rda_weights

Matrix or data frame containing the training data. The rows are the
sample observations, and the columns are the features. Only complete data are
retained.

vector of class labels for each training observation

the RDA pooling parameter. Must be between 0 and 1, inclusively.

lambda

This function calculates the weight for each observation in the data matrix
<code>x</code> in order to calculate the covariance matrices employed in the HDRDA
classifier, implemented in <code><a rd-options="=rda_high_dim" href="/link/rda_high_dim()?package=sparsediscrim&version=0.3.0&to=%3Drda_high_dim" data-mini-rdoc="=rda_high_dim::rda_high_dim()">rda_high_dim()</a></code>.

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

sparsediscrim

Sparse and Regularized Discriminant Analysis

John Ramey

rda_weights function

This function calculates the weight for each observation in the data matrix
<code>x</code> in order to calculate the covariance matrices employed in the HDRDA
classifier, implemented in <code><a rd-options='=rda_high_dim' href='rda_high_dim()'>rda_high_dim()</a></code>.

rda_weights: Computes the observation weights for each class for the HDRDA classifier

Description

Usage

Arguments

Value

References