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>.
