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swaprinc
View on CRAN: Click
here
Download and install swaprinc package within the R console
Install from CRAN:
install.packages("swaprinc")
Install from Github:
library("remotes")
install_github("cran/swaprinc")
Install by package version:
library("remotes")
install_version("swaprinc", "1.0.1")
Attach the package and use:
library("swaprinc")
Maintained by
Mackson Ncube
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.swaprinc . https://github.com/mncube/swaprinc/issues . https://github.com/mncube/swaprinc . swaprinc results . swaprinc.pdf . swaprinc_1.0.1.tar.gz . swaprinc_1.0.1.zip . swaprinc_1.0.1.zip . swaprinc_1.0.1.zip . swaprinc_1.0.1.tgz . swaprinc_1.0.1.tgz . swaprinc_1.0.1.tgz . swaprinc_1.0.1.tgz . swaprinc_1.0.1.tgz . swaprinc_1.0.1.tgz . https://CRAN.R-project.org/package=swaprinc .
First Published: 2023-04-17
Latest Update: 2023-04-17
Description:
Obtaining accurate and stable estimates of regression coefficients can be challenging when the suggested statistical model has issues related to multicollinearity, convergence, or overfitting. One solution is to use principal component analysis (PCA) results in the regression, as discussed in Chan and Park (2005) <doi:10.1080/01446190500039812>. The swaprinc() package streamlines comparisons between a raw regression model with the full set of raw independent variables and a principal component regression model where principal components are estimated on a subset of the independent variables, then swapped into the regression model in place of those variables. The swaprinc() function compares one raw regression model to one principal component regression model, while the compswap() function compares one raw regression model to many principal component regression models. Package functions include parameters to center, scale, and undo centering and scaling, as described by Harvey and Hansen (2022) <https://cran.r-project.org/package=LearnPCA/vignettes/Vig_03_Step_By_Step_PCA.pdf>. Additionally, the package supports using Gifi methods to extract principal components from categorical variables, as outlined by Rossiter (2021) <https://www.css.cornell.edu/faculty/dgr2/_static/files/R_html/NonlinearPCA.html#2_Package>.
How to cite:
Mackson Ncube (2023). swaprinc: Swap Principal Components into Regression Models. R package version 1.0.1, https://cran.r-project.org/web/packages/swaprinc. Accessed 29 Mar. 2025.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for swaprinc
Functions, R codes and Examples using
the swaprinc R package
Some associated functions: compswap . pipe . swaprinc . swaprinc_loud .
Some associated R codes: compswap.R . swaprinc.R . utils-pipe.R . Full swaprinc package functions and examples
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