R Under development (unstable) (2023-12-12 r85669 ucrt) -- "Unsuffered Consequences"
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> library(Rmixmod)
Loading required package: Rcpp
Rmixmod v. 2.1.10 / URI: www.mixmod.org
> data(iris)
> data <- iris[, 1:4]
> labels <- ifelse(iris[5] == "virginica", 3, ifelse(iris[5] == "versicolor", 2, 1))
> rand <- sample(c(rep(1, each = 50), rep(0, each = 100)))
> kl <- labels * rand
> mixmodCluster(data, nbCluster = 3, knownLabels = kl)
****************************************
*** INPUT:
****************************************
* nbCluster =  3 
* criterion =  BIC 
****************************************
*** MIXMOD Models:
* list =  Gaussian_pk_Lk_C 
* This list includes only models with free proportions.
****************************************
* data (limited to a 10x10 matrix) =
      Sepal.Length Sepal.Width Petal.Length Petal.Width
 [1,] 5.1          3.5         1.4          0.2        
 [2,] 4.9          3           1.4          0.2        
 [3,] 4.7          3.2         1.3          0.2        
 [4,] 4.6          3.1         1.5          0.2        
 [5,] 5            3.6         1.4          0.2        
 [6,] 5.4          3.9         1.7          0.4        
 [7,] 4.6          3.4         1.4          0.3        
 [8,] 5            3.4         1.5          0.2        
 [9,] 4.4          2.9         1.4          0.2        
[10,] 4.9          3.1         1.5          0.1        
* ... ...
* knownLabels =  1 1 0 0 1 0 0 0 0 0  ...
****************************************
*** MIXMOD Strategy:
* algorithm            =  EM 
* number of tries      =  1 
* number of iterations =  200 
* epsilon              =  0.001 
*** Initialization strategy:
* algorithm            =  smallEM 
* number of tries      =  10 
* number of iterations =  5 
* epsilon              =  0.001 
* seed                 =  NULL 
****************************************


****************************************
*** BEST MODEL OUTPUT:
*** According to the BIC criterion
****************************************
* nbCluster   =  3 
* model name  =  Gaussian_pk_Lk_C 
* criterion   =  BIC(611.5840)
* likelihood  =  -240.6538 
****************************************
*** Cluster 1 
* proportion =  0.3333 
* means      =  5.0060 3.4280 1.4620 0.2460 
* variances  = |     0.1516     0.0639     0.0851     0.0222 |
               |     0.0639     0.0823     0.0323     0.0188 |
               |     0.0851     0.0323     0.0984     0.0249 |
               |     0.0222     0.0188     0.0249     0.0223 |
*** Cluster 2 
* proportion =  0.3239 
* means      =  5.9277 2.7709 4.2443 1.3184 
* variances  = |     0.2187     0.0922     0.1228     0.0320 |
               |     0.0922     0.1188     0.0466     0.0271 |
               |     0.1228     0.0466     0.1419     0.0359 |
               |     0.0320     0.0271     0.0359     0.0321 |
*** Cluster 3 
* proportion =  0.3428 
* means      =  6.5779 2.9675 5.5312 2.0139 
* variances  = |     0.3904     0.1646     0.2192     0.0572 |
               |     0.1646     0.2121     0.0831     0.0484 |
               |     0.2192     0.0831     0.2533     0.0641 |
               |     0.0572     0.0484     0.0641     0.0574 |
****************************************
> 
> proc.time()
   user  system elapsed 
   0.29    0.06    0.34