Principal Component Analysis of an array of matrices
Calculate the principal components of an array of correlation or covariance matrices.
## S3 method for class 'array' pca(x, use.svd = TRUE, rm.gaps=TRUE, ...)
x |
an array of matrices, e.g. correlation or covariance
matrices as obtained from functions |
use.svd |
logical, if TRUE singular value decomposition (SVD) is called instead of eigenvalue decomposition. |
rm.gaps |
logical, if TRUE gap cells (with missing coordinate data in any input matrix) are removed before calculation. This is equivalent to removing NA cells from x. |
... |
. |
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.
Returns a list with components equivalent to the output from
pca.xyz.
Xin-Qiu Yao, Lars Skjaerven
Grant, B.J. et al. (2006) Bioinformatics 22, 2695–2696.
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