miceadds: pca.covridge – R documentation

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pca.covridge

Principal Component Analysis with Ridge Regularization

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, wt=NULL )

Arguments

`x`	A numeric matrix
`ridge`	Ridge regularization parameter for the covariance matrix
`wt`	Optional vector of weights

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)

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

Some Additional Multiple Imputation Functions, Especially for 'mice'

v3.11-6

GPL (>= 2)

Authors

Alexander Robitzsch [aut,cre] (<https://orcid.org/0000-0002-8226-3132>), Simon Grund [aut] (<https://orcid.org/0000-0002-1290-8986>), Thorsten Henke [ctb]

Initial release

2021-01-21 11:48:47