From: Trevor Hastie <hastie_at_stanford.edu>

Date: Mon, 02 Jun 2008 11:08:16 -0700

Date: Mon, 02 Jun 2008 11:08:16 -0700

glmnet is a package that fits the regularization path for linear, two-
and multi-class logistic regression

models with "elastic net" regularization (tunable mixture of L1 and L2
penalties).

glmnet uses pathwise coordinate descent, and is very fast.

Some of the features of glmnet:

- by default it computes the path at 100 uniformly spaced (on the log scale) values of the regularization parameter
- glmnet appears to be faster than any of the packages that are freely available, in some cases by two orders of magnitude.
- recognizes and exploits sparse input matrices (ala Matrix package). Coefficient matrices are output in sparse matrix representation.
- penalty is (1-a)*||\beta||_2^2 +a*||beta||_1 where a is between 0 and 1; a=0 is the Lasso penalty, a=1 is the ridge penalty. For many correlated predictors, a=.95 or thereabouts improves the performance of the lasso.
- convenient predict, plot, print, and coef methods
- variable-wise penalty modulation allows each variable to be penalized by a scalable amount; if zero that variable always enters
- glmnet uses a symmetric parametrization for multinomial, with constraints enforced by the penalization.

Other families such as poisson might appear in later versions of glmnet.

Examples of glmnet speed trials:

Newsgroup data: N=11,000, p=4 Million, two class logistic. 100 values
along lasso path. Time = 2mins

14 Class cancer data: N=144, p=16K, 14 class multinomial, 100 values
along lasso path. Time = 30secs

Authors: Jerome Friedman, Trevor Hastie, Rob Tibshirani.

See our paper http://www-stat.stanford.edu/~hastie/Papers/glmnet.pdf for
implementation details,

and comparisons with other related software.

-- -------------------------------------------------------------------- Trevor Hastie hastie_at_stanford.edu Professor & Chair, 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 _______________________________________________ R-packages mailing list R-packages_at_r-project.org https://stat.ethz.ch/mailman/listinfo/r-packagesReceived on Tue 03 Jun 2008 - 05:56:14 EST

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