BootTest: Bootstrap hypothesis test (BootTest) method

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

Bootstrap hypothesis test (BootTest) method

Description

Identifies the number of common eigenvectors in several groups using the bootstrap hypothesis test (BootTest) method, adapted from Klingenberg and McIntyre (1998).

Usage

BootTest(origdata, q = ncol(origdata[[1]]), reps = 1000)

Arguments

origdata	List of the sample data sets.
q	Number of common eigenvectors to test for.
reps	Number of bootstrap replications to use.

Details

Tests the hypothesis, H_0: eigenvector pair are common, against the alternative, H_1: eigenvector pair are NOT common.

Value

Returns a data frame with the columns:

Group1	Order of the eigenvector for Group 1.
Group2	Order of the eigenvector for Group 2.
vec.correlations	Vector correlations of the eigenvector pairs.
p.values	P-values for the null hypothesis of commonness of the eigenvector pairs.

Note

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

Author(s)

Theo Pepler

References

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.

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]
BootTest(origdata = list(versicolor, virginica))

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