Best model based on BIC

Determines the best model from clustering via mclustBIC for a given set of model parameterizations and numbers of components.

Usage

mclustModel(data, BICvalues, G, modelNames, ...)

Arguments

data: The matrix or vector of observations used to generate `object'.
BICvalues: An 'mclustBIC' object, which is the result of applying mclustBIC to data.
G: A vector of integers giving the numbers of mixture components (clusters) from which the best model according to BIC will be selected (as.character(G) must be a subset of the row names of BICvalues). The default is to select the best model for all numbers of mixture components used to obtain BICvalues.
modelNames: A vector of integers giving the model parameterizations from which the best model according to BIC will be selected (as.character(model) must be a subset of the column names of BICvalues). The default is to select the best model for parameterizations used to obtain BICvalues.
...: Not used. For generic/method consistency.

Value

A list giving the optimal (according to BIC) parameters, conditional probabilities z, and log-likelihood, together with the associated classification and its uncertainty.

The details of the output components are as follows:

modelName

A character string indicating the model. The help file for mclustModelNames describes the available models.

n

The number of observations in the data.

d

The dimension of the data.

G

The number of components in the Gaussian mixture model corresponding to the optimal BIC.

bic

The optimal BIC value.

loglik

The log-likelihood corresponding to the optimal BIC.

parameters

A list with the following components:

pro: A vector whose kth component is the mixing proportion for the kth component of the mixture model. If missing, equal proportions are assumed.
mean: The mean for each component. If there is more than one component, this is a matrix whose kth column is the mean of the kth component of the mixture model.
variance: A list of variance parameters for the model. The components of this list depend on the model specification. See the help file for mclustVariance for details.
Vinv: The estimate of the reciprocal hypervolume of the data region used in the computation when the input indicates the addition of a noise component to the model.

z

A matrix whose [i,k]th entry is the probability that observation i in the test data belongs to the kth class.

Examples

irisBIC <- mclustBIC(iris[,-5])
mclustModel(iris[,-5], irisBIC)
#> $modelName
#> [1] "VEV"
#> 
#> $n
#> [1] 150
#> 
#> $d
#> [1] 4
#> 
#> $G
#> [1] 2
#> 
#> $bic
#> [1] -561.7285
#> 
#> $loglik
#> [1] -215.726
#> 
#> $parameters
#> $parameters$pro
#> [1] 0.3333319 0.6666681
#> 
#> $parameters$mean
#>                   [,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
#> 
#> $parameters$variance
#> $parameters$variance$modelName
#> [1] "VEV"
#> 
#> $parameters$variance$d
#> [1] 4
#> 
#> $parameters$variance$G
#> [1] 2
#> 
#> $parameters$variance$sigma
#> , , 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
#> 
#> 
#> $parameters$variance$scale
#> [1] 0.0377238 0.1330765
#> 
#> $parameters$variance$shape
#> [1] 7.9106959 0.9228733 0.6299550 0.2174371
#> 
#> $parameters$variance$orientation
#> , , 1
#> 
#>              Sepal.Length Sepal.Width Petal.Length Petal.Width
#> Sepal.Length  -0.66908566   0.5978655   -0.4399773 -0.03607204
#> Sepal.Width   -0.73414108  -0.6206713    0.2746297 -0.01955857
#> Petal.Length  -0.09654297   0.4900812    0.8324338 -0.23990403
#> Petal.Width   -0.06356659   0.1309366    0.1950707  0.96992903
#> 
#> , , 2
#> 
#>              Sepal.Length Sepal.Width Petal.Length Petal.Width
#> Sepal.Length    0.5565148  0.75863444 -0.006116263  -0.3387148
#> Sepal.Width     0.1865004  0.02937396 -0.901899502   0.3884997
#> Petal.Length    0.7428959 -0.33350461  0.344100463   0.4674134
#> Petal.Width     0.3218923 -0.55891469 -0.261026344  -0.7182374
#> 
#> 
#> 
#> $parameters$Vinv
#> NULL
#> 
#> 
#> $z
#>                 [,1]         [,2]
#>   [1,]  1.000000e+00 2.513256e-11
#>   [2,]  9.999999e-01 5.556629e-08
#>   [3,]  1.000000e+00 3.635567e-09
#>   [4,]  9.999999e-01 8.612037e-08
#>   [5,]  1.000000e+00 8.504814e-12
#>   [6,]  1.000000e+00 1.400416e-12
#>   [7,]  1.000000e+00 2.971743e-09
#>   [8,]  1.000000e+00 4.053088e-10
#>   [9,]  9.999993e-01 6.585467e-07
#>  [10,]  9.999999e-01 7.276067e-08
#>  [11,]  1.000000e+00 1.189599e-12
#>  [12,]  1.000000e+00 3.285947e-09
#>  [13,]  9.999999e-01 1.034990e-07
#>  [14,]  9.999998e-01 1.791935e-07
#>  [15,]  1.000000e+00 2.623274e-16
#>  [16,]  1.000000e+00 1.665011e-18
#>  [17,]  1.000000e+00 2.857132e-14
#>  [18,]  1.000000e+00 3.517596e-11
#>  [19,]  1.000000e+00 2.111154e-12
#>  [20,]  1.000000e+00 9.845182e-13
#>  [21,]  1.000000e+00 6.254470e-09
#>  [22,]  1.000000e+00 1.579205e-11
#>  [23,]  1.000000e+00 1.159139e-10
#>  [24,]  9.999995e-01 4.953933e-07
#>  [25,]  9.999989e-01 1.061700e-06
#>  [26,]  9.999994e-01 5.954046e-07
#>  [27,]  1.000000e+00 6.015651e-09
#>  [28,]  1.000000e+00 5.309896e-11
#>  [29,]  1.000000e+00 1.021530e-10
#>  [30,]  9.999999e-01 6.263700e-08
#>  [31,]  9.999998e-01 1.604474e-07
#>  [32,]  1.000000e+00 6.134473e-10
#>  [33,]  1.000000e+00 5.534184e-15
#>  [34,]  1.000000e+00 3.165137e-17
#>  [35,]  1.000000e+00 3.683247e-08
#>  [36,]  1.000000e+00 1.058361e-09
#>  [37,]  1.000000e+00 9.055560e-12
#>  [38,]  1.000000e+00 2.144971e-11
#>  [39,]  9.999999e-01 1.445739e-07
#>  [40,]  1.000000e+00 3.176730e-10
#>  [41,]  1.000000e+00 3.224240e-11
#>  [42,]  9.997974e-01 2.025712e-04
#>  [43,]  1.000000e+00 2.424854e-08
#>  [44,]  9.999997e-01 3.077601e-07
#>  [45,]  1.000000e+00 2.213493e-09
#>  [46,]  9.999999e-01 9.248798e-08
#>  [47,]  1.000000e+00 2.393976e-12
#>  [48,]  1.000000e+00 1.123879e-08
#>  [49,]  1.000000e+00 1.458995e-12
#>  [50,]  1.000000e+00 6.608732e-10
#>  [51,]  5.013996e-97 1.000000e+00
#>  [52,]  1.064131e-88 1.000000e+00
#>  [53,] 2.002769e-110 1.000000e+00
#>  [54,]  2.021014e-68 1.000000e+00
#>  [55,]  5.251038e-98 1.000000e+00
#>  [56,]  5.873800e-85 1.000000e+00
#>  [57,] 4.044755e-100 1.000000e+00
#>  [58,]  3.692996e-36 1.000000e+00
#>  [59,]  1.711073e-91 1.000000e+00
#>  [60,]  2.496491e-65 1.000000e+00
#>  [61,]  1.718670e-44 1.000000e+00
#>  [62,]  1.255500e-77 1.000000e+00
#>  [63,]  1.580921e-64 1.000000e+00
#>  [64,]  6.715502e-97 1.000000e+00
#>  [65,]  3.284253e-50 1.000000e+00
#>  [66,]  2.488880e-83 1.000000e+00
#>  [67,]  5.063871e-90 1.000000e+00
#>  [68,]  2.134991e-62 1.000000e+00
#>  [69,]  9.313637e-99 1.000000e+00
#>  [70,]  5.368204e-58 1.000000e+00
#>  [71,] 1.593168e-114 1.000000e+00
#>  [72,]  1.544262e-65 1.000000e+00
#>  [73,] 1.093577e-114 1.000000e+00
#>  [74,]  1.254390e-92 1.000000e+00
#>  [75,]  2.218319e-77 1.000000e+00
#>  [76,]  4.064949e-84 1.000000e+00
#>  [77,] 5.126083e-106 1.000000e+00
#>  [78,] 8.081566e-123 1.000000e+00
#>  [79,]  5.062549e-91 1.000000e+00
#>  [80,]  8.744165e-42 1.000000e+00
#>  [81,]  5.434987e-55 1.000000e+00
#>  [82,]  2.182395e-49 1.000000e+00
#>  [83,]  3.129395e-59 1.000000e+00
#>  [84,] 3.734291e-125 1.000000e+00
#>  [85,]  2.775862e-90 1.000000e+00
#>  [86,]  2.764222e-90 1.000000e+00
#>  [87,] 7.510173e-100 1.000000e+00
#>  [88,]  5.994677e-88 1.000000e+00
#>  [89,]  8.912310e-67 1.000000e+00
#>  [90,]  7.231734e-67 1.000000e+00
#>  [91,]  3.262253e-79 1.000000e+00
#>  [92,]  2.747440e-91 1.000000e+00
#>  [93,]  8.120502e-64 1.000000e+00
#>  [94,]  8.983340e-37 1.000000e+00
#>  [95,]  4.355364e-73 1.000000e+00
#>  [96,]  7.803931e-68 1.000000e+00
#>  [97,]  1.572709e-71 1.000000e+00
#>  [98,]  1.267390e-76 1.000000e+00
#>  [99,]  1.592626e-30 1.000000e+00
#> [100,]  1.810229e-68 1.000000e+00
#> [101,] 2.738152e-219 1.000000e+00
#> [102,] 3.179495e-138 1.000000e+00
#> [103,] 6.128766e-193 1.000000e+00
#> [104,] 2.393731e-159 1.000000e+00
#> [105,] 1.229207e-190 1.000000e+00
#> [106,] 1.049682e-242 1.000000e+00
#> [107,] 4.074631e-103 1.000000e+00
#> [108,] 1.165356e-208 1.000000e+00
#> [109,] 1.375100e-177 1.000000e+00
#> [110,] 1.922118e-221 1.000000e+00
#> [111,] 5.753408e-139 1.000000e+00
#> [112,] 1.661327e-149 1.000000e+00
#> [113,] 2.522136e-168 1.000000e+00
#> [114,] 3.502666e-140 1.000000e+00
#> [115,] 3.351376e-168 1.000000e+00
#> [116,] 6.720655e-167 1.000000e+00
#> [117,] 5.175344e-153 1.000000e+00
#> [118,] 1.008251e-242 1.000000e+00
#> [119,] 7.666712e-282 1.000000e+00
#> [120,] 2.119278e-121 1.000000e+00
#> [121,] 5.006541e-189 1.000000e+00
#> [122,] 2.454880e-133 1.000000e+00
#> [123,] 9.319185e-250 1.000000e+00
#> [124,] 3.324456e-124 1.000000e+00
#> [125,] 4.943602e-176 1.000000e+00
#> [126,] 1.245399e-183 1.000000e+00
#> [127,] 3.204034e-118 1.000000e+00
#> [128,] 6.332105e-121 1.000000e+00
#> [129,] 2.764556e-175 1.000000e+00
#> [130,] 4.206125e-167 1.000000e+00
#> [131,] 2.582052e-201 1.000000e+00
#> [132,] 4.979621e-213 1.000000e+00
#> [133,] 5.636391e-181 1.000000e+00
#> [134,] 1.534458e-121 1.000000e+00
#> [135,] 6.800824e-148 1.000000e+00
#> [136,] 1.586542e-219 1.000000e+00
#> [137,] 4.923324e-188 1.000000e+00
#> [138,] 1.132434e-151 1.000000e+00
#> [139,] 3.529233e-116 1.000000e+00
#> [140,] 7.067477e-162 1.000000e+00
#> [141,] 1.221522e-190 1.000000e+00
#> [142,] 5.085741e-158 1.000000e+00
#> [143,] 3.179495e-138 1.000000e+00
#> [144,] 9.761103e-201 1.000000e+00
#> [145,] 5.525864e-201 1.000000e+00
#> [146,] 7.088920e-164 1.000000e+00
#> [147,] 7.438019e-136 1.000000e+00
#> [148,] 3.252601e-146 1.000000e+00
#> [149,] 1.122150e-170 1.000000e+00
#> [150,] 8.593381e-131 1.000000e+00
#> 
#> attr(,"class")
#> [1] "mclustModel"
mclustModel(iris[,-5], irisBIC, G = 1:6, modelNames = c("VII", "VVI", "VVV"))
#> $modelName
#> [1] "VVV"
#> 
#> $n
#> [1] 150
#> 
#> $d
#> [1] 4
#> 
#> $G
#> [1] 2
#> 
#> $bic
#> [1] -574.0178
#> 
#> $loglik
#> [1] -214.3547
#> 
#> $parameters
#> $parameters$pro
#> [1] 0.3333291 0.6666709
#> 
#> $parameters$mean
#>                   [,1]     [,2]
#> Sepal.Length 5.0060064 6.261989
#> Sepal.Width  3.4280142 2.871996
#> Petal.Length 1.4620020 4.905977
#> Petal.Width  0.2459993 1.675991
#> 
#> $parameters$variance
#> $parameters$variance$modelName
#> [1] "VVV"
#> 
#> $parameters$variance$d
#> [1] 4
#> 
#> $parameters$variance$G
#> [1] 2
#> 
#> $parameters$variance$sigma
#> , , 1
#> 
#>              Sepal.Length Sepal.Width Petal.Length Petal.Width
#> Sepal.Length   0.12176231 0.097226044  0.016027170 0.010124469
#> Sepal.Width    0.09722604 0.140801778  0.011461848 0.009112879
#> Petal.Length   0.01602717 0.011461848  0.029556040 0.005948185
#> Petal.Width    0.01012447 0.009112879  0.005948185 0.010884100
#> 
#> , , 2
#> 
#>              Sepal.Length Sepal.Width Petal.Length Petal.Width
#> Sepal.Length    0.4349729  0.12094155    0.4488654  0.16550230
#> Sepal.Width     0.1209416  0.10961737    0.1413800  0.07923243
#> Petal.Length    0.4488654  0.14138003    0.6748420  0.28587362
#> Petal.Width     0.1655023  0.07923243    0.2858736  0.17863487
#> 
#> 
#> $parameters$variance$cholsigma
#> , , 1
#> 
#>              Sepal.Length Sepal.Width Petal.Length  Petal.Width
#> Sepal.Length   -0.3489446  -0.2786289  -0.04593042 -0.029014550
#> Sepal.Width     0.0000000   0.2513319  -0.00531447  0.004092543
#> Petal.Length    0.0000000   0.0000000  -0.16558440 -0.028005560
#> Petal.Width     0.0000000   0.0000000   0.00000000  0.096131137
#> 
#> , , 2
#> 
#>              Sepal.Length Sepal.Width Petal.Length Petal.Width
#> Sepal.Length    0.6595247   0.1833768   0.68058911   0.2509418
#> Sepal.Width     0.0000000   0.2756634   0.06013044   0.1204931
#> Petal.Length    0.0000000   0.0000000  -0.45609737  -0.2364409
#> Petal.Width     0.0000000   0.0000000   0.00000000  -0.2126975
#> 
#> 
#> 
#> $parameters$Vinv
#> NULL
#> 
#> 
#> $z
#>                 [,1]         [,2]
#>   [1,]  1.000000e+00 2.179894e-11
#>   [2,]  9.999999e-01 7.812157e-08
#>   [3,]  1.000000e+00 4.901731e-09
#>   [4,]  9.999999e-01 1.329205e-07
#>   [5,]  1.000000e+00 6.909601e-12
#>   [6,]  1.000000e+00 8.337540e-13
#>   [7,]  1.000000e+00 3.054481e-09
#>   [8,]  1.000000e+00 3.980511e-10
#>   [9,]  9.999985e-01 1.512753e-06
#>  [10,]  9.999999e-01 9.028445e-08
#>  [11,]  1.000000e+00 8.462320e-13
#>  [12,]  1.000000e+00 3.143054e-09
#>  [13,]  9.999998e-01 1.533242e-07
#>  [14,]  9.999996e-01 3.856414e-07
#>  [15,]  1.000000e+00 1.071648e-16
#>  [16,]  1.000000e+00 7.735086e-19
#>  [17,]  1.000000e+00 1.400778e-14
#>  [18,]  1.000000e+00 2.966321e-11
#>  [19,]  1.000000e+00 1.468305e-12
#>  [20,]  1.000000e+00 6.429245e-13
#>  [21,]  1.000000e+00 5.071850e-09
#>  [22,]  1.000000e+00 1.039716e-11
#>  [23,]  1.000000e+00 8.130753e-11
#>  [24,]  9.999996e-01 3.782611e-07
#>  [25,]  9.999993e-01 6.606676e-07
#>  [26,]  9.999992e-01 7.672190e-07
#>  [27,]  1.000000e+00 5.214441e-09
#>  [28,]  1.000000e+00 4.525101e-11
#>  [29,]  1.000000e+00 8.997348e-11
#>  [30,]  9.999999e-01 7.741922e-08
#>  [31,]  9.999998e-01 2.151977e-07
#>  [32,]  1.000000e+00 4.496760e-10
#>  [33,]  1.000000e+00 2.358894e-15
#>  [34,]  1.000000e+00 1.540121e-17
#>  [35,]  1.000000e+00 4.874255e-08
#>  [36,]  1.000000e+00 1.041329e-09
#>  [37,]  1.000000e+00 5.611454e-12
#>  [38,]  1.000000e+00 1.650417e-11
#>  [39,]  9.999997e-01 2.967315e-07
#>  [40,]  1.000000e+00 3.015350e-10
#>  [41,]  1.000000e+00 2.652560e-11
#>  [42,]  9.993722e-01 6.278412e-04
#>  [43,]  1.000000e+00 3.744704e-08
#>  [44,]  9.999998e-01 1.536195e-07
#>  [45,]  1.000000e+00 1.009277e-09
#>  [46,]  9.999999e-01 1.346885e-07
#>  [47,]  1.000000e+00 1.491418e-12
#>  [48,]  1.000000e+00 1.593775e-08
#>  [49,]  1.000000e+00 1.055513e-12
#>  [50,]  1.000000e+00 7.010884e-10
#>  [51,]  1.028184e-91 1.000000e+00
#>  [52,]  4.118505e-83 1.000000e+00
#>  [53,] 4.254815e-104 1.000000e+00
#>  [54,]  5.533854e-64 1.000000e+00
#>  [55,]  3.727474e-92 1.000000e+00
#>  [56,]  5.295789e-79 1.000000e+00
#>  [57,]  1.821162e-93 1.000000e+00
#>  [58,]  9.420780e-34 1.000000e+00
#>  [59,]  3.558122e-86 1.000000e+00
#>  [60,]  1.460888e-60 1.000000e+00
#>  [61,]  8.911083e-42 1.000000e+00
#>  [62,]  2.262077e-72 1.000000e+00
#>  [63,]  7.500913e-61 1.000000e+00
#>  [64,]  1.711672e-90 1.000000e+00
#>  [65,]  9.052760e-47 1.000000e+00
#>  [66,]  1.382354e-78 1.000000e+00
#>  [67,]  2.067665e-83 1.000000e+00
#>  [68,]  2.407041e-58 1.000000e+00
#>  [69,]  8.428344e-93 1.000000e+00
#>  [70,]  2.777254e-54 1.000000e+00
#>  [71,] 2.860975e-106 1.000000e+00
#>  [72,]  1.594952e-61 1.000000e+00
#>  [73,] 1.521401e-107 1.000000e+00
#>  [74,]  1.474621e-86 1.000000e+00
#>  [75,]  8.345281e-73 1.000000e+00
#>  [76,]  2.992676e-79 1.000000e+00
#>  [77,] 5.093954e-100 1.000000e+00
#>  [78,] 2.339534e-115 1.000000e+00
#>  [79,]  5.839041e-85 1.000000e+00
#>  [80,]  2.920437e-39 1.000000e+00
#>  [81,]  1.690054e-51 1.000000e+00
#>  [82,]  2.604584e-46 1.000000e+00
#>  [83,]  1.837805e-55 1.000000e+00
#>  [84,] 1.328964e-116 1.000000e+00
#>  [85,]  2.228873e-83 1.000000e+00
#>  [86,]  7.605550e-84 1.000000e+00
#>  [87,]  5.785348e-94 1.000000e+00
#>  [88,]  7.563474e-83 1.000000e+00
#>  [89,]  5.492330e-62 1.000000e+00
#>  [90,]  2.101525e-62 1.000000e+00
#>  [91,]  1.260017e-73 1.000000e+00
#>  [92,]  3.129421e-85 1.000000e+00
#>  [93,]  8.807567e-60 1.000000e+00
#>  [94,]  1.915922e-34 1.000000e+00
#>  [95,]  4.525684e-68 1.000000e+00
#>  [96,]  4.922630e-63 1.000000e+00
#>  [97,]  1.435982e-66 1.000000e+00
#>  [98,]  7.246796e-72 1.000000e+00
#>  [99,]  1.570401e-28 1.000000e+00
#> [100,]  7.753220e-64 1.000000e+00
#> [101,] 1.715600e-203 1.000000e+00
#> [102,] 2.063080e-128 1.000000e+00
#> [103,] 4.144542e-181 1.000000e+00
#> [104,] 1.802435e-148 1.000000e+00
#> [105,] 9.595656e-178 1.000000e+00
#> [106,] 2.450871e-228 1.000000e+00
#> [107,]  3.858134e-95 1.000000e+00
#> [108,] 4.501334e-196 1.000000e+00
#> [109,] 2.115792e-166 1.000000e+00
#> [110,] 2.850242e-207 1.000000e+00
#> [111,] 7.896941e-130 1.000000e+00
#> [112,] 8.635475e-140 1.000000e+00
#> [113,] 1.065583e-157 1.000000e+00
#> [114,] 2.995927e-130 1.000000e+00
#> [115,] 6.014984e-156 1.000000e+00
#> [116,] 1.855013e-155 1.000000e+00
#> [117,] 6.112142e-143 1.000000e+00
#> [118,] 5.557076e-228 1.000000e+00
#> [119,] 4.071437e-265 1.000000e+00
#> [120,] 1.582251e-113 1.000000e+00
#> [121,] 6.551920e-177 1.000000e+00
#> [122,] 1.497616e-123 1.000000e+00
#> [123,] 3.260843e-235 1.000000e+00
#> [124,] 3.221091e-116 1.000000e+00
#> [125,] 2.126742e-164 1.000000e+00
#> [126,] 1.750926e-172 1.000000e+00
#> [127,] 1.827616e-110 1.000000e+00
#> [128,] 1.157196e-112 1.000000e+00
#> [129,] 1.541398e-163 1.000000e+00
#> [130,] 2.818306e-157 1.000000e+00
#> [131,] 1.280706e-189 1.000000e+00
#> [132,] 5.515772e-201 1.000000e+00
#> [133,] 8.508603e-169 1.000000e+00
#> [134,] 1.229485e-113 1.000000e+00
#> [135,] 1.419650e-137 1.000000e+00
#> [136,] 4.920802e-207 1.000000e+00
#> [137,] 1.319075e-174 1.000000e+00
#> [138,] 2.047411e-141 1.000000e+00
#> [139,] 3.924939e-108 1.000000e+00
#> [140,] 7.842830e-152 1.000000e+00
#> [141,] 4.784736e-178 1.000000e+00
#> [142,] 4.379310e-148 1.000000e+00
#> [143,] 2.063080e-128 1.000000e+00
#> [144,] 1.527640e-187 1.000000e+00
#> [145,] 1.865711e-187 1.000000e+00
#> [146,] 2.781164e-153 1.000000e+00
#> [147,] 4.075976e-127 1.000000e+00
#> [148,] 1.085405e-136 1.000000e+00
#> [149,] 2.104379e-158 1.000000e+00
#> [150,] 2.032250e-121 1.000000e+00
#> 
#> attr(,"class")
#> [1] "mclustModel"

Usage

Arguments

Value

See also

Examples