[R] glmnet vignette question

[Dominik Schneider]

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

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