@hovicke wrote: Thanks, Paige. I guess I was confused about what proc pls actually does and did not realize variables did not have to be removed due to the multicollinearity. The objective was to find the “best” model from a set of data. I used proc glmselect for model selection and then wanted to test for multicollinearity using proc pls due to having both continuous and categorical variables and not being able to check vif with proc reg. If I did want to remove variables using the proc pls output, how would I go about doing that? Or should that not even be done to fit a “best” model due to what proc pls does?
The idea of finding a "best" model is one that isn't really defined. If you fit a lot of models to a set of data, you could chose a "best" according to some criterion, but it may not be best under some other criterion. There are lots of criteria you could possibly use, and one criteria isn't actually available in SAS, but a study showed that PLS produces lower mean square error on its predicted values and regression coefficients than other methods in the study ... meaning the model is more robust and stable.
In my opinion, the whole idea is backwards of using PLS to figure out what variable to remove from PROC REG. You don't run PLS to fix the multicollinearity problems in PROC REG. You run PLS to get a model which is robust to multicollinearity that you can use instead of the PROC REG model.
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