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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 |
| Reference manual: | auto.pca.pdf |
| 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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.