Package: auto.pca 0.3
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 <http://www.ijcem.org/papers032013/ijcem_032013_06.pdf> web page.
Authors:
auto.pca_0.3.tar.gz
auto.pca_0.3.zip(r-4.5)auto.pca_0.3.zip(r-4.4)auto.pca_0.3.zip(r-4.3)
auto.pca_0.3.tgz(r-4.4-any)auto.pca_0.3.tgz(r-4.3-any)
auto.pca_0.3.tar.gz(r-4.5-noble)auto.pca_0.3.tar.gz(r-4.4-noble)
auto.pca_0.3.tgz(r-4.4-emscripten)auto.pca_0.3.tgz(r-4.3-emscripten)
auto.pca.pdf |auto.pca.html✨
auto.pca/json (API)
# Install 'auto.pca' in R: |
install.packages('auto.pca', repos = c('https://navinkumarnedunchezhian.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 7 years agofrom:d15c12316d. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Sep 13 2024 |
R-4.5-win | OK | Sep 13 2024 |
R-4.5-linux | OK | Sep 13 2024 |
R-4.4-win | OK | Sep 13 2024 |
R-4.4-mac | OK | Sep 13 2024 |
R-4.3-win | OK | Sep 13 2024 |
R-4.3-mac | OK | Sep 13 2024 |
Exports:auto.pca
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Automatic Variable Reduction Using Principal Component Analysis | auto.pca |