schottTest: Schott's Test for testing independency

In TestIndVars: Testing the Independence of Variables for Specific Covariance Structures

Schott's Test for testing independency

Description

Performs Schott's test for the correlation matrix to assess if the correlation matrix is significantly different from an identity matrix.

Usage

schottTest(X, alpha = 0.05)

Arguments

X	A numeric matrix or data frame containing the variables.
alpha	The significance level for the test (default is 0.05).

Value

A data frame containing the test statistic, alpha value, p-value, and test result.

References

Schott, J. R. (2005). Testing for complete independence in high dimensions, Biometrika, 92(4), 951–956.

Examples

library(MASS)

n = 50 # Sample Size
p = 5
rho = 0.1
# Building a Covariance structure with Autoregressive structure
cov_mat <- covMatAR(p = p, rho = rho)
# Simulated data
data <- mvrnorm(n = n, mu = rep(0,p), Sigma = cov_mat)
# Performing the test
schottTest(data)

# Building a Covariance structure with Compound Symmetry structure
cov_mat <- covMatCS(p = p, rho = rho)
# Simulated data
data <- mvrnorm(n = n, mu = rep(0,p), Sigma = cov_mat)
# Performing the test
schottTest(data)

# Building a Covariance structure with Circular structure
cov_mat <- covMatC(p = p, rho = rho)
# Simulated data
data <- mvrnorm(n = n, mu = rep(0,p), Sigma = cov_mat)
# Performing the test
schottTest(data)

TestIndVars documentation built on May 29, 2024, 6:14 a.m.