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 ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. |
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 <[hidden email]> 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 > > ______________________________________________ > [hidden email] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. -- Bert Gunter Genentech Nonclinical Biostatistics Internal Contact Info: Phone: 467-7374 Website: http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. |
Thank you.
On Thu, Mar 1, 2012 at 9:58 AM, Bert Gunter <[hidden email]> 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 <[hidden email]> 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 >> >> ______________________________________________ >> [hidden email] mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. > > > > -- > > Bert Gunter > Genentech Nonclinical Biostatistics > > Internal Contact Info: > Phone: 467-7374 > Website: > http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. |
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