prop.test: Likelihood ratio test of proportionality of several...

In tpepler/cpc: Common principal component (CPC) analysis and applications

Likelihood ratio test of proportionality of several covariance matrices

Description

Calculates the likelihood ratio statistic and its degrees of freedom for the hypothesis of proportional covariance matrices against the alternative of unrelated covariance matrices.

Usage

prop.test(covmats, nvec)

Arguments

covmats	Array of covariance matrices.
nvec	Vector of sample sizes of the k groups.

Details

This is an implementation of the algorithm described in Flury (1988).

Value

Returns a list with the following:

chi.square	The likelihood ratio test statistic.
df	Degrees of freedom of the test statistic under the null hypothesis.
covmats.prop	Estimated covariance matrices under the null hypothesis model.

Note

This test is based on the assumption that the populations from which the data originated are distributed multivariate normal.

Author(s)

Theo Pepler

References

Flury, B. (1988). Common Principal Components and Related Multivariate Models. Wiley.

See Also

flury.test, equal.test, cpc.test and cpcq.test

Examples

# Versicolor and virginica groups of the Iris data
data(iris)
versicolor <- iris[51:100, 1:4]
virginica <- iris[101:150, 1:4]

# Create array containing the two covariance matrices
S <- array(NA, c(4, 4, 2))
S[, , 1] <- cov(versicolor)
S[, , 2] <- cov(virginica)
nvec <- c(nrow(versicolor), nrow(virginica))

cpc::prop.test(covmats = S, nvec = nvec)

tpepler/cpc documentation built on July 7, 2022, 2:13 a.m.