Package: kpcaIG 1.0

Mitja Briscik

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.

Authors:Mitja Briscik [aut, cre], Mohamed Heimida [aut], Sébastien Déjean [aut]

kpcaIG_1.0.tar.gz
kpcaIG_1.0.tar.gz(r-4.5-noble)kpcaIG_1.0.tar.gz(r-4.4-noble)
kpcaIG_1.0.tgz(r-4.4-emscripten)kpcaIG_1.0.tgz(r-4.3-emscripten)
kpcaIG.pdf |kpcaIG.html✨
kpcaIG/json (API)

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4 exports 1 stars 0.09 score 59 dependencies 225 downloads

Last updated 2 months agofrom:3d3883843b. Checks:OK: 2. Indexed: yes.

Exports:kernelpcakpca_igradplot_kpca2Dplot_kpca3D

Dependencies:base64encbslibcachemclicolorspacecrayondigestevaluatefansifarverfastmapfontawesomefsggplot2gluegridExtragtablehighrhmshtmltoolshtmlwidgetsisobandjquerylibjsonlitekernlabknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmepillarpkgconfigprettyunitsprogressR6rappdirsRColorBrewerrglrlangrmarkdownsassscalestibbletinytexutf8vctrsviridisviridisLiteWallomicsDatawithrxfunyaml