randSVD: Singular value decomposition in sparse triangular matrix
In randPedPCA: Fast PCA for Large Pedigrees
randSVD R Documentation
Singular value decomposition in sparse triangular matrix
Description
Uses randomised linear algebra, see Halko et al. (2010). Singular value
decomposition (SVD) decomposes a matrix X=U\Sigma W^T
Usage
randSVD(L, rank, depth, numVectors, cent = FALSE)
Arguments
L
a pedigree's L inverse matrix in sparse 'spam' format
rank
An integer, how many principal components to return
depth
integer, the number of iterations for generating the range matrix
numVectors
An integer > rank to specify the oversampling for the
cent
logical, whether or not to (implicitly) centre the additive
relationship matrix
Value
A list of three: u (=U), d (=Sigma), and v (=W^T)
randPedPCA documentation built on Aug. 8, 2025, 6:37 p.m.
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| randSVD | R Documentation |
Singular value decomposition in sparse triangular matrix
Description
Uses randomised linear algebra, see Halko et al. (2010). Singular value
decomposition (SVD) decomposes a matrix X=U\Sigma W^T
Usage
randSVD(L, rank, depth, numVectors, cent = FALSE)
Arguments
L |
a pedigree's L inverse matrix in sparse 'spam' format |
rank |
An |
depth |
|
numVectors |
An |
cent |
|
Value
A list of three: u (=U), d (=Sigma), and v (=W^T)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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