SVDforPCA: Singular value decomposition for PCA analysis

In FranceschiniS/LMWiRe: R Routines for Linear Model with Wide Response

Description

PCA over a X matrix by singular value decomposition, the preprocessing involves the mean-centering of X.

Usage

SVDforPCA(x, ncomp = min(dim(x)))

Arguments

x	A data matrix on which will be based the analysis.
ncomp	Number of Principal Components.

Value

A list with the following elements:

scores

Scores

loadings

Loadings

eigval

Eigenvalues

pcd

Singular values

pcu

Normalized scores

var

Explained variance

cumvar

Cumulated explained variance

original.dataset

Original dataset

Examples

data('UCH')
PCA.res = SVDforPCA(UCH$outcomes)

FranceschiniS/LMWiRe documentation built on Oct. 30, 2019, 6:20 p.m.