Singular Value Decomposition (SVD) Wrapper — svd

Computes the singular value decomposition of a matrix using one of the specified methods. It is designed to be an easy-to-use wrapper for various SVD methods available in R.

Usage

svd_wrapper(
  X,
  ncomp = min(dim(X)),
  preproc = pass(),
  method = c("fast", "base", "irlba", "propack", "rsvd", "svds"),
  q = 2,
  p = 10,
  tol = .Machine$double.eps,
  ...
)

Arguments

X: the input matrix
ncomp: the number of components to estimate (default: min(dim(X)))
preproc: the pre-processor to apply on the input matrix (e.g., center(), standardize(), pass())
method: the SVD method to use: 'base', 'fast', 'irlba', 'propack', 'rsvd', or 'svds'
q: parameter passed to method rsvd (default: 2)
p: parameter passed to method rsvd (default: 10)
tol: minimum eigenvalue magnitude, otherwise component is dropped (default: .Machine$double.eps)
...: extra arguments passed to the selected SVD function

Value

an SVD object that extends projector

Examples

# Load iris dataset and select the first four columns
data(iris)
X <- iris[, 1:4]

# Compute SVD using the base method and 3 components
fit <- svd_wrapper(X, ncomp = 3, preproc = center(), method = "base")