var_shrinkage

a vector of the sample variances for each feature.

var_feature

The number of values used to find the optimal amount of
shrinkage.

num_alphas

a constant specified by the user that indicates the exponent to use
with the variance estimator. By default, t = -1 as in Pang et al. See the
paper for more details.

This function computes the shrinkage-based estimator of variance of each
feature (variable) from Pang et al. (2009) for the SDLDA classifier.

A collection of sparse and regularized discriminant analysis
methods intended for small-sample, high-dimensional data sets. The package
features the High-Dimensional Regularized Discriminant Analysis classifier
from Ramey et al. (2017) <arXiv:1602.01182>. Other classifiers include
those from Dudoit et al. (2002) <doi:10.1198/016214502753479248>, Pang et
al. (2009) <doi:10.1111/j.1541-0420.2009.01200.x>, and Tong et al. (2012)
<doi:10.1093/bioinformatics/btr690>.

var_shrinkage: Shrinkage-based estimator of variances for each feature from Pang et al. (2009).

Description

Usage

Arguments

Value

References