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harris.test

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

Argument Description
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:

Value Description
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
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