kl.divergence: Kullback-Leibler divergence (relative entropy)
In spatialEco: Spatial Analysis and Modelling Utilities

kl.divergence

R Documentation

Kullback-Leibler divergence (relative entropy)

Description

Calculates the Kullback-Leibler divergence (relative entropy)

Usage

kl.divergence(object, eps = 10^-4, overlap = TRUE)

Arguments

`object`	Matrix or dataframe object with >=2 columns
`eps`	Probabilities below this threshold are replaced by this threshold for numerical stability.
`overlap`	Logical, do not determine the KL divergence for those pairs where for each point at least one of the densities has a value smaller than eps.

Details

Calculates the Kullback-Leibler divergence (relative entropy) between unweighted theoretical component distributions. Divergence is calculated as: int [f(x) (log f(x) - log g(x)) dx] for distributions with densities f() and g().

Value

pairwise Kullback-Leibler divergence index (matrix)

Author(s)

Jeffrey S. Evans <jeffrey_evans@tnc.org>

References

Kullback S., and R. A. Leibler (1951) On information and sufficiency. The Annals of Mathematical Statistics 22(1):79-86

Examples

x <- seq(-3, 3, length=200)
y <- cbind(n=dnorm(x), t=dt(x, df=10))
  matplot(x, y, type='l')
    kl.divergence(y)
   
# extract value for last column
  kl.divergence(y[,1:2])[3:3]

spatialEco documentation built on Nov. 18, 2023, 1:13 a.m.