rda_weights

matrix containing the training data. The rows are the sample
observations, and the columns are the features.

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="" href="/link/hdrda?package=sparsediscrim&version=0.2.4" data-mini-rdoc="sparsediscrim::hdrda">hdrda</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.

John Ramey

sparsediscrim

Sparse and Regularized Discriminant Analysis

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='' href='hdrda'>hdrda</a></code>.

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

Description

Usage

Arguments

Value

References