covtest {PEtests} | R Documentation |
Two-sample covariance tests for high-dimensional data
Description
This function implements five two-sample covariance tests on high-dimensional
covariance matrices.
Let and
be two
-dimensional populations with mean vectors
and covariance matrices
, respectively.
The problem of interest is to test the equality of the two
covariance matrices:
Suppose are i.i.d.
copies of
, and
are i.i.d. copies of
. We denote
dataX=
and
dataY=
.
Usage
covtest(dataX,dataY,method='pe.comp',delta=NULL)
Arguments
dataX |
an |
dataY |
an |
method |
the method type (default =
|
delta |
This is needed only in |
Value
method
the method type
stat
the value of test statistic
pval
the p-value for the test.
References
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(501):265–277.
Li, J. and Chen, S. X. (2012). Two sample tests for high-dimensional covariance matrices. The Annals of Statistics, 40(2):908–940.
Yu, X., Li, D., and Xue, L. (2022). Fisher’s combined probability test for high-dimensional covariance matrices. Journal of the American Statistical Association, (in press):1–14.
Yu, X., Li, D., Xue, L., and Li, R. (2022). Power-enhanced simultaneous test of high-dimensional mean vectors and covariance matrices with application to gene-set testing. Journal of the American Statistical Association, (in press):1–14.
Examples
n1 = 100; n2 = 100; pp = 500
set.seed(1)
X = matrix(rnorm(n1*pp), nrow=n1, ncol=pp)
Y = matrix(rnorm(n2*pp), nrow=n2, ncol=pp)
covtest(X,Y)