Frank Harrell <f.harrell <at> vanderbilt.edu> writes: > > Unlike L1 (lasso) regression or elastic net (mixture of L1 and L2), L2 norm > regression (ridge regression) does not select variables. Selection of > variables would not work properly, and it's unclear why you would want to > omit "apparently" weak variables anyway. > Frank >
... and this was cross-posted from StackOverflow, where I said more or less the same thing about ridge regression (I didn't get into the "don't do variable selection" issue yet, I was waiting ...) http://stackoverflow.com/questions/14046569/ridge-regression-in-r For the other questions (what are the lambda values? What does the output mean?) I would suggest getting a copy of _Modern Applied Statistics in S_ [the book that the package, MASS, was written to accompany] and reading the relevant chapter. > maths123 wrote > > I have a .txt file containing a dataset with 500 samples. There are 10 > > variables. > > > > I am trying to perform variable selection using the ridge regression > > method but I am very confused. > > > > I have input the following: > > diabetes10<-read.table("diabetes10.txt", header=TRUE) > > diabetes10 > > library(MASS) > > select(lm.ridge(y=diabetes10 ~ age+sex+bmi+map+tc , diabetes10, > > lambda = seq(0,0.1,0.0001))) > > > > First of all, i am confused about the lamda values, > > Second of all, my output is: > > > > modified HKB estimator is -1.334073e-29 > > modified L-W estimator is -5.610557e-28 > > smallest value of GCV at 1e-04 > > > > > > I have no idea what that is telling me and where I am supposed to work out > > which variables have been selected. > > ______________________________________________ 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.
