kmeans_discrete

Splitting of a vector of continuous values into k groups
using kmeans. Used to discretize node labels/weights of a fitness landscape
or node-weighted graph.

Generalizes application of gray-level co-occurrence matrix
(GLCM) metrics to objects outside of images. The current focus is to
apply GLCM metrics to the study of biological networks and fitness
landscapes that are used in studying evolutionary medicine and
biology, particularly the evolution of cancer resistance. The package was
developed as part of the author's publication in Physics in Medicine and Biology
Barker-Clarke et al. (2023) <doi:10.1088/1361-6560/ace305>.
A general reference to learn more about mathematical oncology can be found at
Rockne et al. (2019) <doi:10.1088/1478-3975/ab1a09>.

Rowan Barker-Clarke

gtexture

Generalized Application of Co-Occurrence Matrices and Haralick
Texture

Raoul Wadhwa

Davis Weaver

Jacob Scott

kmeans_discrete function

<dl><dt>nlevels</dt>
<dd>number of levels to split continuous vector into</dd></dl>

Arguments

Kmeans clustering discretization — kmeans_discrete

<dl>

<dt>nlevels</dt>
<dd>number of levels to split continuous vector into</dd>

</dl>

kmeans_discrete: Kmeans clustering discretization

Description

Usage

Value

Arguments