fastCovEVs: Computation of the k leading eigenvectors of the covariance...

In locStra: Fast Implementation of (Local) Population Stratification Methods

Computation of the k leading eigenvectors of the covariance matrix for a (sparse) input matrix.

Description

Computation of the k leading eigenvectors of the covariance matrix for a (sparse) input matrix.

Usage

fastCovEVs(m, k, useCpp = TRUE, sparse = TRUE, q = 2)

Arguments

m	A (sparse) matrix for which the eigenvectors of its covariance 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 useCpp=TRUE.
sparse	Flag to switch between purpose-built dense or sparse implementations. Default is sparse=TRUE.
q	The number of power iteration steps (default is q=2).

Value

The k leading eigenvectors of the covariance matrix of m as a column matrix.

References

R Core Team (2014). R: A Language and Environment for Statistical Computing. R Foundation for Stat Comp, Vienna, Austria.

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.

Examples

require(locStra)
require(Matrix)
m <- matrix(sample(0:1,100,replace=TRUE),ncol=5)
sparseM <- Matrix(m,sparse=TRUE)
print(fastCovEVs(sparseM,k=2,useCpp=FALSE))

locStra documentation built on April 13, 2022, 1:07 a.m.