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pcaPP

Robust PCA by Projection Pursuit

Provides functions for robust PCA by projection pursuit. The methods are described in Croux et al. (2006) <doi:10.2139/ssrn.968376>, Croux et al. (2013) <doi:10.1080/00401706.2012.727746>, Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>.

Functions (15)

PCAgrid

(Sparse) Robust Principal Components using the Grid search algorithm

PCAproj

Robust Principal Components using the algorithm of Croux and Ruiz-Gazen (2005)

PCdiagplot

Diagnostic plot for principal components

Qn

scale estimation using the robust Qn estimator

ScaleAdv

centers and rescales data

cor.fk

Fast estimation of Kendall's tau rank correlation coefficient

covPC

Covariance Matrix Estimation from princomp Object

covPCA

Robust Covariance Matrix Estimation

data.Zou

Test Data Generation for Sparse PCA examples

l1median

Multivariate L1 Median

l1median_NLM

Multivariate L1 Median

objplot

Objective Function Plot for Sparse PCs

opt.TPO

Model Selection for Sparse (Robust) Principal Components

plot.opt.TPO

Tradeoff Curves for Sparse PCs

plotcov

Compare two Covariance Matrices in Plots

pcaPP

Robust PCA by Projection Pursuit

v1.9-74
GPL (>= 3)
Authors
Peter Filzmoser, Heinrich Fritz, Klaudius Kalcher
Initial release
2021-04-22

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