R svd Function

svd()function computes the singular-value decomposition of a rectangular matrix.

svd(x, nu = min(n, p), nv = min(n, p), LINPACK = FALSE)
La.svd(x, nu = min(n, p), nv = min(n, p))

x: a numeric, logical or complex matrix 
nu: the number of left singular vectors to be computed. This must between 0 and n = nrow(x)
nv: the number of right singular vectors to be computed. This must be between 0 and p = ncol(x)
LINPACK: logical. Should LINPACK be used (for compatibility with R < 1.7.0)? In this case nu must be 0, nrow(x) or ncol(x)

> x <- matrix(1:16,4,4)
> x

     [,1] [,2] [,3] [,4]
[1,]    1    5    9   13
[2,]    2    6   10   14
[3,]    3    7   11   15
[4,]    4    8   12   16

> svd(x)

$d
[1] 3.862266e+01 2.071323e+00 2.076990e-15 4.119458e-16

$u
           [,1]       [,2]        [,3]       [,4]
[1,] -0.4284124 -0.7186535  0.43803202  0.3288281
[2,] -0.4743725 -0.2738078 -0.82913672 -0.1119477
[3,] -0.5203326  0.1710379  0.34417739 -0.7625890
[4,] -0.5662928  0.6158835  0.04692732  0.5457086

$v
           [,1]        [,2]       [,3]       [,4]
[1,] -0.1347221  0.82574206  0.5322301 -0.1293488
[2,] -0.3407577  0.42881720 -0.6132292  0.5691660
[3,] -0.5467933  0.03189234 -0.3702319 -0.7502855
[4,] -0.7528288 -0.36503251  0.4512310  0.3104683