We have uploaded to CRAN the first version of glmpath,
which fits the L1 regularization path for generalized linear models.
The lars package fits the entire piecewise-linear L1 regularization
path for
the lasso. The coefficient paths for L1 regularized glms, however,
are not piecewise linear.
glmpath uses convex optimization - in particular predictor-corrector
methods-
to fit the coefficient path at important junctions. These junctions
are at the "knots" in |beta|
where variables enter/leave the active set; i.e. nonzero/zero values.
Users can request greater resolution at a cost of more computation,
and compute values
on a fine grid between the knots.
The code is fast, and can handle largish problems efficiently.
it took just over 4 sec system cpu time to fit the logistic
regression path for
the "spam" data from UCI with 3065 training obs and 57 predictors.
For a microarray example with 5000 variables and 100 observations, 11
seconds cpu time.
Currently glmpath implements binomial, poisson and gaussian families.
Mee Young Park and Trevor Hastie
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Trevor Hastie hastie at stanford.edu
Professor, Department of Statistics, Stanford University
Phone: (650) 725-2231 (Statistics) Fax: (650) 725-8977
(650) 498-5233 (Biostatistics) Fax: (650) 725-6951
URL: http://www-stat.stanford.edu/~hastie
address: room 104, Department of Statistics, Sequoia Hall
390 Serra Mall, Stanford University, CA 94305-4065
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