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kpcaIG  

Variables Interpretability with Kernel PCA
View on CRAN: Click here


Download and install kpcaIG package within the R console
Install from CRAN:
install.packages("kpcaIG")

Install from Github:
library("remotes")
install_github("cran/kpcaIG")

Install by package version:
library("remotes")
install_version("kpcaIG", "1.0")



Attach the package and use:
library("kpcaIG")
Maintained by
Mitja Briscik
[Scholar Profile | Author Map]
First Published: 2024-07-21
Latest Update: 2024-07-21
Description:
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.
How to cite:
Mitja Briscik (2024). kpcaIG: Variables Interpretability with Kernel PCA. R package version 1.0, https://cran.r-project.org/web/packages/kpcaIG. Accessed 17 Feb. 2025.
Previous versions and publish date:
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Functions, R codes and Examples using the kpcaIG R package
Full kpcaIG package functions and examples
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