HDtest (version 2.1)

testCov: Testing the equality of two sample covariance matrices in high dimension.

Description

Testing the equality of two sample covariance matrices in high dimension using different methods.

Usage

testCov(X, Y, method = "ALL", J = 2500, alpha = 0.05, n.core = 1)

Arguments

X

the n x p training data, could be a matrix or a data.frame object.

Y

the n x p training data matrix, could be a matrix or a data.frame object.

method

a string incidating the method for the test. The current available methods are ALL, HD, LC, CLX, Scott.

J

the number of repetition in the test

alpha

the significant level of the test.

n.core

the number of cores to be used in parallel when HD is called.

Value

For any single method, the function returns an htest object.

For method ALL: A list of four htest objects.

HD refers to "Chang, J., Zhou, W., Zhou, W.-X., and Wang, L. (2016). Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering. Biometrics. To appear"#'

CLX refers to "Cai, T. T., Liu, W., and Xia, Y. (2013). Two-sample covariance matrix testing and support recovery in high-dimensional and sparse settings. Journal of the American Statistical Association 108, 265-277."

Sc refers to "Schott, J. R. (2007). A test for the equality of covariance matrices when the dimension is large relative to the sample size. Computational Statistics and Data Analysis 51, 6535-6542."

Examples

# NOT RUN {
data(GO54)
testCov(GO54$X, GO54$Y, method = "ALL", J = 100)
data(GO26)
testCov(GO26$X, GO26$Y, method = "ALL", J = 100)

# }

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