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 |
| Imports: |
grDevices, rgl, kernlab, ggplot2, stats, progress, viridis, WallomicsData |
| Published: |
2024-07-21 |
| 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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