kl.divergence

From spatialEco v1.3-2 by Jeffrey S Evans

Kullback-Leibler divergence (relative entropy)

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().

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.

Value

pairwise Kullback-Leibler divergence index (matrix)

References

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

Examples

# NOT RUN {
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]

# }

Documentation reproduced from package spatialEco, version 1.3-2, License: GPL-3

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