Fast Implementation of (Local) Population Stratification Methods

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Documentation for package ‘locStra’ version 1.4

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covMatrix Cpp implementation of a function to compute the covariance matrix for a (sparse) matrix. The function is equivalent to the R command 'cov' applied to matrices.
fullscan Main function: A full scan of the input data 'm' using a collection of windows given by the two-column matrix 'windows'. For each window, the data is processed using the function 'matrixFunction' (this could be e.g. the 'covMatrix' function), then the processed data is summarised using the function 'summaryFunction' (e.g., the largest eigenvector computed with the function 'powerMethod'), and finally the global and local summary scores (e.g., the largest eigenvectors) are compared using the function 'comparisonFunction' (e.g., the vector correlation with R's function 'cor'). The function returns a two-column matrix which contains per row the global (e.g., the correlation between global and local eigenvectors) and local (e.g., the correlation between the local eigenvector for the current window and the eigenvector for the last window) summary statistics for each window.
grMatrix Cpp implementation of the genomic relationship matrix (grm) for a (sparse) input matrix as defined in Yang et al. (2011).
jaccardMatrix Cpp implementation of the Jaccard similarity matrix computation for a (sparse) input matrix.
makeWindows Auxiliary function to generate a two-column matrix of window sizes to be used in the function 'fullscan'.
powerMethod Cpp implementation of the power method (von Mises iteration) to compute the largest eigenvector for a (sparse) input matrix.
sMatrix Cpp implementation of the s-matrix function (which computes the weighted Jaccard similarity matrix) for a (sparse) input matrix as in the 'Stego' package on https://github.com/dschlauch/stego.
testdata Simulated test data.