# 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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