EntropyGMM {mclustAddons}R Documentation

Gaussian mixture-based estimation of entropy

Description

Compute an estimate of the (differential) entropy from a Gaussian Mixture Model (GMM) fitted using the mclust package.

Usage

EntropyGMM(object, ...)

## S3 method for class 'densityMclust'
EntropyGMM(object, ...)

## S3 method for class 'densityMclustBounded'
EntropyGMM(object, ...)

## S3 method for class 'Mclust'
EntropyGMM(object, ...)

## S3 method for class 'data.frame'
EntropyGMM(object, ...)

## S3 method for class 'matrix'
EntropyGMM(object, ...)

EntropyGauss(sigma)

nats2bits(x)

bits2nats(x)

Arguments

object

An object of class 'Mclust', 'densityMclust', or 'densityMclustBounded', obtained by fitting a Gaussian mixture via, respectively, mclust::Mclust(), mclust::densityMclust(), and densityMclustBounded().

If a matrix or data.frame is provided as input, a GMM using the provided data is estimated preliminary to computing the entropy. In this case further arguments can be provided to control the fitted model (e.g. number of mixture components and/or covariances decomposition).

...

Further arguments passed to or from other methods.

sigma

A symmetric covariance matrix.

x

A vector of values.

Details

For more details see vignette("mclustAddons")

Value

Author(s)

Luca Scrucca

References

Robin S. and Scrucca L. (2023) Mixture-based estimation of entropy. Computational Statistics & Data Analysis, 177, 107582. doi:10.1016/j.csda.2022.107582

See Also

mclust::Mclust(), mclust::densityMclust().

Examples


X = iris[,1:4]
mod = densityMclust(X, plot = FALSE)
h = EntropyGMM(mod)
h
bits2nats(h)
EntropyGMM(X)



[Package mclustAddons version 0.9 Index]