SVDforPCA: Singular value decomposition for PCA analysis

In ManonMartin/PAMMULAD:

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('iris')
PCA.res = SVDforPCA(iris[,1:4])

ManonMartin/PAMMULAD documentation built on May 23, 2019, 9 p.m.