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. Sparse distance weighted discrimination is a high-dimensional margin-based classifier.
Details
Package: | sdwd |
Type: | Package |
Version: | 1.0.3 |
Date: | 2020-02-16 |
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 of the sparse DWD over a grid of lambda
values. The value of lambda2
can be further tuned by cross-validation.
The package sdwd
contains five main functions:
sdwd
cv.sdwd
coef.sdwd
plot.sdwd
plot.cv.sdwd
Author(s)
Boxiang Wang and Hui Zou
Maintainer: Boxiang Wang boxiang-wang@uiowa.edu
References
Wang, B. and Zou, H. (2016)
“Sparse Distance Weighted Discrimination", Journal of Computational and Graphical Statistics, 25(3), 826–838.
https://www.tandfonline.com/doi/full/10.1080/10618600.2015.1049700
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.
https://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://www.tandfonline.com/doi/abs/10.1198/016214507000001120
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.
https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9868.2011.01004.x
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.
https://www.tandfonline.com/doi/full/10.1080/10618600.2012.680324