penaltyParameter {DWDLargeR} | R Documentation |
Compute the penalty parameter for the model.
Description
Find the best penalty parameter for the generalized distance weighted discrimination (DWD) model.
Usage
penaltyParameter(X,y,expon,rmzeroFea = 1, scaleFea = 1)
Arguments
X |
A |
y |
A vector of length |
expon |
A positive number representing the exponent |
rmzeroFea |
Switch for removing zero features in the data matrix. Default is set to be 1 (removing zero features). |
scaleFea |
Switch for scaling features in the data matrix. This is to make the features having roughly similar magnitude. Default is set to be 1 (scaling features). |
Details
The best parameter is empirically found to be inversely proportional to the typical distance between different samples raised to the power of ().
It is also dependent on the sample size
and feature dimension
.
Value
A number which represents the best penalty parameter for the generalized DWD model.
Author(s)
Xin-Yee Lam, J.S. Marron, Defeng Sun, and Kim-Chuan Toh
References
Lam, X.Y., Marron, J.S., Sun, D.F., and Toh, K.C. (2018)
“Fast algorithms for large scale generalized distance weighted discrimination", Journal of Computational and Graphical Statistics, forthcoming.
https://arxiv.org/abs/1604.05473
Examples
# load the data
data("mushrooms")
# calculate the best penalty parameter
C = penaltyParameter(mushrooms$X,mushrooms$y,expon=1)