I'm doing some linear modeling and am new to the ridge/lasso/elasticnet procedures. In my case I have N>>p (p=15 based on variables used in past literature and some physical reasoning) so my understanding is that I should be interested in ridge regression to avoid the issue of multicollinearity of predictors. Lasso is useful when p>>N.
In the past I have performed step-wise regression with stepAIC in both directions to choose my variables and then used VIF to determine if any of these variables are correlated. My understanding is that ridge regression is a more robust approach for this workflow. Reading the glmnet_beta vignette, it describes the alpha parameter where alpha=1 is a lasso regression and alpha=0 is a ridge regression. Farther down the authors suggest a 10 fold validation to determine an alpha value and based on the plots shown, say that alpha=1 does the best here. However, all the models look like they approach the same MSE and alpha=0 is the lowest curve for all lambda (but maybe this second point doesn't matter?). With my data I get a very similar looking set of curves so I'm trying to decide if I should stick with alpha=1 instead of alpha=0. Is there a way to extract MSE for a lambda, e.g. lambda.1se? Any advice or clarification is appreciated. Thanks. Dominik [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.