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

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