pcaBySvd: Singular value decomposition for PCA analysis

In bgovaerts/LMWiRe: Linear Models for Wide Responses

Singular value decomposition for PCA analysis

Description

Operates a Principal Component Analysis on the Y outcome/response matrix by a singular value decomposition (the pre-processing involves the mean-centering of Y). Outputs are represented with functions pcaScorePlot, pcaLoading1dPlot, pcaLoading2dPlot and pcaScreePlot.

Usage

pcaBySvd(Y, nPC = min(dim(Y)))

Arguments

Y	The nxm matrix with n observations and m response variables.
nPC	Number of Principal Components.

Value

A list with the following elements:

scores

Scores

loadings

Loadings

eigval

Eigenvalues

singvar

Singular values

var

Explained variances

cumvar

Cumulated explained variances

original.dataset

Original dataset

Examples

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

bgovaerts/LMWiRe documentation built on Sept. 17, 2022, 12:32 a.m.