[R] GLM with regularization

[Dmitriy Lyubimov]

Hello,

Thank you for probably not so new question, but i am new to R.

Does any of packages have something like glm+regularization? So far i
see probably something close to that as a ridge regression in MASS but
I think i need something like GLM, in particular binomial regularized
versions of polynomial regression.

Also I am not sure how some of the K-fold crossvalidation helpers out
there (cv.glm) could be used to adjust reg rate as there seems to be
no way to apply them over data not used for training (or  i am not
seeing a solution here as training is completely separated from
crossvalidation error computation here) .

The example here in cv.glm doesn't look right to me since it computes
cv error over model trained on 100% of data. (e.g. wikipedia
crossvalidation article lists this as an example of misuse of K-fold
CV).


----- doc quote ----
# leave-one-out and 6-fold cross-validation prediction error for
# the mammals data set.
data(mammals, package="MASS")
mammals.glm <- glm(log(brain)~log(body),data=mammals)
cv.err <- cv.glm(mammals,mammals.glm)
cv.err.6 <- cv.glm(mammals, mammals.glm, K=6)
---- end of quote ---


Those seem to be pretty common techniques, any poniter in the right
direction (package) will be greatly appreciated.

thank you very much.
-Dmitriy