pca.covridge: Principal Component Analysis with Ridge Regularization

In miceadds: Some Additional Multiple Imputation Functions, Especially for 'mice'

Description

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

Usage

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

## 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. 9, 2017, 5:04 p.m.