Please note, per the posting guide linked below: "*Questions about statistics:* The R mailing lists are primarily intended for questions and discussion about the R software. However, questions about statistical methodology are sometimes posted. If the question is well-asked and of interest to someone on the list, it *may* elicit an informative up-to-date answer. See also the Usenet groups sci.stat.consult (applied statistics and consulting) and sci.stat.math (mathematical stat and probability). " -- also stats.stackexchange.com
Also: "For questions about functions in standard packages distributed with R (see the FAQ Add-on packages in R <https://cran.r-project.org/doc/FAQ/R-FAQ.html#Add-on-packages-in-R>), ask questions on R-help. If the question relates to a *contributed package* , e.g., one downloaded from CRAN, try contacting the package maintainer first. You can also use find("functionname") and packageDescription("packagename") to find this information. *Only* send such questions to R-help or R-devel if you get no reply or need further assistance. This applies to both requests for help and to bug reports." -- see also ?maintainer Your query seems to be mostly statistical in nature and certainly about a non-standard package (glmnet), so if you do not get a useful response here within a few days -- you might despite the above -- try the above. Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Tue, Feb 23, 2021 at 1:07 PM <kevinega...@gmail.com> wrote: > Hello, > > I'm currently reviewing how to correctly implement `glmnet` and am having > a hard time understanding why the results seem to be different between each > method when `intercept = TRUE/FALSE` as I thought it should just drop the > intercept from the model. However, it seems to be acting a bit different > and I'm not sure how. > > For a given lambda, if both `X` and `y` are scaled, it appears we can > identify the same results: > ``` > library(glmnet) > data(QuickStartExample) > lambda_grid <- 10 ^ seq(10, -2, length = 100) > With_Intercept<-glmnet(scale(x),c(scale(y))) > Without_Intercept<-glmnet(scale(x),c(scale(y)), intercept=FALSE) > # Extract coefficients at a single value of lambda > cbind(coef(With_Intercept,s=0.01), coef(Without_Intercept,s=0.01))[-1,] > ``` > While this is good, it's not clear to me how to put these back into their > original scale. Further, this is for a given value of lambda. When using > `cv.glmnet`, I'd like to identify the optimal lambda such that: > ``` > With_Intercept <- cv.glmnet(scale(x),c(scale(y)), lambda = lambda_grid) > Without_Intercept <- cv.glmnet(scale(x), c(scale(y)), lambda = > lambda_grid, intercept=FALSE) > cbind(coef(With_Intercept, s=With_Intercept$lambda.min, exact = TRUE, x = > scale(x), y = scale(y)), > coef(Without_Intercept, s=Without_Intercept$lambda.min, exact = > TRUE, x = scale(x), y = scale(y)))[-1,] > ``` > If I use `With_Intercept$lambda.min` to identify the `Without_Intercept` > model, I get the same coefficients, but this doesn't necessarily give me > confidence in what is the right model to use. Further, I'm not sure how to > put the coefficients back into the right scale. > > I've tried to compare all of the possible combinations between > standardising, scaling, and leaving the variables as they are, but I'm > still struggling with the best method and how to ensure I'm implementing > `glmnet` correctly. > > If anyone has advice on how to proceed and interpret these methods or get > consistent results I would appreciate it. I've been reading the > Introduction to Statistical Learning, Elements of Statistical Learning, > Statistical Learning and Sparsity, as well as the `glmnet` vignette but am > still a bit unclear. > > Thanks, > > Kevin > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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. > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
