# 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

 `1` ```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 Oct. 6, 2017, 12:14 a.m.