R: Examples for 'mclust::plot.MclustSSC'

### ** Examples

X <- iris[,1:4]
class <- iris$Species
# randomly remove class labels
set.seed(123)
class[sample(1:length(class), size = 120)] <- NA
table(class, useNA = "ifany")

class
    setosa versicolor  virginica       <NA> 
        10         15          5        120

clPairs(X, ifelse(is.na(class), 0, class),
        symbols = c(0, 16, 17, 18), colors = c("grey", 4, 2, 3),
        main = "Partially classified data")

plot of chunk example-mclust-plot.MclustSSC-1

# Fit semi-supervised classification model
mod_SSC  <- MclustSSC(X, class)

fitting ...

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summary(mod_SSC, parameters = TRUE)

---------------------------------------------------------------- 
Gaussian finite mixture model for semi-supervised classification 
---------------------------------------------------------------- 

 log-likelihood   n df       BIC
       -193.521 150 38 -577.4461
            
Classes        n     % Model G
  setosa      10  6.67   VEV 1
  versicolor  15 10.00   VEV 1
  virginica    5  3.33   VEV 1
  <NA>       120 80.00        

Mixing probabilities:
    setosa versicolor  virginica 
 0.3333333  0.3876695  0.2789972 

Means:
             setosa versicolor virginica
Sepal.Length  5.006   6.055272  6.549251
Sepal.Width   3.428   2.828321  2.932692
Petal.Length  1.462   4.453865  5.534246
Petal.Width   0.246   1.396544  2.064307

Variances:
setosa 
             Sepal.Length Sepal.Width Petal.Length Petal.Width
Sepal.Length   0.15368111  0.13158310  0.021865057 0.013501154
Sepal.Width    0.13158310  0.17948985  0.015459683 0.012186709
Petal.Length   0.02186506  0.01545968  0.029128899 0.006498098
Petal.Width    0.01350115  0.01218671  0.006498098 0.009759900
versicolor 
             Sepal.Length Sepal.Width Petal.Length Petal.Width
Sepal.Length   0.29753053  0.10407744    0.2505462  0.07457733
Sepal.Width    0.10407744  0.10023478    0.1202666  0.05072488
Petal.Length   0.25054623  0.12026661    0.3560530  0.11652817
Petal.Width    0.07457733  0.05072488    0.1165282  0.06076865
virginica 
             Sepal.Length Sepal.Width Petal.Length Petal.Width
Sepal.Length   0.46326081  0.07787790   0.34315068  0.06944725
Sepal.Width    0.07787790  0.08041718   0.05955965  0.05271723
Petal.Length   0.34315068  0.05955965   0.35468725  0.06589333
Petal.Width    0.06944725  0.05271723   0.06589333  0.06694996

Classification summary:
            Predicted
Class        setosa versicolor virginica
  setosa         10          0         0
  versicolor      0         15         0
  virginica       0          0         5
  <NA>           40         45        35

pred_SSC <- predict(mod_SSC)
table(Predicted = pred_SSC$classification, Actual = class, useNA = "ifany")

            Actual
Predicted    setosa versicolor virginica <NA>
  setosa         10          0         0   40
  versicolor      0         15         0   45
  virginica       0          0         5   35

plot(mod_SSC, what = "BIC")

plot(mod_SSC, what = "classification")

plot(mod_SSC, what = "uncertainty")

Examples for 'mclust::plot.MclustSSC'

Plotting method for MclustSSC semi-supervised classification