I believe you can only do it with a continuous outcome in SAS if you don't have the DataMiner platform. Is that what you want to do? I haven't used SAS, but I am sure I can help. I have done it with binary outcome a bunch in R.
It is a process for feature selection, so model building. Ideally, you use it for figuring out terms using a training set and then fit that model to a holdout set. Do you have enough data to do that? You dont have to do it like that, but it drastically helps for generalizability.
If I get time this weekend i will see about setting up a n example using work.heart set.
I've used lasso in the caret package (in r) if it's any help. I've used it in the context of predictive modeling and the regression coefficients shrink to 0. Kind of plays a role in variable selection.
For cross sectional data I don't use models to predict, just to show relationships between variables.
For time series I think there is agreement that multivariate approaches have little value to predict relative to univariate models [or at best add little] so I will not be using multivariate approaches to predict, only to show relationships between variables.
Running out of time this weekend. I am sure you saw this:
of note, you can just jam all variables into model and it will identify the true/best model. It just optimizes given presented terms. So for example, if you put effects of both covariates and the outcome, the model doesnt know this and will treat them as a covariate.
In this modified optimization there is a tuning parameter, lambda. When lamba = 0 we get the ols beta coefficients. But sometimes lambda is tuned such that the betas are exactly zero and this is due to the constraint region. Basically, lasso is a trade-off between bias and variance. We trade more bias for less variance in our predictions. I think ridge regression can be used to mitigate multicollinearity.
". It is necessary to standardize variables before using Lasso and Ridge Regression. Lasso regression puts constraints on the size of the coefficients associated to each variable. However, this value will depend on the magnitude of each variable. The result of centering the variables means that there is no longer an intercept. This applies equally to ridge regression."
If you get rid of the intercept how do you deal with reference levels of the dummy variables?
One thing I am not clear on is if you chose the variables with LASSO, then run those variables through the OLS model. Do you need to check regression assumptions when running LASSO (like normality, equal error variance).
This is on page 5 of this article about LASSO. It made me wonder....