fastSMatrixEVs | R Documentation |
sMatrix
, the choice phased=FALSE
cannot be modified for the fast eigenvector computation.Computation of the k leading eigenvectors of the s-matrix (the weighted Jaccard similarity matrix) for a (sparse) input matrix. Note that in contrast to the parameters of the function sMatrix
, the choice phased=FALSE
cannot be modified for the fast eigenvector computation.
fastSMatrixEVs(m, k, useCpp = TRUE, sparse = TRUE, Djac = FALSE, q = 2)
m |
A (sparse) matrix for which the eigenvectors of its s-matrix are sought. The input matrix is assumed to be oriented to contain the data for one individual per column. |
k |
The number of leading eigenvectors. |
useCpp |
Flag to switch between R or C++ implementations. Default is |
sparse |
Flag to switch between purpose-built dense or sparse implementations. Default is |
Djac |
Flag to switch between the unweighted ( |
q |
The number of power iteration steps (default is |
The k leading eigenvectors of the s-matrix of m
as a column matrix.
Daniel Schlauch (2016). Implementation of the stego algorithm - Similarity Test for Estimating Genetic Outliers. https://github.com/dschlauch/stego
N. Halko, P.G. Martinsson, and J.A. Tropp (2011). Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions. SIAM Review: 53(2), pp. 217–288.
require(locStra) require(Matrix) m <- matrix(sample(0:1,100,replace=TRUE),ncol=5) sparseM <- Matrix(m,sparse=TRUE) print(fastSMatrixEVs(sparseM,k=2,useCpp=FALSE))
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