There are at least two options. There is the sparseLDA package that was written by the author of "Sparse Discriminant Analysis". The paper can be found at
http://www-stat.stanford.edu/~hastie/Papers/sda_line.pdf The initial version of the package is here: http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=5672 I'm working on another version that is more R-centric (e.g. object oriented, has predict methods, etc). I think that will be on CRAN soon. Another option is the sparse PLS model in the spls package that also uses methods similar to the elastic net. I've written functions to extend this to classification in the same manner as the plsda function in the caret package. I've submitted the code to the package maintainers, but I can send it to anyone who emails me off-list. Max On Tue, Dec 9, 2008 at 1:09 PM, Jack Luo <[EMAIL PROTECTED]> wrote: > Hi, List > > The elastic net package (by Hastie and Zou at Stanford) is used to do > regularization and variable selection, it can also do regression. I am > wondering if it can perform binary classification (discrete outcome). > Anybody having similar experience? > > Many thanks, > > -Jack > > [[alternative HTML version deleted]] > > ______________________________________________ > 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. > -- Max ______________________________________________ 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.
