MclustMEM: Modal EM algorithm for Gaussian Mixtures fitted via _mclust_...

In mclustAddons: Addons for the 'mclust' Package

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(object, data = NULL, ...)

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

Arguments

object	An object of class 'Mclust' or 'densityMclust' obtained by fitting a Gaussian mixture via, respectively, mclust::Mclust() and mclust::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.
...	Further arguments passed to or from other methods.

Details

For more details see vignette("mclustAddons")

Value

Returns an object of class 'MclustMEM' with elements described in 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. \Sexpr[results=rd]{tools:::Rd_expr_doi("doi: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)

mclustAddons documentation built on Sept. 20, 2024, 5:06 p.m.