BVD | R Documentation |
Identifies the number of common eigenvectors in several groups using the bootstrap vector correlation distributions (BVD) method.
BVD(origdata, reps = 1000)
origdata |
List of the sample data sets. |
reps |
Number of bootstrap replications to use. |
Identifies the number of common eigenvectors using the BVD 1a, BVD 1b, BVD 1c, BVD 2a, BVD 2b, BVD 2c and BVD 2d methods.
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 that this implementation of the BVD method can currently handle only two groups of data.
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.
ensemble.test
# 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))
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.