harris.test: Test for variance homogeneity of correlated variables

In fastmatrix: Fast Computation of some Matrices Useful in Statistics

Test for variance homogeneity of correlated variables

Description

Performs large-sample methods for testing equality of p \ge 2 correlated variables.

Usage

harris.test(x, test = "Wald")

Arguments

x	a matrix or data frame. As usual, rows are observations and columns are variables.
test	test statistic to be used. One of "Wald" (default), "log", "robust" or "log-robust".

Value

A list of class 'harris.test' with the following elements:

statistic	value of the statistic, i.e. the value of either Wald test, using the log-transformation, or distribution-robust versions of the test (robust and log-robust).
parameter	the degrees of freedom for the test statistic, which is chi-square distributed.
p.value	the p-value for the test.
estimate	the estimated covariance matrix.
method	a character string indicating what type of test was performed.

References

Harris, P. (1985). Testing the variance homogeneity of correlated variables. Biometrika 72, 103-107.

Examples

x <- iris[,1:4]
z <- harris.test(x, test = "robust")
z

fastmatrix documentation built on Oct. 12, 2023, 5:14 p.m.