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 <> web page.

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


Reference manual: auto.pca.pdf


Package source: auto.pca_0.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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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