Summarizing Gaussian Finite Mixture Model Fits

Summary method for class "Mclust".

Usage

# S3 method for Mclust
summary(object, classification = TRUE, parameters = FALSE, ...)
# S3 method for summary.Mclust
print(x, digits = getOption("digits"), ...)

Arguments

object

An object of class 'Mclust' resulting of a call to Mclust or densityMclust.

x

An object of class 'summary.Mclust', usually, a result of a call to summary.Mclust.

classification

Logical; if TRUE a table of MAP classification/clustering of observations is printed.

parameters

Logical; if TRUE, the parameters of mixture components are printed.

digits

The number of significant digits to use when printing.

...

Further arguments passed to or from other methods.

Author

Luca Scrucca

See also

Mclust, densityMclust.

Examples

# \donttest{
mod1 = Mclust(iris[,1:4])
summary(mod1)
#> ---------------------------------------------------- 
#> Gaussian finite mixture model fitted by EM algorithm 
#> ---------------------------------------------------- 
#> 
#> Mclust VEV (ellipsoidal, equal shape) model with 2 components: 
#> 
#>  log-likelihood   n df       BIC       ICL
#>        -215.726 150 26 -561.7285 -561.7289
#> 
#> Clustering table:
#>   1   2 
#>  50 100 
summary(mod1, parameters = TRUE, classification = FALSE)
#> ---------------------------------------------------- 
#> Gaussian finite mixture model fitted by EM algorithm 
#> ---------------------------------------------------- 
#> 
#> Mclust VEV (ellipsoidal, equal shape) model with 2 components: 
#> 
#>  log-likelihood   n df       BIC       ICL
#>        -215.726 150 26 -561.7285 -561.7289
#> 
#> Mixing probabilities:
#>         1         2 
#> 0.3333319 0.6666681 
#> 
#> Means:
#>                   [,1]     [,2]
#> Sepal.Length 5.0060022 6.261996
#> Sepal.Width  3.4280049 2.871999
#> Petal.Length 1.4620007 4.905992
#> Petal.Width  0.2459998 1.675997
#> 
#> Variances:
#> [,,1]
#>              Sepal.Length Sepal.Width Petal.Length Petal.Width
#> Sepal.Length   0.15065114  0.13080115   0.02084463  0.01309107
#> Sepal.Width    0.13080115  0.17604529   0.01603245  0.01221458
#> Petal.Length   0.02084463  0.01603245   0.02808260  0.00601568
#> Petal.Width    0.01309107  0.01221458   0.00601568  0.01042365
#> [,,2]
#>              Sepal.Length Sepal.Width Petal.Length Petal.Width
#> Sepal.Length    0.4000438  0.10865444    0.3994018  0.14368256
#> Sepal.Width     0.1086544  0.10928077    0.1238904  0.07284384
#> Petal.Length    0.3994018  0.12389040    0.6109024  0.25738990
#> Petal.Width     0.1436826  0.07284384    0.2573899  0.16808182

mod2 = densityMclust(faithful, plot = FALSE)
summary(mod2)
#> ------------------------------------------------------- 
#> Density estimation via Gaussian finite mixture modeling 
#> ------------------------------------------------------- 
#> 
#> Mclust EEE (ellipsoidal, equal volume, shape and orientation) model with 3
#> components: 
#> 
#>  log-likelihood   n df       BIC       ICL
#>       -1126.326 272 11 -2314.316 -2357.824
summary(mod2, parameters = TRUE)
#> ------------------------------------------------------- 
#> Density estimation via Gaussian finite mixture modeling 
#> ------------------------------------------------------- 
#> 
#> Mclust EEE (ellipsoidal, equal volume, shape and orientation) model with 3
#> components: 
#> 
#>  log-likelihood   n df       BIC       ICL
#>       -1126.326 272 11 -2314.316 -2357.824
#> 
#> Mixing probabilities:
#>         1         2         3 
#> 0.1656784 0.3563696 0.4779520 
#> 
#> Means:
#>                [,1]      [,2]      [,3]
#> eruptions  3.793066  2.037596  4.463245
#> waiting   77.521051 54.491158 80.833439
#> 
#> Variances:
#> [,,1]
#>            eruptions    waiting
#> eruptions 0.07825448  0.4801979
#> waiting   0.48019785 33.7671464
#> [,,2]
#>            eruptions    waiting
#> eruptions 0.07825448  0.4801979
#> waiting   0.48019785 33.7671464
#> [,,3]
#>            eruptions    waiting
#> eruptions 0.07825448  0.4801979
#> waiting   0.48019785 33.7671464
# }