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(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.

Value

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

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. doi:10.1002/sam.11527

Author

Luca Scrucca

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(object = 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(object = 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)

# }