tong_mean_shrinkage

a matrix with <code>n</code> rows and <code>p</code> columns.

the shrinkage coefficient. If <code>NULL</code> (default), we calculate
the shrinkage coefficient with the formula given just above Equation 5 on page
533 and denoted by \(\hat{r}_{opt}\). We allow the user to specify an
alternative value to investigate better approximations.

r_opt

An implementation of the Lindley-type shrunken mean estimator utilized in
shrinkage-mean-based diagonal linear discriminant analysis (SmDLDA).

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>.

Max Kuhn

tong_mean_shrinkage: Tong et al. (2012)'s Lindley-type Shrunken Mean Estimator

Description

Usage

Arguments

Value

References