BVD: Bootstrap vector correlation distribution (BVD) method

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

Bootstrap vector correlation distribution (BVD) method

Description

Identifies the number of common eigenvectors in several groups using the bootstrap vector correlation distributions (BVD) method.

Usage

BVD(origdata, reps = 1000)

Arguments

origdata	List of the sample data sets.
reps	Number of bootstrap replications to use.

Details

Identifies the number of common eigenvectors using the BVD 1a, BVD 1b, BVD 1c, BVD 2a, BVD 2b, BVD 2c and BVD 2d methods.

Value

Returns a matrix with the following rows:

Common eigenvector	Number of the eigenvector in the modal matrix.
BVD 1a	Vector indicating the common eigenvectors (=1) according to this method.
BVD 1b	Vector indicating the common eigenvectors (=1) according to this method.
BVD 1c	Vector indicating the common eigenvectors (=1) according to this method.
BVD 2a	Vector indicating the common eigenvectors (=1) according to this method.
BVD 2b	Vector indicating the common eigenvectors (=1) according to this method.
BVD 2c	Vector indicating the common eigenvectors (=1) according to this method.
BVD 2d	Vector indicating the common eigenvectors (=1) according to this method.

Note

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

Author(s)

Theo Pepler

References

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

See Also

ensemble.test

Examples

# 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]
BVD(origdata = list(versicolor, virginica))

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