| 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 |
Automatic Variable Reduction Using Principal Component Analysis
Description
Prints the uncorrelated variables from the input dataframe
Usage
auto.pca(input_data)
Arguments
input_data |
dataframe without ID Variables & Categorical Variables |
Examples
auto.pca(attitude)