| kerndwd-package {kerndwd} | R Documentation |
Kernel Distance Weighted Discrimination
Description
Extremely novel efficient procedures for solving linear generalized DWD and kernel generalized DWD in reproducing kernel Hilbert spaces for classification. The algorithm is based on the majorization-minimization (MM) principle to compute the entire solution path at a given fine grid of regularization parameters.
Details
Suppose x is predictor and y is a binary response. The package computes the entire solution path over a grid of lambda values.
The main functions of the package kerndwd include:
kerndwd
cv.kerndwd
tunedwd
predict.kerndwd
plot.kerndwd
plot.cv.kerndwd
Author(s)
Boxiang Wang and Hui Zou
Maintainer: Boxiang Wang boxiang-wang@uiowa.edu
References
Wang, B. and Zou, H. (2018)
“Another Look at Distance Weighted Discrimination,"
Journal of Royal Statistical Society, Series B, 80(1), 177–198.
https://rss.onlinelibrary.wiley.com/doi/10.1111/rssb.12244
Karatzoglou, A., Smola, A., Hornik, K., and Zeileis, A. (2004)
“kernlab – An S4 Package for Kernel Methods in R",
Journal of Statistical Software, 11(9), 1–20.
https://www.jstatsoft.org/v11/i09/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