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

Calculates the likelihood ratio statistic and its degrees of freedom for the hypothesis of common eigenvectors in the k groups against the alternative of unrelated covariance matrices.

1 |

`covmats ` |
Array of covariance matrices. |

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

`B ` |
Modal matrix simultaneously diagonalising the covariance matrices, estimated under the assumption of common eigenvectors in the k groups. Can be estimated using simultaneous diagonalisation algorithms such as the Flury-Gautschi (implemented in |

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.cpc ` |
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.

`FG`

, `flury.test`

, `equal.test`

, `prop.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.test(covmats = S, nvec = nvec)
``` |

tpepler/cpc documentation built on Nov. 19, 2017, 1:19 p.m.

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