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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=kpcaIG to link to this page.
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