# summary.Mclust: Summarizing Gaussian Finite Mixture Model Fits In mclust: Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

## Description

Summary method for class `"Mclust"`.

## Usage

 ```1 2 3 4``` ```## S3 method for class 'Mclust' summary(object, classification = TRUE, parameters = FALSE, ...) ## S3 method for class '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(s)

Luca Scrucca

`Mclust`, `densityMclust`.

## Examples

 ```1 2 3 4 5 6 7``` ```mod1 = Mclust(iris[,1:4]) summary(mod1) summary(mod1, parameters = TRUE, classification = FALSE) mod2 = densityMclust(faithful) summary(mod2) summary(mod2, parameters = TRUE) ```

### Example output

```Package 'mclust' version 5.4.3
Type 'citation("mclust")' for citing this R package in publications.
----------------------------------------------------
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
----------------------------------------------------
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

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
-------------------------------------------------------
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

Clustering table:
1   2   3
40  97 135
-------------------------------------------------------
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

Clustering table:
1   2   3
40  97 135

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
```

mclust documentation built on Dec. 17, 2021, 5:19 p.m.