Description Usage Arguments Details Value Note Author(s) References See Also Examples

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

1 | ```
prop.test(covmats, nvec)
``` |

`covmats ` |
Array of covariance matrices. |

`nvec ` |
Vector of sample sizes of the k groups. |

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

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. |

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

Theo Pepler

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

`flury.test`

, `equal.test`

, `cpc.test`

and `cpcq.test`

1 2 3 4 5 6 7 8 9 10 11 12 | ```
# 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)
``` |

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