MclustMEM {mclustAddons}

Modal EM algorithm for Gaussian Mixtures fitted via mclust package

Description

Modal-clustering estimation by applying the Modal EM algorithm to Gaussian mixtures fitted using the mclust package.

Usage

MclustMEM(mclustObject, data = NULL, ...)

## S3 method for class 'MclustMEM'
print(x, digits = getOption("digits"), ...)

## S3 method for class 'MclustMEM'
summary(object, ...)

Arguments

mclustObject

An object of class 'Mclust' or 'densityMclust' obtained by fitting a Gaussian mixture via, respectively, Mclust and densityMclust.

data

If provided, a numeric vector, matrix, or data frame of observations. If a matrix or data frame, rows correspond to observations (n) and columns correspond to variables (d). If not provided, the data used for fitting the Gaussian mixture model, and provided with the object argument, are used.

x, object

An object of class 'MclustMEM'.

digits

The number of significant digits to use for printing.

...

Further arguments passed to or from other methods.

Value

Returns an object of class 'MclustMEM'. See also the output returned by GaussianMixtureMEM.

Author(s)

Luca Scrucca

References

Scrucca L. (2021) A fast and efficient Modal EM algorithm for Gaussian mixtures. Statistical Analysis and Data Mining, 14:4, 305–314. https://doi.org/10.1002/sam.11527

See Also

GaussianMixtureMEM, plot.MclustMEM.

Examples

data(Baudry_etal_2010_JCGS_examples, package = "mclust")

plot(ex4.1)
GMM <- Mclust(ex4.1)
plot(GMM, what = "classification")
MEM <- MclustMEM(GMM)
MEM
summary(MEM)
plot(MEM)

plot(ex4.4.2)
GMM <- Mclust(ex4.4.2)
plot(GMM, what = "classification")
MEM <- MclustMEM(GMM)
MEM
summary(MEM)
plot(MEM, addDensity = FALSE)