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

Identifies the number of common eigenvectors in several groups using the random vector correlations (RVC) method, adapted from Klingenberg and McIntyre (1998).

1 | ```
RVC(covmats, reps = 100000)
``` |

`covmats` |
Array of covariance matrices for the k groups. |

`reps` |
Number of randomisations to use. |

Vector correlations between sample eigenvectors are compared to a distribution of vector correlations between of pairs of vectors randomly generated on the unit sphere, in order to determine whether the associated eigenvectors of the population covariance matrices are common.

Returns a data frame with the columns:

`commonvec.order` |
Order of the eigenvectors in the two groups. |

`vec.correlations` |
Vector correlations of the eigenvector pairs. |

`p.values` |
P values for the null hypothesis of commonness of the eigenvector pairs. |

Note that this implementation of the RVC method can currently handle only two groups of data.

Theo Pepler

Klingenberg, C. P. and McIntyre, G. S. (1998). Geometric morphometrics of developmental instability: Analyzing patterns of fluctuating asymmetry with Procrustes methods. Evolution, 52(5): 1363-1375.

Pepler, P.T. (2014). The identification and application of common principal components. PhD dissertation in the Department of Statistics and Actuarial Science, Stellenbosch University.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
# Determine number of common eigenvectors in the covariance matrices of the
# versicolor and virginica groups
data(iris)
versicolor <- iris[51:100, 1:4]
virginica <- iris[101:150, 1:4]
# Create array containing the covariance matrices
S <- array(NA, dim = c(4, 4, 2))
S[, , 1] <- cov(versicolor)
S[, , 2] <- cov(virginica)
RVC(covmats = S)
``` |

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

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