| Name : R-swaprinc
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| Version : 1.0.1
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp156.6.1
| Date : 2024-09-10 14:57:15
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| Group : Development/Libraries/Other
| Source RPM : R-swaprinc-1.0.1-lp156.6.1.src.rpm
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| Size : 0.04 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : Swap Principal Components into Regression Models
|
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>.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/15.6/x86_64 |
