Package: auto.pca
Type: Package
Version: 0.3
Title: Automatic Variable Reduction Using Principal Component Analysis
Date: 2017-09-03
Author: Navinkumar Nedunchezhian
Maintainer: Navinkumar Nedunchezhian <navinkumar.nedunchezhian@gmail.com>
Description: 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.
License: GPL-2
LazyData: TRUE
Imports: psych,plyr
Suggests: knitr
NeedsCompilation: no
Packaged: 2017-09-12 02:08:08 UTC; NSD
Repository: CRAN
Date/Publication: 2017-09-12 09:24:21 UTC
Built: R 4.6.0; ; 2026-04-25 13:28:12 UTC; unix
