I had posted this in the SAS Macro board, and was told this might've been a better place for this question. It seems like my question was answered, but I wanted to see if this board had any other input.
With Automated Model Selection (best subsets, forward selection, etc.), I'm having trouble with datasets that have higher order terms or interaction terms. How can I manipulate SAS into taking these variables into consideration? (ie, if Variable 'x' discarded in backwards elimination, then it has to discard Variable 'x^2' and Variable 'x*y')
First, I'd like to make a recommendation on another statistical technique that many find preferable to stepwise regression or best subsets. The problem is that Least Squares just isn't that good a modelling technique when you have many X variables and they are highly correlated with one another. There are many critiques of stepwise and best subsets on the Internet (and elsewhere) in these situations. Here are some. A better technique is Partial Least Squares regression, which in SAS is PROC PLS.
If you absolutely have to use PROC REG and want interactions and polynomial terms in the model, first create an X matrix in PROC GLMMOD to represent your main effects, interactions and polynomial terms, and then run that through PROC REG. I will note that I have never had much success doing things this way, hence the PROC PLS recommendation.
Message was edited by: Paige