flexmix (version 2.3-17)

KLdiv: Kullback-Leibler Divergence

Description

Estimate the Kullback-Leibler divergence of several distributions.

Usage

# S4 method for matrix
KLdiv(object, eps = 10^-4, overlap = TRUE,...)
# S4 method for flexmix
KLdiv(object, method = c("continuous", "discrete"), ...)

Arguments

object

See Methods section below.

method

The method to be used; "continuous" determines the Kullback-Leibler divergence between the unweighted theoretical component distributions and the unweighted posterior probabilities at the observed points are used by "discrete".

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.

...

Passed to the matrix method.

Value

A matrix of KL divergences where the rows correspond to using the respective distribution as \(f()\) in the formula above.

Methods

object = "matrix":

Takes as input a matrix of density values with one row per observation and one column per distribution.

object = "flexmix":

Returns the Kullback-Leibler divergence of the mixture components.

Details

Estimates $$\int f(x) (\log f(x) - \log g(x)) dx$$ for distributions with densities \(f()\) and \(g()\).

References

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

Friedrich Leisch. Exploring the structure of mixture model components. In Jaromir Antoch, editor, Compstat 2004--Proceedings in Computational Statistics, 1405--1412. Physika Verlag, Heidelberg, Germany, 2004. ISBN 3-7908-1554-3.

Examples

# NOT RUN {
## Gaussian and Student t are much closer to each other than
## to the uniform:

x <- seq(-3, 3, length = 200)
y <- cbind(u = dunif(x), n = dnorm(x), t = dt(x, df = 10))

matplot(x, y, type = "l")
KLdiv(y)

if (require("mlbench")) {
set.seed(2606)
x <-  mlbench.smiley()$x
model1 <- flexmix(x ~ 1, k = 9, model = FLXmclust(diag = FALSE),
                  control  =  list(minprior = 0))
plotEll(model1, x)
KLdiv(model1)
}
# }