getDiffMatrix: The differential matrix

In lingxuez/sLED: A Sparse Leading Eigenvalue Driven (sLED) Test for High-dimensional Matrices

Description

Given observations from two populations X and Y, compute the differential matrix

D = A(Y) - A(X)

where A() is the covariance matrix, or the weighted adjacency matrices defined as

A_{ij} = |corr(i, j)|^b

for some constant b > 0, 1 <= i, j <= p. Let A represent the regular correlation matrix when b=0, and covariance matrix when b<0.

Usage

getDiffMatrix(X, Y, adj.beta = -1)

Arguments

X	n1-by-p matrix for samples from the first population. Rows are samples/observations, while columns are the features.
Y	n2-by-p matrix for samples from the second population. Rows are samples/observations, while columns are the features.
adj.beta	Power to transform correlation matrices to weighted adjacency matrice by A_{ij} = |r_ij|^adj.beta where r_ij represents the Pearson correlation. When adj.beta=0, the correlation marix is used. When adj.beta<0, the covariance matrix is used. The default value is adj.beta=-1.

Value

The p-by-p differential matrix D = A(Y) - A(X)

lingxuez/sLED documentation built on May 7, 2019, 2:55 a.m.