Modal EM algorithm for Gaussian Mixtures fitted via mclust package

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 MclustMEM
print(x, digits = getOption("digits"), ...)

# S3 method for 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

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

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
#> Call:
#> MclustMEM(mclustObject = GMM)
#> 
#> 'MclustMEM' object containing: 
#>  [1] "data"           "n"              "d"              "parameters"    
#>  [5] "modelName"      "G"              "iter"           "nmodes"        
#>  [9] "modes"          "path"           "logdens"        "logvol"        
#> [13] "classification"
summary(MEM)
#> ── Modal EM for GMMs ─────────────────── 
#> 
#> Data dimensions = 600 x 2 
#> Mclust model    = EEV,6 
#> MEM iterations  = 17 
#> Number of modes = 4 
#> 
#> Modes:
#>                X1          X2
#> mode1  8.06741504 -0.01772230
#> mode2  8.07370160  4.98485099
#> mode3  1.10622966  4.97230749
#> mode4 -0.01639289  0.06464381
#> 
#> Modal clustering:
#>   1   2   3   4 
#> 118 122 228 132 
plot(MEM)


plot(ex4.4.2)

GMM <- Mclust(ex4.4.2)
plot(GMM, what = "classification")

MEM <- MclustMEM(GMM)
MEM
#> Call:
#> MclustMEM(mclustObject = GMM)
#> 
#> 'MclustMEM' object containing: 
#>  [1] "data"           "n"              "d"              "parameters"    
#>  [5] "modelName"      "G"              "iter"           "nmodes"        
#>  [9] "modes"          "path"           "logdens"        "logvol"        
#> [13] "classification"
summary(MEM)
#> ── Modal EM for GMMs ─────────────────── 
#> 
#> Data dimensions = 300 x 3 
#> Mclust model    = EVI,3 
#> MEM iterations  = 15 
#> Number of modes = 2 
#> 
#> Modes:
#>              X1        X2        X3
#> mode1 0.7964915 0.7444244 0.4547285
#> mode2 0.4996361 0.5014374 0.4957522
#> 
#> Modal clustering:
#>   1   2 
#>  78 222 
plot(MEM, addDensity = FALSE)

# }