pca.covridge: Principal Component Analysis with Ridge Regularization

Description Usage Arguments Value Author(s) See Also Examples

View source: R/pca.covridge.R

Description

Performs a principal component analysis for a dataset while a ridge parameter is added on the diagonal of the covariance matrix.

Usage

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pca.covridge(x, ridge = 1E-10 )

Arguments

x

A numeric matrix

ridge

Ridge regularization parameter for the covariance matrix

Value

A list with following entries:

loadings

Matrix of factor loadings

scores

Matrix of principal component scores

sdev

Vector of standard deviations of factors (square root of eigenvalues)

Author(s)

Alexander Robitzsch

See Also

Principal component analysis in stats: stats::princomp

For calculating first eigenvalues of a symmetric matrix see also sirt::eigenvalues.sirt in the sirt package.

Examples

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## Not run: 
#############################################################################
# EXAMPLE 1: PCA on imputed internet data
#############################################################################

library(mice)
data(data.internet)
dat <- as.matrix( data.internet)

# single imputation in mice
imp <- mice::mice( dat , m=1 , maxit=10 )

# apply PCA
pca.imp <- miceadds::pca.covridge( complete(imp) )
  ##   > pca.imp$sdev
  ##      Comp.1    Comp.2    Comp.3    Comp.4    Comp.5    Comp.6    Comp.7 
  ##   3.0370905 2.3950176 2.2106816 2.0661971 1.8252900 1.7009921 1.6379599 

# compare results with princomp
pca2.imp <- stats::princomp( complete(imp) )
  ##   > pca2.imp
  ##   Call:
  ##   stats::princomp(x = complete(imp))
  ##   
  ##   Standard deviations:
  ##      Comp.1    Comp.2    Comp.3    Comp.4    Comp.5    Comp.6    Comp.7 
  ##   3.0316816 2.3907523 2.2067445 2.0625173 1.8220392 1.6979627 1.6350428 

## End(Not run)

miceadds documentation built on Aug. 25, 2017, 1:03 a.m.