covclx {Domean} | R Documentation |
Two-Sample Covariance Test for High-Dimensional Data
Description
Performs a test to compare the covariance matrices of two high-dimensional samples. This test is designed for situations where the number of variables \( p \) is large relative to the sample sizes \( n_1 \) and \( n_2 \).
Usage
covclx(X, Y)
Arguments
X |
A numeric matrix representing the first sample, where rows are observations and columns are variables. |
Y |
A numeric matrix representing the second sample, where rows are observations and columns are variables. |
Details
This function tests the null hypothesis that the covariance matrices of two samples are equal:
H_0: \Sigma_1 = \Sigma_2
against the alternative hypothesis that they are not equal.
The test statistic is based on the maximum normalized squared difference between the two sample covariance matrices. The p-value is computed using an extreme value distribution.
Value
A list containing the following components:
stat |
The test statistic. |
pval |
The p-value of the test. |
See Also
cov
: Used for calculating sample covariance matrices.
Examples
# Example usage:
set.seed(123)
n1 <- 20
n2 <- 30
p <- 50
X <- matrix(rnorm(n1 * p), nrow = n1, ncol = p)
Y <- matrix(rnorm(n2 * p), nrow = n2, ncol = p)
result <- covclx(X, Y)
print(result)