Information measures — entropy • immunarch

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.

Value

A numeric value.

Examples

P <- abs(rnorm(10))
Q <- abs(rnorm(10))
entropy(P)
#> [1] 3.021958
kl_div(P, Q)
#> [1] 0.7606774
js_div(P, Q)
#> [1] 0.1411576
cross_entropy(P, Q)
#> [1] 3.782636