bed_fastCovEVs

The filename of the bed file (including its extension). The bim and fam files need to be in the same folder and have the same base filename.

The number of leading eigenvectors.

The number of power iteration steps (default is <code>q=2</code>).

Computation of the k leading eigenvectors of the covariance matrix directly from a bed+bim+fam file.

Fast implementations to compute the genetic covariance matrix, the Jaccard similarity matrix, the s-matrix (the weighted Jaccard similarity matrix), and the (classic or robust) genomic relationship matrix of a (dense or sparse) input matrix (see Hahn, Lutz, Hecker, Prokopenko, Cho, Silverman, Weiss, and Lange (2020) <doi:10.1002/gepi.22356>). Full support for sparse matrices from the R-package 'Matrix'. Additionally, an implementation of the power method (von Mises iteration) to compute the largest eigenvector of a matrix is included, a function to perform an automated full run of global and local correlations in population stratification data, a function to compute sliding windows, and a function to invert minor alleles and to select those variants/loci exceeding a minimal cutoff value. New functionality in locStra allows one to extract the k leading eigenvectors of the genetic covariance matrix, Jaccard similarity matrix, s-matrix, and genomic relationship matrix via fast PCA without actually computing the similarity matrices. The fast PCA to compute the k leading eigenvectors can now also be run directly from 'bed'+'bim'+'fam' files.

bed_fastCovEVs: Computation of the k leading eigenvectors of the covariance matrix directly from a bed+bim+fam file.

Description

Usage

Arguments

Value

References

Examples