Calculate the principal components of an array of correlation or covariance matrices.
# S3 method for array
pca(x, use.svd = TRUE, ...)an array of matrices, e.g. correlation or covariance
matrices as obtained from functions dccm or enma2covs.
logical, if TRUE singular value decomposition (SVD) is called instead of eigenvalue decomposition.
.
Returns a list with components equivalent to the output from
pca.xyz.
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.
Grant, B.J. et al. (2006) Bioinformatics 22, 2695--2696.