WL.randProj.test: Two-sample covariance test (Wu and Li 2015)

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

Description

The two-sample covariance test using random projections proposed in Wu and Li (2015) "Tests for High-Dimensional Covariance Matrices Using Random Matrix Projection".

Usage

WL.randProj.test(X, Y, nproj = 100, useMC = FALSE, mc.cores = 1)

Arguments

X	n1 by p matrix, observation of the first population, columns are features
Y	n2 by p matrix, observation of the second population, columns are features
nproj	number of random projections to use
useMC	logical variable indicating whether to use multicore parallelization. R packages parallel and doParallel are required if set to TRUE.
mc.cores	decide the number of cores to use when useMC is set to TRUE.

Value

A list containing the following components:

test.stat	test statistic
pVal	the p-value calculated using the limiting distribution (max of independent standard normal)

References

Wu and Li (2015) "Tests for High-Dimensional Covariance Matrices Using Random Matrix Projection", arXiv preprint arXiv:1511.01611.

See Also

Cai.max.test(), Chang.maxBoot.test(), LC.U.test(), Schott.Frob.test().

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