Hello all,

I am trying to do factorial regression using lm() like this (example):

model<-lm(y ~ x1 + x2 + x3 + x4 + x1*x2*x3*x4)

The final term 'x1*x2*x3*x4' adds all possible interactions between
explanatory variables to the model. i.e. x1:x2, x1:x2:x3, etc, etc. Now, the
issue is that some of the interactions are significant and some are not.

I can manually remove features/interactions using 'update' like this:

model1<-update(model,~. - x1:x2:x4)

.... one by one as long as all the explanatory variables/features or
interactions are significant. But, this is so tedious. There must be a way
to say to R automatically  'I want to retain only significant
features/interactions' OR to do something to update(remove) all
non-significant interactions.

model2<-step(model)

..was not very helpful. Are there any options to it?

Can someone shed light on how I can do that? Can glm() or gam() or anything
else be more powerful to do this? Any help is greatly appreciated!

Many thanks,
Parthiban.

	[[alternative HTML version deleted]]

