# entropy: Information measures In immunomind/immunarch: Bioinformatics Analysis of T-Cell and B-Cell Immune Repertoires

 entropy R Documentation

## Information measures

### Description

Compute information-based estimates and distances.

### Usage

entropy(.data, .base = 2, .norm = FALSE, .do.norm = NA, .laplace = 1e-12)

kl_div(.alpha, .beta, .base = 2, .do.norm = NA, .laplace = 1e-12)

js_div(.alpha, .beta, .base = 2, .do.norm = NA, .laplace = 1e-12, .norm.entropy = FALSE)

cross_entropy(.alpha, .beta, .base = 2, .do.norm = NA,
.laplace = 1e-12, .norm.entropy = FALSE)

### Arguments

 .data Numeric vector. Any distribution. .base Numeric. A base of logarithm. .norm Logical. If TRUE then normalises the entropy by the maximal value of the entropy. .do.norm If TRUE then normalises the input distributions to make them sum up to 1. .laplace Numeric. A value for the laplace correction. .alpha Numeric vector. A distribution of some random value. .beta Numeric vector. A distribution of some random value. .norm.entropy Logical. If TRUE then normalises the resulting value by the average entropy of input distributions.

A numeric value.

### Examples

P <- abs(rnorm(10))
Q <- abs(rnorm(10))
entropy(P)
kl_div(P, Q)
js_div(P, Q)
cross_entropy(P, Q)

immunomind/immunarch documentation built on March 20, 2024, 12:01 p.m.