Package: nsprcomp
Authors@R: c( person("Christian", "Sigg", email =
"christian@sigg-iten.ch", role = c("aut", "cre")), person("R
Core team", role = "aut"))
Version: 0.3
Date: 2013-05-28
Title: Non-Negative Sparse PCA
Description: This package implements two methods for performing a
principal component analysis (PCA) with constraints, 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 variance under the
given constraints. Then the data matrix is deflated using one
of several deflation methods, and the process is repeated with
the deflated data matrix to compute the next PC.  In contrast,
the function 'nscumcomp' jointly computes all PCs such that the
cumulative variance is maximal, subject to an upper bound on
the divergence from orthonormality of the rotation matrix.
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'.
URL: http://sigg-iten.ch/research/
License: GPL (>= 2)
Imports: stats
Suggests: testthat, roxygen2, MASS
Collate: 'nsprcomp-package.R' 'nsprcomp.R' 'nscumcomp.R'
Packaged: 2013-05-29 06:27:25 UTC; chrsigg
Author: Christian Sigg [aut, cre], R Core team [aut]
Maintainer: Christian Sigg <christian@sigg-iten.ch>
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-05-29 09:07:46

Description: 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
Collate: 'asdev.R' 'cardinality.R' 'normv.R' 'nscumcomp.R'