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

Provides descriptive measures to decide whether there are common eigenvectors in several covariance matrices.

1 |

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

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

`cutoff ` |
Cut-off value to use in the vector correlation scree plot. |

`plotting ` |
Logical, indicating whether a scree plot of the vector correlations should be constructed (default = TRUE). |

`main ` |
Title of the scree plot, if |

Identifies possibly common eigenvectors in k data groups by investigating the vectors correlations of all combinations of eigenvectors from the groups. These sets may be tested further for commonness.

Produces a scree plot of the vector correlations (if `plotting = TRUE`

) and returns a list with the values:

`all.correlations ` |
Summary of all eigenvector combinations from the k groups, and the geometric means of the vector correlations. |

`commonvec.order ` |
Order of the (possibly) common eigenvectors in the modal matrix (if an estimate was supplied). |

Theo Pepler

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 14 15 16 | ```
# 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)
findcpc(covmats = S)
# Estimate the modal matrix with the FG algorithm
nvec <- c(nrow(versicolor), nrow(virginica))
B <- cpc::FG(covmats = S, nvec = nvec)$B
findcpc(covmats = S, B = B)
``` |

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

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