canonicalSVD: singular value decomposition
In SplitKnockoff: Split Knockoffs for Structural Sparsity
canonicalSVD R Documentation
singular value decomposition
Description
Computes a reduced SVD without sign ambiguity. Our convention is that
the sign of each vector in U is chosen such that the coefficient
with largest absolute value is positive.
Usage
canonicalSVD(X)
Arguments
X
the input matrix
Value
S
U
V
Examples
nu = 10
n = 350
m = 100
A_gamma <- rbind(matrix(0,n,m),-diag(m)/sqrt(nu))
svd.result = canonicalSVD(A_gamma)
S <- svd.result$S
S <- diag(S)
V <- svd.result$V
SplitKnockoff documentation built on Oct. 14, 2024, 5:09 p.m.
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| canonicalSVD | R Documentation |
singular value decomposition
Description
Computes a reduced SVD without sign ambiguity. Our convention is that the sign of each vector in U is chosen such that the coefficient with largest absolute value is positive.
Usage
canonicalSVD(X)
Arguments
X |
the input matrix |
Value
S
U
V
Examples
nu = 10
n = 350
m = 100
A_gamma <- rbind(matrix(0,n,m),-diag(m)/sqrt(nu))
svd.result = canonicalSVD(A_gamma)
S <- svd.result$S
S <- diag(S)
V <- svd.result$V
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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