sdwd-package {sdwd} | R Documentation |
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.
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
Boxiang Wang and Hui Zou
Maintainer: Boxiang Wang boxiang@umn.edu
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