# pca.covridge: Principal Component Analysis with Ridge Regularization In alexanderrobitzsch/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

 `1` ```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)

Principal component analysis in stats: `stats::princomp`
For calculating first eigenvalues of a symmetric matrix see also `sirt::eigenvalues.sirt` in the sirt package.
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29``` ```## 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) ```