matsbyname (version 0.6.11)

svd_byname: Calculate the singular value decomposition of a matrix

Description

The singular value decomposition decomposes matrix A into A = U D V^T, where U and V are orthogonal matrices and D is a diagonal matrix. U is the left singular vectors of A. V is the right singular vectors of A.

Usage

svd_byname(a, which = c("d", "u", "v"))

Value

A matrix of the singular value decomposition of a.

Arguments

a

A matrix to be decomposed.

which

The matrix to be returned. Default is "d". See details.

Details

which determines the part of the singular value decomposition to be returned. "d" (default) gives the D matrix. "u" gives the U matrix. "v" gives the V matrix (not its transpose).

Examples

A = matrix(c(4, 0, 
             3, -5), nrow = 2, ncol = 2, byrow = TRUE, 
           dimnames = list(c("r1", "r2"), c("c1", "c2"))) %>% 
  setrowtype("Product") %>% setcoltype("Industry")
A
svd_byname(A) # Gives D matrix, by default
svd_byname(A, which = "d")
svd_byname(A, which = "u")
svd_byname(A, which = "v")