mclustAddons (version 0.10)

MclustMEM: 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 MclustMEM
summary(object, ...)

Value

Returns an object of class 'MclustMEM' with elements described in GaussianMixtureMEM().

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.

Author

Luca Scrucca

Details

For more details see vignette("mclustAddons")

References

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

See Also

GaussianMixtureMEM(), plot.MclustMEM().

Examples

# \donttest{
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)
# }