swaprinc: Swap Principal Components into Regression Models
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>.
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