[R] GLM with regularization

[Dmitriy Lyubimov]

Thank you.

On Thu, Mar 1, 2012 at 9:58 AM, Bert Gunter <gunter.berton at gene.com> wrote:
> Google is your friend! -- as usual.
>
> If you had searched on "glm with regularization" you would have bumped
> into the glmnet R package, which I think is what you're looking for.
>
> -- Bert
>
> On Wed, Feb 29, 2012 at 6:22 PM, Dmitriy Lyubimov <dlieu.7 at gmail.com> wrote:
>> 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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>
>
>
> --
>
> Bert Gunter
> Genentech Nonclinical Biostatistics
>
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