is it possible to use:
selection=ELASTICNET ...
in PROC GLMSELECT so that no feature selection is performed (i.e. all featured are 'forced' into model)?
I don't think so. If you want all variables in the model, use SELECTION=NONE to get the OLS estimates. But I don't think you can get the elastic net estimates for the full model.
It depends on how correlated the variables are. Strong correlations can result in large standard errors of the OLS estimates due to the X`X matrix being ill-conditioned. You can use the VIF option in PROC REG to examine whether the X`X matrix is ill-conditioned.
If the VIF indicates strong correlations, you might try ridged regression in PROC REG, which is close to the Elastic Net in that it includes the quadratic penalty term. That would probably permit the closest comparison.
For other options for regression of correlated variables, see a comparison of PLS and PCR (principal component regression).
I would say that PLS is superior to PCR in the case of correlated X variables, because PCR may find components that are not good predictors of the Y variable(s), while PLS will find components of X that predict Y well (if such components exist). Studies show that PLS does well with correlated variables, compared to other methods such as stepwise regression, ridge regression and even PCR.
https://amstat.tandfonline.com/doi/abs/10.1080/00401706.1993.10485033
Don't miss out on SAS Innovate - Register now for the FREE Livestream!
Can't make it to Vegas? No problem! Watch our general sessions LIVE or on-demand starting April 17th. Hear from SAS execs, best-selling author Adam Grant, Hot Ones host Sean Evans, top tech journalist Kara Swisher, AI expert Cassie Kozyrkov, and the mind-blowing dance crew iLuminate! Plus, get access to over 20 breakout sessions.
ANOVA, or Analysis Of Variance, is used to compare the averages or means of two or more populations to better understand how they differ. Watch this tutorial for more.
Find more tutorials on the SAS Users YouTube channel.