mod1:
In SaraTouzani/EM: Algorithme EM ("Espérance-Maximisation")

Usage Format Examples

1	data("mod1")

The format is: List of 15 $ call : language Mclust(data = iris[, 1:4], G = 5) $ data : num [1:150, 1:4] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : chr [1:4] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" $ modelName : chr "EEE" $ n : int 150 $ d : int 4 $ G : int 5 $ BIC : 'mclustBIC' num [1, 1:14] -783 -743 -742 -688 -767 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr "5" .. ..$ : chr [1:14] "EII" "VII" "EEI" "VEI" ... ..- attr(*, "G")= num 5 ..- attr(*, "modelNames")= chr [1:14] "EII" "VII" "EEI" "VEI" ... ..- attr(*, "control")=List of 4 .. ..$ eps : num 2.22e-16 .. ..$ tol : num [1:2] 1.00e-05 1.49e-08 .. ..$ itmax : int [1:2] 2147483647 2147483647 .. ..$ equalPro: logi FALSE ..- attr(*, "initialization")=List of 3 .. ..$ hcPairs: 'hc' num [1:2, 1:149] 102 143 128 139 18 41 1 18 1 28 ... .. .. ..- attr(*, "initialPartition")= num [1:150] 1 2 3 4 5 6 7 8 9 10 ... .. .. .. ..- attr(*, "unique")= int 150 .. .. ..- attr(*, "dimensions")= int [1:2] 150 4 .. .. ..- attr(*, "modelName")= chr "VVV" .. .. ..- attr(*, "call")= language hc(data = data, modelName = mclust.options("hcModelName")[1]) .. .. ..- attr(*, "use")= chr "SVD" .. .. ..- attr(*, "data")= num [1:150, 1:4] -0.494 -0.454 -0.516 -0.502 -0.522 ... .. ..$ subset : NULL .. ..$ noise : NULL ..- attr(*, "warn")= logi FALSE ..- attr(*, "n")= int 150 ..- attr(*, "d")= int 4 ..- attr(*, "oneD")= logi FALSE ..- attr(*, "criterion")= chr "BIC" ..- attr(*, "returnCodes")= num [1, 1:14] 0 0 0 0 0 0 0 0 -1 0 ... .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr "5" .. .. ..$ : chr [1:14] "EII" "VII" "EEI" "VEI" ... $ bic : num -605 $ loglik : num -217 $ df : num 34 $ hypvol : num NA $ parameters :List of 3 ..$ pro : num [1:5] 0.0578 0.2755 0.3279 0.2301 0.1087 ..$ mean : num [1:4, 1:5] 5.41 3.92 1.432 0.256 4.921 ... .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr [1:4] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" .. .. ..$ : NULL ..$ variance:List of 6 .. ..$ modelName: chr "EEE" .. ..$ d : int 4 .. ..$ G : int 5 .. ..$ sigma : num [1:4, 1:4, 1:5] 0.2242 0.0793 0.1406 0.0515 0.0793 ... .. .. ..- attr(*, "dimnames")=List of 3 .. .. .. ..$ : chr [1:4] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" .. .. .. ..$ : chr [1:4] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" .. .. .. ..$ : NULL .. ..$ Sigma : num [1:4, 1:4] 0.2242 0.0793 0.1406 0.0515 0.0793 ... .. .. ..- attr(*, "dimnames")=List of 2 .. .. .. ..$ : chr [1:4] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" .. .. .. ..$ : chr [1:4] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" .. ..$ cholSigma: num [1:4, 1:4] 0.474 0 0 0 0.168 ... .. .. ..- attr(*, "dimnames")=List of 2 .. .. .. ..$ : chr [1:4] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" .. .. .. ..$ : chr [1:4] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" $ z : num [1:150, 1:5] 0.095949 0.001418 0.00498 0.000645 0.13493 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : NULL $ classification: num [1:150] 2 2 2 2 2 2 2 2 2 2 ... $ uncertainty : num [1:150] 0.095949 0.001418 0.00498 0.000645 0.13493 ... - attr(*, "class")= chr "Mclust"