This is a major revision, with two additional models included.
1) Multiresponse regression - family="mgaussian"
Here we have a matrix of M responses, and we fit a series of linear models in
parallel. We use a group-lasso penalty on the set of M coefficients for each
variable.
This means they are all in or out together
2) family="multinomial, type.multinomial="grouped"
Same story = multinomial regression, but now the group lasso penalty ensures all
the coefficients are in or out for each class at the same time. We have left
the default
as type.multinomial="ungrouped" because currently this grouped version
is about 10
times slower. We will be looking to improve this aspect.
Thanks to Noah Simon for his work on developing the algorithms for
both these options. A report is in the works.
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Trevor Hastie hastie@stanford.edu
Professor, Department of Statistics, Stanford University
Phone: (650) 725-2231 Fax: (650) 725-8977
URL: http://www.stanford.edu/~hastie
address: room 104, Department of Statistics, Sequoia Hall
390 Serra Mall, Stanford University, CA 94305-4065
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