model by using
fit<-glm(...). Then I perform test to see if I can exclude any variables:
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?