Principal Component Analysis of an array of matrices

Description

Calculate the principal components of an array of correlation or covariance matrices.

Usage

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## S3 method for class 'array'
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.

...

.

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.

Value

Returns a list with components equivalent to the output from pca.xyz.

Author(s)

Xin-Qiu Yao, Lars Skjaerven

References

Grant, B.J. et al. (2006) Bioinformatics 22, 2695–2696.

See Also

pca.xyz

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