auto.pca: Automatic Variable Reduction Using Principal Component Analysis

PCA done by eigenvalue decomposition of a data correlation matrix, here it automatically determines the number of factors by eigenvalue greater than 1 and it gives the uncorrelated variables based on the rotated component scores, Such that in each principal component variable which has the high variance are selected. It will be useful for non-statisticians in selection of variables. For more information, see the <http://www.ijcem.org/papers032013/ijcem_032013_06.pdf> web page.

Version:	0.3
Imports:	psych, plyr
Suggests:	knitr
Published:	2017-09-12
DOI:	10.32614/CRAN.package.auto.pca
Author:	Navinkumar Nedunchezhian
Maintainer:	Navinkumar Nedunchezhian <navinkumar.nedunchezhian at gmail.com>
License:	GPL-2
NeedsCompilation:	no
CRAN checks:	auto.pca results

Documentation:

Reference manual:

auto.pca.pdf

Downloads:

Package source:	auto.pca_0.3.tar.gz
Windows binaries:	r-devel: auto.pca_0.3.zip, r-release: auto.pca_0.3.zip, r-oldrel: auto.pca_0.3.zip
macOS binaries:	r-release (arm64): auto.pca_0.3.tgz, r-oldrel (arm64): auto.pca_0.3.tgz, r-release (x86_64): auto.pca_0.3.tgz, r-oldrel (x86_64): auto.pca_0.3.tgz

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