Joris,
Ridge regression is a type of regularized estimation approach. The objective
function for least-squares, (Y - Xb)^t (Y - Xb) is modified by adding a
quadratic penalty, k b^t b. Because of this the log-likelihood value (sum of
squared residuals), for a fixed k, does not have much meaning, and is not really
useful. However, a key issue in such regularized estimation is how to choose
the regularization parameter "k". You can see that lm.ridge gives two
different ways to estimate k (there are other ways).
Ravi.
____________________________________________________________________
Ravi Varadhan, Ph.D.
Assistant Professor,
Division of Geriatric Medicine and Gerontology
School of Medicine
Johns Hopkins University
Ph. (410) 502-2619
email: rvaradhan at jhmi.edu
----- Original Message -----
From: joris meys <jorismeys at gmail.com>
Date: Tuesday, March 17, 2009 7:37 pm
Subject: [R] Likelihood of a ridge regression (lm.ridge)?
To: R-help Mailing List <r-help at r-project.org>
> Dear all,
>
> I want to get the likelihood (or AIC or BIC) of a ridge regression model
> using lm.ridge from the MASS library. Yet, I can't really find it. As
> lm.ridge does not return a standard fit object, it doesn't work with
> functions like e.g. BIC (nlme package). Is there a way around it? I would
> calculate it myself, but I'm not sure how to do that for a ridge
regression.
>
> Thank you in advance
> Kind regards
> Joris
>
> [[alternative HTML version deleted]]
>
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