kerndwd-package {kerndwd} | R Documentation |
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.
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
Boxiang Wang and Hui Zou
Maintainer: Boxiang Wang boxiang-wang@uiowa.edu
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