EntropyGMM | R Documentation |
Compute an estimate of the (differential) entropy from a Gaussian Mixture Model (GMM) fitted using the mclust package.
EntropyGMM(object, ...) ## S3 method for class 'densityMclust' EntropyGMM(object, ...) ## S3 method for class 'Mclust' EntropyGMM(object, ...) ## S3 method for class 'densityMclustBounded' EntropyGMM(object, ...) ## S3 method for class 'matrix' EntropyGMM(object, ...) ## S3 method for class 'data.frame' EntropyGMM(object, ...) EntropyGauss(sigma) nats2bits(x) bits2nats(x)
object |
An object of class |
sigma |
A symmetric covariance matrix. |
x |
A vector of values. |
... |
Further arguments passed to or from other methods. |
EntropyGMM()
returns an estimate of the entropy based on a estimated Gaussian mixture model (GMM) fitted using the mclust package. If a matrix of data values is provided, a GMM is preliminary fitted to the data and then the entropy computed.
EntropyGauss()
returns the entropy for a multivariate Gaussian distribution with covariance matrix sigma
.
nats2bits()
and bits2nats()
convert input values in nats to bits, and viceversa. Information-theoretic quantities have different units depending on the base of the logarithm used: nats are expressed in base-2 logarithms, whereas bits in natural logarithms.
Luca Scrucca
Robin S. and Scrucca L. (2023) Mixture-based estimation of entropy. Computational Statistics & Data Analysis, 177, 107582. https://doi.org/10.1016/j.csda.2022.107582
Mclust
, densityMclust
.
X = iris[,1:4] mod = densityMclust(X, plot = FALSE) h = EntropyGMM(mod) h bits2nats(h) EntropyGMM(X)
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