This package implements two methods for performing a constrained principal component analysis (PCA), where non-negativity and/or sparsity constraints are enforced on the principal axes (PAs). The function 'nsprcomp' computes one principal component (PC) after the other. Each PA is optimized such that the corresponding PC has maximum additional variance not explained by the previous components. In contrast, the function 'nscumcomp' jointly computes all PCs such that the cumulative variance is maximal. Both functions have the same interface as the 'prcomp' function from the 'stats' package (plus some extra parameters), and both return the result of the analysis as an object of class 'nsprcomp', which inherits from 'prcomp'.

Documentation

Manual: nsprcomp.pdf
Vignette: None available.

Maintainer: Christian Sigg <christian at sigg-iten.ch>

Author(s): Christian Sigg*, R Core team*

Install package and any missing dependencies by running this line in your R console:

install.packages("nsprcomp")

Depends
Imports stats
Suggests MASS, testthat(>=0.8), roxygen2
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Package nsprcomp
Materials
URL http://sigg-iten.ch/research/
Task Views Psychometrics
Version 0.5
Published 2014-07-17
License GPL (>= 2)
BugReports
SystemRequirements
NeedsCompilation no
Citation
CRAN checks nsprcomp check results
Package source nsprcomp_0.5.tar.gz