pcaPP v1.9-73

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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 in pcaPP

Name Description
ScaleAdv centers and rescales data
cor.fk Fast estimation of Kendall's tau rank correlation coefficient
PCAgrid (Sparse) Robust Principal Components using the Grid search algorithm
PCAproj Robust Principal Components using the algorithm of Croux and Ruiz-Gazen (2005)
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
PCdiagplot Diagnostic plot for principal components
qn scale estimation using the robust Qn estimator
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
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Vignettes of pcaPP

Name
load.package.name.R
matlab.example.txt
matlab.rnw
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Details

Date 2018-01-04
License GPL (>= 3)
NeedsCompilation yes
Repository CRAN
Packaged 2018-01-05 06:39:02 UTC; TodorovV
Date/Publication 2018-01-14 18:27:29 UTC

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