pca.array: Principal Component Analysis of an array of matrices
Description
Calculate the principal components of an array of correlation or
covariance matrices.
Usage
"pca"(x, use.svd = TRUE, ...)
Arguments
x
an array of matrices, e.g. correlation or covariance
matrices as obtained from functions dccm or enma2covs.
use.svd
logical, if TRUE singular value decomposition (SVD) is
called instead of eigenvalue decomposition.
...
.
Value
Returns a list with components equivalent to the output from
pca.xyz.
Details
This function performs PCA of symmetric matrices, such as distance
matrices from an ensemble of crystallographic structures, residue-residue
cross-correlations or covariance matrices derived from ensemble NMA
or MD simulation replicates, and so on. The ‘upper triangular’
region of the matrix is regarded as a long vector of random variables.
The function returns M eigenvalues and eigenvectors with each eigenvector
having the dimension N(N-1)/2, where M is the number of matrices and N
the number of rows/columns of matrices.
References
Grant, B.J. et al. (2006) Bioinformatics22, 2695--2696.