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 ______________________________________________ R-help@r-project.org 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.
