ensemble.test: Ensemble test for the identification of common eigenvectors

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

Ensemble test for the identification of common eigenvectors

Description

Identifies the number of common eigenvectors in several groups using the ensemble test.

Usage

ensemble.test(origdata, standardize = FALSE)

Arguments

origdata	List of the sample data sets.
standardize	Logical, indicating whether the data columns should be standardized (mean=0, stdev=1) before performing the ensemble test (default = FALSE).

Details

Ensemble method to identify common eigenvectors in k groups: majority vote on number of common eigenvectors from Flury's AIC, Bootstrap Vector Correlation Distribution (BVD), Bootstrap Confidence Regions (BCR), Random Vector Correlations (RVC) and Bootstrap hypothesis test (BootTest) methods.

Value

Returns a list with the following components:

Results	Row 1: order of common eigenvectors in B; Row 2-5: results from AIC, BVD, BCR and RVC tests (1 = eigenvector common); Row 6: ensemble test common eigenvector indicator (1 = eigenvector common).
commonvecs	Positions of the common eigenvectors in the modal matrix.
commonvecmat	The estimated common eigenvectors, extracted from the modal matrix.

Note

Note that this implementation of the Ensemble test 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

flury.AIC, BVD, BCR, RVC, and BootTest

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]
ensemble.test(origdata = list(versicolor, virginica))

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