entropy: Information measures

In abrown435/immunarch-test: Bioinformatics Analysis of T-Cell and B-Cell Immune Repertoires

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 normalise the entropy by the maximal value of the entropy.
.do.norm	If TRUE then normalise 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 normalise the resultant value by the average entropy of input distributions.

Value

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)

abrown435/immunarch-test documentation built on July 29, 2020, 12:04 a.m.