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:
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)
| # Install 'kpcaIG' in R: |
| install.packages('kpcaIG', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 5 months agofrom:3d3883843b. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 31 2024 |
| R-4.5-linux | OK | Oct 31 2024 |
Exports:kernelpcakpca_igradplot_kpca2Dplot_kpca3D
Dependencies:base64encbslibcachemclicolorspacecrayondigestevaluatefansifarverfastmapfontawesomefsggplot2gluegridExtragtablehighrhmshtmltoolshtmlwidgetsisobandjquerylibjsonlitekernlabknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmepillarpkgconfigprettyunitsprogressR6rappdirsRColorBrewerrglrlangrmarkdownsassscalestibbletinytexutf8vctrsviridisviridisLiteWallomicsDatawithrxfunyaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Kernel Principal Components Analysis | kernelpca |
| KPCA-IG: Variables Interpretability in Kernel PCA | kpca_igrad |
| 2D Kernel PCA Plot with Variables Representation | plot_kpca2D |
| 3D Kernel PCA Plot with Variables Representation | plot_kpca3D |