Ginv: Compute Generalized Inverse of Input Matrix

Description Usage Arguments Details Value References See Also Examples

View source: R/Ginv.R

Description

Singular value decomposition (svd) is used to compute a generalized inverse of input matrix.

Usage

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Ginv(x, eps=1e-6)

Arguments

x

A matrix.

eps

minimum cutoff for singular values in svd of x

Details

The svd function uses the LAPACK standard library to compute the singular values of the input matrix, and the rank of the matrix is determined by the number of singular values that are at least as large as max(svd)*eps, where eps is a small value. For S-PLUS, the Matrix library is required (Ginv loads Matrix if not already done so).

Value

List with components:

Ginv

Generalized inverse of x.

rank

Rank of matrix x.

References

Press WH, Teukolsky SA, Vetterling WT, Flannery BP. Numerical recipes in C. The art of scientific computing. 2nd ed. Cambridge University Press, Cambridge.1992. page 61.

Anderson, E., et al. (1994). LAPACK User's Guide, 2nd edition, SIAM, Philadelphia.

See Also

svd

Examples

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# for matrix x, extract the generalized inverse and 
# rank of x as follows
    x <- matrix(c(1,2,1,2,3,2),ncol=3)
    save <- Ginv(x)
    ginv.x <- save$Ginv
    rank.x <- save$rank

haplo.stats documentation built on Sept. 5, 2021, 5:36 p.m.