loop: Loop to computate all entropy metrics outputs

In edlansiaux/muinther: Mutual Information Theory and Correlation Computation for Numerous Variables

Loop to computate all entropy metrics outputs

Description

Table of mutual information theory coefficients for variables association study. Computated outputs gather: X variable name, Y variable name, X information entropy, Y information entropy, Computed marginal EPMF of X, Computed marginal EPMF of Y, Chi2, Chi2 p-value, Information entropy of X, Information entropy of Y, Joint information entropy of X and Y, Conditional information entropy of Y given X, Conditional information entropy of X given Y, Mutual information of X and Y, Normalized mutual information of X and Y.

Usage

loop(input, m, n)

Arguments

input	is the source data frame
m	is the first studied X variable column number
n	is the studied variables number

Author(s)

Edouard Lansiaux, Philippe Pierre Pébaÿ

References

Shannon, C. A mathematical theory of communication. Bell Labs Tech. J. 27, 379–423, DOI: 10.1002/j.1538-7305.1948.tb01338.x (1948).

Examples

## Entropy outputs coumputation and visualization
### Computation
loop(docs_phenotype_file_1,1,8)
### Visualization on a heatmap
entropy_outputs <- readr::read_csv('entropy_outputs.csv')
heatmap2(entropy_outputs)

edlansiaux/muinther documentation built on Dec. 24, 2024, 4:46 p.m.