BCR: Bootstrap confidence regions (BCR) method

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

Bootstrap confidence regions (BCR) method

Description

Identifies the number of common eigenvectors in several groups using the bootstrap confidence regions (BCR) method.

Usage

BCR(origdata, reps = 1000)

Arguments

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

Details

Calculates 95% bootstrap confidence regions for eigenvector pairs; if the regions overlap, the eigenvectors are considered to be common.

Value

Returns a data frame with the columns:

Common eigenvector	Order of the eigenvectors in the model matrix.
common.ind	Indicator of eigenvector commonness (1 = common eigenvector).

Note

Note that this implementation of the BCR 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]
BCR(origdata = list(versicolor, virginica))

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