R: Information measures

entropy {immunarch} | 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 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)

[Package immunarch version 0.6.7 Index]