[R-pkgs] glmpath: L1 regularization path for glms

Trevor Hastie hastie at stanford.edu
Mon Nov 28 02:28:46 CET 2005

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  
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

   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

More information about the R-packages mailing list