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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 |
| Reference manual: | kpcaIG.pdf |
| Package source: | kpcaIG_1.0.tar.gz |
| Windows binaries: | r-devel: kpcaIG_1.0.zip, r-release: kpcaIG_1.0.zip, r-oldrel: kpcaIG_1.0.zip |
| macOS binaries: | r-release (arm64): kpcaIG_1.0.tgz, r-oldrel (arm64): kpcaIG_1.0.tgz, r-release (x86_64): kpcaIG_1.0.tgz, r-oldrel (x86_64): kpcaIG_1.0.tgz |
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These binaries (installable software) and packages are in development.
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