sdwd-package {sdwd}R Documentation

Sparse Distance Weighted Discrimination

Description

This package implements the generalized coordinate descent (GCD) algorithm to efficiently compute the solution path of the sparse Distance Weighted Discrimination (DWD) at a given fine grid of regularization parameters.

Details

Package: sdwd
Type: Package
Version: 1.0.2
Date: 2015-08-05
License: GPL-2

Suppose x is the predictors and y is the binary response. With a fixed value lambda2, the package produces the solution path over a grid of lambda values.

The package sdwd contains five main functions:
sdwd
coef.sdwd
predict.sdwd
print.sdwd
plot.sdwd

Author(s)

Boxiang Wang and Hui Zou
Maintainer: Boxiang Wang boxiang@umn.edu

References

Wang, B. and Zou, H. (2015) “Sparse Distance Weighted Discrimination", Journal of Computational and Graphical Statistics, forthcoming.
http://arxiv.org/abs/1501.06066

Friedman, J., Hastie, T., and Tibshirani, R. (2010), "Regularization paths for generalized linear models via coordinate descent," Journal of Statistical Software, 33(1), 1–22
http://www.jstatsoft.org/v33/i01/paper

Marron, J.S., Todd, M.J., Ahn, J. (2007) “Distance-Weighted Discrimination"", Journal of the American Statistical Association, 102(408), 1267–1271
https://faculty.franklin.uga.edu/jyahn/sites/faculty.franklin.uga.edu.jyahn/files/DWD3.pdf

Tibshirani, Robert., Bien, J., Friedman, J.,Hastie, T.,Simon, N.,Taylor, J., and Tibshirani, Ryan. (2012) Strong Rules for Discarding Predictors in Lasso-type Problems, Journal of the Royal Statistical Society, Series B, 74(2), 245–266
http://statweb.stanford.edu/~tibs/ftp/strong.pdf

Yang, Y. and Zou, H. (2013) “An Efficient Algorithm for Computing the HHSVM and Its Generalizations", Journal of Computational and Graphical Statistics, 22(2), 396–415
http://users.stat.umn.edu/~yiyang/resources/papers/JCGS_gcdnet.pdf


[Package sdwd version 1.0.2 Index]