kpcaIG: Variables Interpretability with Kernel PCA
The kernelized version of principal component analysis (KPCA) has proven to be a valid nonlinear alternative for tackling the nonlinearity of biological sample spaces. However, it poses new challenges in terms of the interpretability of the original variables. 'kpcaIG' aims to provide a tool to select the most relevant variables based on the kernel PCA representation of the data as in Briscik et al. (2023) <doi:10.1186/s12859-023-05404-y>. It also includes functions for 2D and 3D visualization of the original variables (as arrows) into the kernel principal components axes, highlighting the contribution of the most important ones.
| Version: | 1.0.1 |
| Imports: | grDevices,rgl,kernlab,ggplot2, stats,progress,viridis,WallomicsData, utils |
| Published: | 2025-03-28 |
| DOI: | 10.32614/CRAN.package.kpcaIG |
| Author: | Mitja Briscik [aut, cre], Mohamed Heimida [aut], Sébastien Déjean [aut] |
| Maintainer: | Mitja Briscik <mitja.briscik at math.univ-toulouse.fr> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | kpcaIG results |
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