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auto.pca  

Automatic Variable Reduction Using Principal Component Analysis
View on CRAN: Click here


Download and install auto.pca package within the R console
Install from CRAN:
install.packages("auto.pca")

Install from Github:
library("remotes")
install_github("cran/auto.pca")

Install by package version:
library("remotes")
install_version("auto.pca", "0.3")



Attach the package and use:
library("auto.pca")
Maintained by
Navinkumar Nedunchezhian
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-09-12
Latest Update: 2017-09-12
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 web page.
How to cite:
Navinkumar Nedunchezhian (2017). auto.pca: Automatic Variable Reduction Using Principal Component Analysis. R package version 0.3, https://cran.r-project.org/web/packages/auto.pca. Accessed 06 Nov. 2024.
Previous versions and publish date:
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Complete documentation for auto.pca
Functions, R codes and Examples using the auto.pca R package
Some associated functions: auto.pca . 
Some associated R codes: auto.pca.R .  Full auto.pca package functions and examples
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