Learn R Programming

sdwd (version 1.0.5)

Sparse Distance Weighted Discrimination

Description

Formulates a sparse distance weighted discrimination (SDWD) for high-dimensional classification and implements a very fast algorithm for computing its solution path with the L1, the elastic-net, and the adaptive elastic-net penalties. More details about the methodology SDWD is seen on Wang and Zou (2016) ().

Copy Link

Version

Install

install.packages('sdwd')

Monthly Downloads

283

Version

1.0.5

License

GPL-2

Maintainer

Boxiang Wang

Last Published

October 27th, 2020

Functions in sdwd (1.0.5)

print.sdwd

print an sdwd object
coef.cv.sdwd

compute coefficients from a "cv.sdwd" object
colon

simplified gene expression data from Alon et al. (1999)
plot.cv.sdwd

plot the cross-validation curve of the sparse DWD
predict.cv.sdwd

make predictions from a "cv.sdwd" object
coef.sdwd

compute coefficients for the sparse DWD
cv.sdwd

cross-validation for the sparse DWD
plot.sdwd

plot coefficients for the sparse DWD
sdwd-internal

internal sdwd functions
predict.sdwd

make predictions for the sparse DWD
sdwd

fit the sparse DWD
sdwd-package

Sparse Distance Weighted Discrimination