CRAN Package Check Results for Package MixAll

Last updated on 2019-12-13 07:52:30 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.5.1 648.26 95.82 744.08 OK
r-devel-linux-x86_64-debian-gcc 1.5.1 479.33 77.31 556.64 OK
r-devel-linux-x86_64-fedora-clang 1.5.1 1093.55 NOTE
r-devel-linux-x86_64-fedora-gcc 1.5.1 1110.78 OK
r-devel-windows-ix86+x86_64 1.5.1 1333.00 169.00 1502.00 ERROR
r-devel-windows-ix86+x86_64-gcc8 1.5.1 1353.00 240.00 1593.00 NOTE
r-patched-linux-x86_64 1.5.1 559.95 89.76 649.71 OK
r-patched-solaris-x86 1.5.1 888.00 OK
r-release-linux-x86_64 1.5.1 553.62 87.88 641.50 OK
r-release-windows-ix86+x86_64 1.5.1 1341.00 169.00 1510.00 NOTE
r-release-osx-x86_64 1.5.1 NOTE
r-oldrel-windows-ix86+x86_64 1.5.1 1312.00 174.00 1486.00 ERROR
r-oldrel-osx-x86_64 1.5.1 NOTE

Check Details

Version: 1.5.1
Check: installed package size
Result: NOTE
     installed size is 40.1Mb
     sub-directories of 1Mb or more:
     libs 37.8Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-windows-ix86+x86_64, r-devel-windows-ix86+x86_64-gcc8, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64

Version: 1.5.1
Check: running tests for arch ‘i386’
Result: ERROR
     Running 'ClusterSimul.R' [0s]
     Running 'clusterDiagGaussianLikelihood.R' [1s]
     Running 'clusterGammaLikelihood.R' [1s]
     Running 'simulHeterogeneous.R' [0s]
     Running 'simulNonLinear.R' [0s]
     Running 'testAllLearners.R' [1s]
     Running 'testPoissonExample.R' [1s]
     Running 'testPredict.R' [8s]
    Running the tests in 'tests/testAllLearners.R' failed.
    Complete output:
     > library(MixAll)
     Loading required package: rtkore
     Loading required package: Rcpp
    
     Attaching package: 'rtkore'
    
     The following object is masked from 'package:Rcpp':
    
     LdFlags
    
     > ## get data and target from iris data set
     > data(iris)
     > x <- as.matrix(iris[,1:4]); z <- as.vector(iris[,5]); n <- nrow(x); p <- ncol(x)
     > ## add missing values at random
     > indexes <- matrix(c(round(runif(5,1,n)), round(runif(5,1,p))), ncol=2)
     > cbind(indexes, x[indexes])
     [,1] [,2] [,3]
     [1,] 12 4 0.2
     [2,] 47 4 0.2
     [3,] 72 2 2.8
     [4,] 47 3 1.6
     [5,] 137 4 2.4
     > x[indexes] <- NA
     > ## learn continuous model
     > model <- learnDiagGaussian( data=x, labels= z, prop = c(1/3,1/3,1/3)
     + , models = clusterDiagGaussianNames(prop = "equal")
     + , algo = "simul", nbIter = 2, epsilon = 1e-08
     + )
     > missingValues(model)
     row col value
     1 72 2 2.73799795
     2 47 3 1.36603653
     3 12 4 -0.07200802
     4 47 4 0.27170501
     5 137 4 1.75461411
     > print(model)
     ****************************************
     * model name = gaussian_p_sk
     * data =
     Sepal.Length Sepal.Width Petal.Length Petal.Width
     [1,] 5.10000000 3.50000000 1.40000000 0.20000000
     [2,] 4.90000000 3.00000000 1.40000000 0.20000000
     [3,] 4.70000000 3.20000000 1.30000000 0.20000000
     [4,] 4.60000000 3.10000000 1.50000000 0.20000000
     [5,] 5.00000000 3.60000000 1.40000000 0.20000000
     [6,] 5.40000000 3.90000000 1.70000000 0.40000000
     [7,] 4.60000000 3.40000000 1.40000000 0.30000000
     [8,] 5.00000000 3.40000000 1.50000000 0.20000000
     [9,] 4.40000000 2.90000000 1.40000000 0.20000000
     [10,] 4.90000000 3.10000000 1.50000000 0.10000000
     [11,] 5.40000000 3.70000000 1.50000000 0.20000000
     [12,] 4.80000000 3.40000000 1.60000000 -0.07200802
     [13,] 4.80000000 3.00000000 1.40000000 0.10000000
     [14,] 4.30000000 3.00000000 1.10000000 0.10000000
     [15,] 5.80000000 4.00000000 1.20000000 0.20000000
     [16,] 5.70000000 4.40000000 1.50000000 0.40000000
     [17,] 5.40000000 3.90000000 1.30000000 0.40000000
     [18,] 5.10000000 3.50000000 1.40000000 0.30000000
     [19,] 5.70000000 3.80000000 1.70000000 0.30000000
     [20,] 5.10000000 3.80000000 1.50000000 0.30000000
     [21,] 5.40000000 3.40000000 1.70000000 0.20000000
     [22,] 5.10000000 3.70000000 1.50000000 0.40000000
     [23,] 4.60000000 3.60000000 1.00000000 0.20000000
     [24,] 5.10000000 3.30000000 1.70000000 0.50000000
     [25,] 4.80000000 3.40000000 1.90000000 0.20000000
     [26,] 5.00000000 3.00000000 1.60000000 0.20000000
     [27,] 5.00000000 3.40000000 1.60000000 0.40000000
     [28,] 5.20000000 3.50000000 1.50000000 0.20000000
     [29,] 5.20000000 3.40000000 1.40000000 0.20000000
     [30,] 4.70000000 3.20000000 1.60000000 0.20000000
     [31,] 4.80000000 3.10000000 1.60000000 0.20000000
     [32,] 5.40000000 3.40000000 1.50000000 0.40000000
     [33,] 5.20000000 4.10000000 1.50000000 0.10000000
     [34,] 5.50000000 4.20000000 1.40000000 0.20000000
     [35,] 4.90000000 3.10000000 1.50000000 0.20000000
     [36,] 5.00000000 3.20000000 1.20000000 0.20000000
     [37,] 5.50000000 3.50000000 1.30000000 0.20000000
     [38,] 4.90000000 3.60000000 1.40000000 0.10000000
     [39,] 4.40000000 3.00000000 1.30000000 0.20000000
     [40,] 5.10000000 3.40000000 1.50000000 0.20000000
     [41,] 5.00000000 3.50000000 1.30000000 0.30000000
     [42,] 4.50000000 2.30000000 1.30000000 0.30000000
     [43,] 4.40000000 3.20000000 1.30000000 0.20000000
     [44,] 5.00000000 3.50000000 1.60000000 0.60000000
     [45,] 5.10000000 3.80000000 1.90000000 0.40000000
     [46,] 4.80000000 3.00000000 1.40000000 0.30000000
     [47,] 5.10000000 3.80000000 1.36603653 0.27170501
     [48,] 4.60000000 3.20000000 1.40000000 0.20000000
     [49,] 5.30000000 3.70000000 1.50000000 0.20000000
     [50,] 5.00000000 3.30000000 1.40000000 0.20000000
     [51,] 7.00000000 3.20000000 4.70000000 1.40000000
     [52,] 6.40000000 3.20000000 4.50000000 1.50000000
     [53,] 6.90000000 3.10000000 4.90000000 1.50000000
     [54,] 5.50000000 2.30000000 4.00000000 1.30000000
     [55,] 6.50000000 2.80000000 4.60000000 1.50000000
     [56,] 5.70000000 2.80000000 4.50000000 1.30000000
     [57,] 6.30000000 3.30000000 4.70000000 1.60000000
     [58,] 4.90000000 2.40000000 3.30000000 1.00000000
     [59,] 6.60000000 2.90000000 4.60000000 1.30000000
     [60,] 5.20000000 2.70000000 3.90000000 1.40000000
     [61,] 5.00000000 2.00000000 3.50000000 1.00000000
     [62,] 5.90000000 3.00000000 4.20000000 1.50000000
     [63,] 6.00000000 2.20000000 4.00000000 1.00000000
     [64,] 6.10000000 2.90000000 4.70000000 1.40000000
     [65,] 5.60000000 2.90000000 3.60000000 1.30000000
     [66,] 6.70000000 3.10000000 4.40000000 1.40000000
     [67,] 5.60000000 3.00000000 4.50000000 1.50000000
     [68,] 5.80000000 2.70000000 4.10000000 1.00000000
     [69,] 6.20000000 2.20000000 4.50000000 1.50000000
     [70,] 5.60000000 2.50000000 3.90000000 1.10000000
     [71,] 5.90000000 3.20000000 4.80000000 1.80000000
     [72,] 6.10000000 2.73799795 4.00000000 1.30000000
     [73,] 6.30000000 2.50000000 4.90000000 1.50000000
     [74,] 6.10000000 2.80000000 4.70000000 1.20000000
     [75,] 6.40000000 2.90000000 4.30000000 1.30000000
     [76,] 6.60000000 3.00000000 4.40000000 1.40000000
     [77,] 6.80000000 2.80000000 4.80000000 1.40000000
     [78,] 6.70000000 3.00000000 5.00000000 1.70000000
     [79,] 6.00000000 2.90000000 4.50000000 1.50000000
     [80,] 5.70000000 2.60000000 3.50000000 1.00000000
     [81,] 5.50000000 2.40000000 3.80000000 1.10000000
     [82,] 5.50000000 2.40000000 3.70000000 1.00000000
     [83,] 5.80000000 2.70000000 3.90000000 1.20000000
     [84,] 6.00000000 2.70000000 5.10000000 1.60000000
     [85,] 5.40000000 3.00000000 4.50000000 1.50000000
     [86,] 6.00000000 3.40000000 4.50000000 1.60000000
     [87,] 6.70000000 3.10000000 4.70000000 1.50000000
     [88,] 6.30000000 2.30000000 4.40000000 1.30000000
     [89,] 5.60000000 3.00000000 4.10000000 1.30000000
     [90,] 5.50000000 2.50000000 4.00000000 1.30000000
     [91,] 5.50000000 2.60000000 4.40000000 1.20000000
     [92,] 6.10000000 3.00000000 4.60000000 1.40000000
     [93,] 5.80000000 2.60000000 4.00000000 1.20000000
     [94,] 5.00000000 2.30000000 3.30000000 1.00000000
     [95,] 5.60000000 2.70000000 4.20000000 1.30000000
     [96,] 5.70000000 3.00000000 4.20000000 1.20000000
     [97,] 5.70000000 2.90000000 4.20000000 1.30000000
     [98,] 6.20000000 2.90000000 4.30000000 1.30000000
     [99,] 5.10000000 2.50000000 3.00000000 1.10000000
     [100,] 5.70000000 2.80000000 4.10000000 1.30000000
     [101,] 6.30000000 3.30000000 6.00000000 2.50000000
     [102,] 5.80000000 2.70000000 5.10000000 1.90000000
     [103,] 7.10000000 3.00000000 5.90000000 2.10000000
     [104,] 6.30000000 2.90000000 5.60000000 1.80000000
     [105,] 6.50000000 3.00000000 5.80000000 2.20000000
     [106,] 7.60000000 3.00000000 6.60000000 2.10000000
     [107,] 4.90000000 2.50000000 4.50000000 1.70000000
     [108,] 7.30000000 2.90000000 6.30000000 1.80000000
     [109,] 6.70000000 2.50000000 5.80000000 1.80000000
     [110,] 7.20000000 3.60000000 6.10000000 2.50000000
     [111,] 6.50000000 3.20000000 5.10000000 2.00000000
     [112,] 6.40000000 2.70000000 5.30000000 1.90000000
     [113,] 6.80000000 3.00000000 5.50000000 2.10000000
     [114,] 5.70000000 2.50000000 5.00000000 2.00000000
     [115,] 5.80000000 2.80000000 5.10000000 2.40000000
     [116,] 6.40000000 3.20000000 5.30000000 2.30000000
     [117,] 6.50000000 3.00000000 5.50000000 1.80000000
     [118,] 7.70000000 3.80000000 6.70000000 2.20000000
     [119,] 7.70000000 2.60000000 6.90000000 2.30000000
     [120,] 6.00000000 2.20000000 5.00000000 1.50000000
     [121,] 6.90000000 3.20000000 5.70000000 2.30000000
     [122,] 5.60000000 2.80000000 4.90000000 2.00000000
     [123,] 7.70000000 2.80000000 6.70000000 2.00000000
     [124,] 6.30000000 2.70000000 4.90000000 1.80000000
     [125,] 6.70000000 3.30000000 5.70000000 2.10000000
     [126,] 7.20000000 3.20000000 6.00000000 1.80000000
     [127,] 6.20000000 2.80000000 4.80000000 1.80000000
     [128,] 6.10000000 3.00000000 4.90000000 1.80000000
     [129,] 6.40000000 2.80000000 5.60000000 2.10000000
     [130,] 7.20000000 3.00000000 5.80000000 1.60000000
     [131,] 7.40000000 2.80000000 6.10000000 1.90000000
     [132,] 7.90000000 3.80000000 6.40000000 2.00000000
     [133,] 6.40000000 2.80000000 5.60000000 2.20000000
     [134,] 6.30000000 2.80000000 5.10000000 1.50000000
     [135,] 6.10000000 2.60000000 5.60000000 1.40000000
     [136,] 7.70000000 3.00000000 6.10000000 2.30000000
     [137,] 6.30000000 3.40000000 5.60000000 1.75461411
     [138,] 6.40000000 3.10000000 5.50000000 1.80000000
     [139,] 6.00000000 3.00000000 4.80000000 1.80000000
     [140,] 6.90000000 3.10000000 5.40000000 2.10000000
     [141,] 6.70000000 3.10000000 5.60000000 2.40000000
     [142,] 6.90000000 3.10000000 5.10000000 2.30000000
     [143,] 5.80000000 2.70000000 5.10000000 1.90000000
     [144,] 6.80000000 3.20000000 5.90000000 2.30000000
     [145,] 6.70000000 3.30000000 5.70000000 2.50000000
     [146,] 6.70000000 3.00000000 5.20000000 2.30000000
     [147,] 6.30000000 2.50000000 5.00000000 1.90000000
     [148,] 6.50000000 3.00000000 5.20000000 2.00000000
     [149,] 6.20000000 3.40000000 5.40000000 2.30000000
     [150,] 5.90000000 3.00000000 5.10000000 1.80000000
     * missing =
     row col
     [1,] 72 2
     [2,] 47 3
     [3,] 12 4
     [4,] 47 4
     [5,] 137 4
     * nbSample = 150
     * nbCluster = 3
     * lnLikelihood = -1031.707
     * nbFreeParameter= 70
     * criterion name = ICL
     * criterion value= 2421.424
     * zi =
     [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
     [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
     [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
     [112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
     [149] 2 2
     ****************************************
     *** Cluster: 1
     * Proportion = 0.3333333
     * Means = 5.0060000 3.4280000 1.4573207 0.2419939
     * S.D. = 0.2760147 0.2760147 0.2760147 0.2760147
     ****************************************
     *** Cluster: 2
     * Proportion = 0.3333333
     * Means = 5.93600 2.76876 4.26000 1.32600
     * S.D. = 0.3912573 0.3912573 0.3912573 0.3912573
     ****************************************
     *** Cluster: 3
     * Proportion = 0.3333333
     * Means = 6.588000 2.974000 5.552000 2.013092
     * S.D. = 0.46613 0.46613 0.46613 0.46613
     ****************************************
     > model <- learnDiagGaussian( data=x, labels= z,
     + , models = clusterDiagGaussianNames(prop = "equal")
     + , algo = "impute", nbIter = 2, epsilon = 1e-08)
     > missingValues(model)
     row col value
     > print(model)
     ****************************************
     * model name = gaussian_p_sjk
     * data =
     Sepal.Length Sepal.Width Petal.Length Petal.Width
     [1,] 5.10000000 3.50000000 1.40000000 0.20000000
     [2,] 4.90000000 3.00000000 1.40000000 0.20000000
     [3,] 4.70000000 3.20000000 1.30000000 0.20000000
     [4,] 4.60000000 3.10000000 1.50000000 0.20000000
     [5,] 5.00000000 3.60000000 1.40000000 0.20000000
     [6,] 5.40000000 3.90000000 1.70000000 0.40000000
     [7,] 4.60000000 3.40000000 1.40000000 0.30000000
     [8,] 5.00000000 3.40000000 1.50000000 0.20000000
     [9,] 4.40000000 2.90000000 1.40000000 0.20000000
     [10,] 4.90000000 3.10000000 1.50000000 0.10000000
     [11,] 5.40000000 3.70000000 1.50000000 0.20000000
     [12,] 4.80000000 3.40000000 1.60000000 -0.07200802
     [13,] 4.80000000 3.00000000 1.40000000 0.10000000
     [14,] 4.30000000 3.00000000 1.10000000 0.10000000
     [15,] 5.80000000 4.00000000 1.20000000 0.20000000
     [16,] 5.70000000 4.40000000 1.50000000 0.40000000
     [17,] 5.40000000 3.90000000 1.30000000 0.40000000
     [18,] 5.10000000 3.50000000 1.40000000 0.30000000
     [19,] 5.70000000 3.80000000 1.70000000 0.30000000
     [20,] 5.10000000 3.80000000 1.50000000 0.30000000
     [21,] 5.40000000 3.40000000 1.70000000 0.20000000
     [22,] 5.10000000 3.70000000 1.50000000 0.40000000
     [23,] 4.60000000 3.60000000 1.00000000 0.20000000
     [24,] 5.10000000 3.30000000 1.70000000 0.50000000
     [25,] 4.80000000 3.40000000 1.90000000 0.20000000
     [26,] 5.00000000 3.00000000 1.60000000 0.20000000
     [27,] 5.00000000 3.40000000 1.60000000 0.40000000
     [28,] 5.20000000 3.50000000 1.50000000 0.20000000
     [29,] 5.20000000 3.40000000 1.40000000 0.20000000
     [30,] 4.70000000 3.20000000 1.60000000 0.20000000
     [31,] 4.80000000 3.10000000 1.60000000 0.20000000
     [32,] 5.40000000 3.40000000 1.50000000 0.40000000
     [33,] 5.20000000 4.10000000 1.50000000 0.10000000
     [34,] 5.50000000 4.20000000 1.40000000 0.20000000
     [35,] 4.90000000 3.10000000 1.50000000 0.20000000
     [36,] 5.00000000 3.20000000 1.20000000 0.20000000
     [37,] 5.50000000 3.50000000 1.30000000 0.20000000
     [38,] 4.90000000 3.60000000 1.40000000 0.10000000
     [39,] 4.40000000 3.00000000 1.30000000 0.20000000
     [40,] 5.10000000 3.40000000 1.50000000 0.20000000
     [41,] 5.00000000 3.50000000 1.30000000 0.30000000
     [42,] 4.50000000 2.30000000 1.30000000 0.30000000
     [43,] 4.40000000 3.20000000 1.30000000 0.20000000
     [44,] 5.00000000 3.50000000 1.60000000 0.60000000
     [45,] 5.10000000 3.80000000 1.90000000 0.40000000
     [46,] 4.80000000 3.00000000 1.40000000 0.30000000
     [47,] 5.10000000 3.80000000 1.36603653 0.27170501
     [48,] 4.60000000 3.20000000 1.40000000 0.20000000
     [49,] 5.30000000 3.70000000 1.50000000 0.20000000
     [50,] 5.00000000 3.30000000 1.40000000 0.20000000
     [51,] 7.00000000 3.20000000 4.70000000 1.40000000
     [52,] 6.40000000 3.20000000 4.50000000 1.50000000
     [53,] 6.90000000 3.10000000 4.90000000 1.50000000
     [54,] 5.50000000 2.30000000 4.00000000 1.30000000
     [55,] 6.50000000 2.80000000 4.60000000 1.50000000
     [56,] 5.70000000 2.80000000 4.50000000 1.30000000
     [57,] 6.30000000 3.30000000 4.70000000 1.60000000
     [58,] 4.90000000 2.40000000 3.30000000 1.00000000
     [59,] 6.60000000 2.90000000 4.60000000 1.30000000
     [60,] 5.20000000 2.70000000 3.90000000 1.40000000
     [61,] 5.00000000 2.00000000 3.50000000 1.00000000
     [62,] 5.90000000 3.00000000 4.20000000 1.50000000
     [63,] 6.00000000 2.20000000 4.00000000 1.00000000
     [64,] 6.10000000 2.90000000 4.70000000 1.40000000
     [65,] 5.60000000 2.90000000 3.60000000 1.30000000
     [66,] 6.70000000 3.10000000 4.40000000 1.40000000
     [67,] 5.60000000 3.00000000 4.50000000 1.50000000
     [68,] 5.80000000 2.70000000 4.10000000 1.00000000
     [69,] 6.20000000 2.20000000 4.50000000 1.50000000
     [70,] 5.60000000 2.50000000 3.90000000 1.10000000
     [71,] 5.90000000 3.20000000 4.80000000 1.80000000
     [72,] 6.10000000 2.73799795 4.00000000 1.30000000
     [73,] 6.30000000 2.50000000 4.90000000 1.50000000
     [74,] 6.10000000 2.80000000 4.70000000 1.20000000
     [75,] 6.40000000 2.90000000 4.30000000 1.30000000
     [76,] 6.60000000 3.00000000 4.40000000 1.40000000
     [77,] 6.80000000 2.80000000 4.80000000 1.40000000
     [78,] 6.70000000 3.00000000 5.00000000 1.70000000
     [79,] 6.00000000 2.90000000 4.50000000 1.50000000
     [80,] 5.70000000 2.60000000 3.50000000 1.00000000
     [81,] 5.50000000 2.40000000 3.80000000 1.10000000
     [82,] 5.50000000 2.40000000 3.70000000 1.00000000
     [83,] 5.80000000 2.70000000 3.90000000 1.20000000
     [84,] 6.00000000 2.70000000 5.10000000 1.60000000
     [85,] 5.40000000 3.00000000 4.50000000 1.50000000
     [86,] 6.00000000 3.40000000 4.50000000 1.60000000
     [87,] 6.70000000 3.10000000 4.70000000 1.50000000
     [88,] 6.30000000 2.30000000 4.40000000 1.30000000
     [89,] 5.60000000 3.00000000 4.10000000 1.30000000
     [90,] 5.50000000 2.50000000 4.00000000 1.30000000
     [91,] 5.50000000 2.60000000 4.40000000 1.20000000
     [92,] 6.10000000 3.00000000 4.60000000 1.40000000
     [93,] 5.80000000 2.60000000 4.00000000 1.20000000
     [94,] 5.00000000 2.30000000 3.30000000 1.00000000
     [95,] 5.60000000 2.70000000 4.20000000 1.30000000
     [96,] 5.70000000 3.00000000 4.20000000 1.20000000
     [97,] 5.70000000 2.90000000 4.20000000 1.30000000
     [98,] 6.20000000 2.90000000 4.30000000 1.30000000
     [99,] 5.10000000 2.50000000 3.00000000 1.10000000
     [100,] 5.70000000 2.80000000 4.10000000 1.30000000
     [101,] 6.30000000 3.30000000 6.00000000 2.50000000
     [102,] 5.80000000 2.70000000 5.10000000 1.90000000
     [103,] 7.10000000 3.00000000 5.90000000 2.10000000
     [104,] 6.30000000 2.90000000 5.60000000 1.80000000
     [105,] 6.50000000 3.00000000 5.80000000 2.20000000
     [106,] 7.60000000 3.00000000 6.60000000 2.10000000
     [107,] 4.90000000 2.50000000 4.50000000 1.70000000
     [108,] 7.30000000 2.90000000 6.30000000 1.80000000
     [109,] 6.70000000 2.50000000 5.80000000 1.80000000
     [110,] 7.20000000 3.60000000 6.10000000 2.50000000
     [111,] 6.50000000 3.20000000 5.10000000 2.00000000
     [112,] 6.40000000 2.70000000 5.30000000 1.90000000
     [113,] 6.80000000 3.00000000 5.50000000 2.10000000
     [114,] 5.70000000 2.50000000 5.00000000 2.00000000
     [115,] 5.80000000 2.80000000 5.10000000 2.40000000
     [116,] 6.40000000 3.20000000 5.30000000 2.30000000
     [117,] 6.50000000 3.00000000 5.50000000 1.80000000
     [118,] 7.70000000 3.80000000 6.70000000 2.20000000
     [119,] 7.70000000 2.60000000 6.90000000 2.30000000
     [120,] 6.00000000 2.20000000 5.00000000 1.50000000
     [121,] 6.90000000 3.20000000 5.70000000 2.30000000
     [122,] 5.60000000 2.80000000 4.90000000 2.00000000
     [123,] 7.70000000 2.80000000 6.70000000 2.00000000
     [124,] 6.30000000 2.70000000 4.90000000 1.80000000
     [125,] 6.70000000 3.30000000 5.70000000 2.10000000
     [126,] 7.20000000 3.20000000 6.00000000 1.80000000
     [127,] 6.20000000 2.80000000 4.80000000 1.80000000
     [128,] 6.10000000 3.00000000 4.90000000 1.80000000
     [129,] 6.40000000 2.80000000 5.60000000 2.10000000
     [130,] 7.20000000 3.00000000 5.80000000 1.60000000
     [131,] 7.40000000 2.80000000 6.10000000 1.90000000
     [132,] 7.90000000 3.80000000 6.40000000 2.00000000
     [133,] 6.40000000 2.80000000 5.60000000 2.20000000
     [134,] 6.30000000 2.80000000 5.10000000 1.50000000
     [135,] 6.10000000 2.60000000 5.60000000 1.40000000
     [136,] 7.70000000 3.00000000 6.10000000 2.30000000
     [137,] 6.30000000 3.40000000 5.60000000 1.75461411
     [138,] 6.40000000 3.10000000 5.50000000 1.80000000
     [139,] 6.00000000 3.00000000 4.80000000 1.80000000
     [140,] 6.90000000 3.10000000 5.40000000 2.10000000
     [141,] 6.70000000 3.10000000 5.60000000 2.40000000
     [142,] 6.90000000 3.10000000 5.10000000 2.30000000
     [143,] 5.80000000 2.70000000 5.10000000 1.90000000
     [144,] 6.80000000 3.20000000 5.90000000 2.30000000
     [145,] 6.70000000 3.30000000 5.70000000 2.50000000
     [146,] 6.70000000 3.00000000 5.20000000 2.30000000
     [147,] 6.30000000 2.50000000 5.00000000 1.90000000
     [148,] 6.50000000 3.00000000 5.20000000 2.00000000
     [149,] 6.20000000 3.40000000 5.40000000 2.30000000
     [150,] 5.90000000 3.00000000 5.10000000 1.80000000
     * missing =
     row col
     * nbSample = 150
     * nbCluster = 3
     * lnLikelihood = -1028.104
     * nbFreeParameter= 70
     * criterion name = ICL
     * criterion value= 2414.36
     * zi =
     [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
     [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
     [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
     [112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
     [149] 2 2
     ****************************************
     *** Cluster: 1
     * Proportion = 0.3333333
     * Means = 5.0060000 3.4280000 1.4573207 0.2419939
     * S.D. = 0.3489470 0.3752546 0.1712817 0.1132215
     ****************************************
     *** Cluster: 2
     * Proportion = 0.3333333
     * Means = 5.93600 2.76876 4.26000 1.32600
     * S.D. = 0.5109834 0.3106460 0.4651881 0.1957652
     ****************************************
     *** Cluster: 3
     * Proportion = 0.3333333
     * Means = 6.588000 2.974000 5.552000 2.013092
     * S.D. = 0.6294887 0.3192554 0.5463479 0.2691336
     ****************************************
     > model <- learnGamma( data=x, labels= z,
     + , models = clusterGammaNames(prop = "equal")
     + , algo = "simul", nbIter = 2, epsilon = 1e-08
     + )
Flavor: r-devel-windows-ix86+x86_64

Version: 1.5.1
Check: running tests for arch ‘i386’
Result: ERROR
     Running 'ClusterSimul.R' [0s]
     Running 'clusterDiagGaussianLikelihood.R' [1s]
     Running 'clusterGammaLikelihood.R' [1s]
     Running 'simulHeterogeneous.R' [0s]
     Running 'simulNonLinear.R' [1s]
     Running 'testAllLearners.R' [1s]
     Running 'testPoissonExample.R' [1s]
     Running 'testPredict.R' [9s]
    Running the tests in 'tests/testAllLearners.R' failed.
    Complete output:
     > library(MixAll)
     Loading required package: rtkore
     Loading required package: Rcpp
    
     Attaching package: 'rtkore'
    
     The following object is masked from 'package:Rcpp':
    
     LdFlags
    
     > ## get data and target from iris data set
     > data(iris)
     > x <- as.matrix(iris[,1:4]); z <- as.vector(iris[,5]); n <- nrow(x); p <- ncol(x)
     > ## add missing values at random
     > indexes <- matrix(c(round(runif(5,1,n)), round(runif(5,1,p))), ncol=2)
     > cbind(indexes, x[indexes])
     [,1] [,2] [,3]
     [1,] 98 2 2.9
     [2,] 126 4 1.8
     [3,] 9 4 0.2
     [4,] 120 4 1.5
     [5,] 8 4 0.2
     > x[indexes] <- NA
     > ## learn continuous model
     > model <- learnDiagGaussian( data=x, labels= z, prop = c(1/3,1/3,1/3)
     + , models = clusterDiagGaussianNames(prop = "equal")
     + , algo = "simul", nbIter = 2, epsilon = 1e-08
     + )
     > missingValues(model)
     row col value
     1 98 2 2.91136488
     2 8 4 -0.14681092
     3 9 4 -0.06330799
     4 120 4 1.60998473
     5 126 4 1.77714576
     > print(model)
     ****************************************
     * model name = gaussian_p_s
     * data =
     Sepal.Length Sepal.Width Petal.Length Petal.Width
     [1,] 5.10000000 3.50000000 1.40000000 0.20000000
     [2,] 4.90000000 3.00000000 1.40000000 0.20000000
     [3,] 4.70000000 3.20000000 1.30000000 0.20000000
     [4,] 4.60000000 3.10000000 1.50000000 0.20000000
     [5,] 5.00000000 3.60000000 1.40000000 0.20000000
     [6,] 5.40000000 3.90000000 1.70000000 0.40000000
     [7,] 4.60000000 3.40000000 1.40000000 0.30000000
     [8,] 5.00000000 3.40000000 1.50000000 -0.14681092
     [9,] 4.40000000 2.90000000 1.40000000 -0.06330799
     [10,] 4.90000000 3.10000000 1.50000000 0.10000000
     [11,] 5.40000000 3.70000000 1.50000000 0.20000000
     [12,] 4.80000000 3.40000000 1.60000000 0.20000000
     [13,] 4.80000000 3.00000000 1.40000000 0.10000000
     [14,] 4.30000000 3.00000000 1.10000000 0.10000000
     [15,] 5.80000000 4.00000000 1.20000000 0.20000000
     [16,] 5.70000000 4.40000000 1.50000000 0.40000000
     [17,] 5.40000000 3.90000000 1.30000000 0.40000000
     [18,] 5.10000000 3.50000000 1.40000000 0.30000000
     [19,] 5.70000000 3.80000000 1.70000000 0.30000000
     [20,] 5.10000000 3.80000000 1.50000000 0.30000000
     [21,] 5.40000000 3.40000000 1.70000000 0.20000000
     [22,] 5.10000000 3.70000000 1.50000000 0.40000000
     [23,] 4.60000000 3.60000000 1.00000000 0.20000000
     [24,] 5.10000000 3.30000000 1.70000000 0.50000000
     [25,] 4.80000000 3.40000000 1.90000000 0.20000000
     [26,] 5.00000000 3.00000000 1.60000000 0.20000000
     [27,] 5.00000000 3.40000000 1.60000000 0.40000000
     [28,] 5.20000000 3.50000000 1.50000000 0.20000000
     [29,] 5.20000000 3.40000000 1.40000000 0.20000000
     [30,] 4.70000000 3.20000000 1.60000000 0.20000000
     [31,] 4.80000000 3.10000000 1.60000000 0.20000000
     [32,] 5.40000000 3.40000000 1.50000000 0.40000000
     [33,] 5.20000000 4.10000000 1.50000000 0.10000000
     [34,] 5.50000000 4.20000000 1.40000000 0.20000000
     [35,] 4.90000000 3.10000000 1.50000000 0.20000000
     [36,] 5.00000000 3.20000000 1.20000000 0.20000000
     [37,] 5.50000000 3.50000000 1.30000000 0.20000000
     [38,] 4.90000000 3.60000000 1.40000000 0.10000000
     [39,] 4.40000000 3.00000000 1.30000000 0.20000000
     [40,] 5.10000000 3.40000000 1.50000000 0.20000000
     [41,] 5.00000000 3.50000000 1.30000000 0.30000000
     [42,] 4.50000000 2.30000000 1.30000000 0.30000000
     [43,] 4.40000000 3.20000000 1.30000000 0.20000000
     [44,] 5.00000000 3.50000000 1.60000000 0.60000000
     [45,] 5.10000000 3.80000000 1.90000000 0.40000000
     [46,] 4.80000000 3.00000000 1.40000000 0.30000000
     [47,] 5.10000000 3.80000000 1.60000000 0.20000000
     [48,] 4.60000000 3.20000000 1.40000000 0.20000000
     [49,] 5.30000000 3.70000000 1.50000000 0.20000000
     [50,] 5.00000000 3.30000000 1.40000000 0.20000000
     [51,] 7.00000000 3.20000000 4.70000000 1.40000000
     [52,] 6.40000000 3.20000000 4.50000000 1.50000000
     [53,] 6.90000000 3.10000000 4.90000000 1.50000000
     [54,] 5.50000000 2.30000000 4.00000000 1.30000000
     [55,] 6.50000000 2.80000000 4.60000000 1.50000000
     [56,] 5.70000000 2.80000000 4.50000000 1.30000000
     [57,] 6.30000000 3.30000000 4.70000000 1.60000000
     [58,] 4.90000000 2.40000000 3.30000000 1.00000000
     [59,] 6.60000000 2.90000000 4.60000000 1.30000000
     [60,] 5.20000000 2.70000000 3.90000000 1.40000000
     [61,] 5.00000000 2.00000000 3.50000000 1.00000000
     [62,] 5.90000000 3.00000000 4.20000000 1.50000000
     [63,] 6.00000000 2.20000000 4.00000000 1.00000000
     [64,] 6.10000000 2.90000000 4.70000000 1.40000000
     [65,] 5.60000000 2.90000000 3.60000000 1.30000000
     [66,] 6.70000000 3.10000000 4.40000000 1.40000000
     [67,] 5.60000000 3.00000000 4.50000000 1.50000000
     [68,] 5.80000000 2.70000000 4.10000000 1.00000000
     [69,] 6.20000000 2.20000000 4.50000000 1.50000000
     [70,] 5.60000000 2.50000000 3.90000000 1.10000000
     [71,] 5.90000000 3.20000000 4.80000000 1.80000000
     [72,] 6.10000000 2.80000000 4.00000000 1.30000000
     [73,] 6.30000000 2.50000000 4.90000000 1.50000000
     [74,] 6.10000000 2.80000000 4.70000000 1.20000000
     [75,] 6.40000000 2.90000000 4.30000000 1.30000000
     [76,] 6.60000000 3.00000000 4.40000000 1.40000000
     [77,] 6.80000000 2.80000000 4.80000000 1.40000000
     [78,] 6.70000000 3.00000000 5.00000000 1.70000000
     [79,] 6.00000000 2.90000000 4.50000000 1.50000000
     [80,] 5.70000000 2.60000000 3.50000000 1.00000000
     [81,] 5.50000000 2.40000000 3.80000000 1.10000000
     [82,] 5.50000000 2.40000000 3.70000000 1.00000000
     [83,] 5.80000000 2.70000000 3.90000000 1.20000000
     [84,] 6.00000000 2.70000000 5.10000000 1.60000000
     [85,] 5.40000000 3.00000000 4.50000000 1.50000000
     [86,] 6.00000000 3.40000000 4.50000000 1.60000000
     [87,] 6.70000000 3.10000000 4.70000000 1.50000000
     [88,] 6.30000000 2.30000000 4.40000000 1.30000000
     [89,] 5.60000000 3.00000000 4.10000000 1.30000000
     [90,] 5.50000000 2.50000000 4.00000000 1.30000000
     [91,] 5.50000000 2.60000000 4.40000000 1.20000000
     [92,] 6.10000000 3.00000000 4.60000000 1.40000000
     [93,] 5.80000000 2.60000000 4.00000000 1.20000000
     [94,] 5.00000000 2.30000000 3.30000000 1.00000000
     [95,] 5.60000000 2.70000000 4.20000000 1.30000000
     [96,] 5.70000000 3.00000000 4.20000000 1.20000000
     [97,] 5.70000000 2.90000000 4.20000000 1.30000000
     [98,] 6.20000000 2.91136488 4.30000000 1.30000000
     [99,] 5.10000000 2.50000000 3.00000000 1.10000000
     [100,] 5.70000000 2.80000000 4.10000000 1.30000000
     [101,] 6.30000000 3.30000000 6.00000000 2.50000000
     [102,] 5.80000000 2.70000000 5.10000000 1.90000000
     [103,] 7.10000000 3.00000000 5.90000000 2.10000000
     [104,] 6.30000000 2.90000000 5.60000000 1.80000000
     [105,] 6.50000000 3.00000000 5.80000000 2.20000000
     [106,] 7.60000000 3.00000000 6.60000000 2.10000000
     [107,] 4.90000000 2.50000000 4.50000000 1.70000000
     [108,] 7.30000000 2.90000000 6.30000000 1.80000000
     [109,] 6.70000000 2.50000000 5.80000000 1.80000000
     [110,] 7.20000000 3.60000000 6.10000000 2.50000000
     [111,] 6.50000000 3.20000000 5.10000000 2.00000000
     [112,] 6.40000000 2.70000000 5.30000000 1.90000000
     [113,] 6.80000000 3.00000000 5.50000000 2.10000000
     [114,] 5.70000000 2.50000000 5.00000000 2.00000000
     [115,] 5.80000000 2.80000000 5.10000000 2.40000000
     [116,] 6.40000000 3.20000000 5.30000000 2.30000000
     [117,] 6.50000000 3.00000000 5.50000000 1.80000000
     [118,] 7.70000000 3.80000000 6.70000000 2.20000000
     [119,] 7.70000000 2.60000000 6.90000000 2.30000000
     [120,] 6.00000000 2.20000000 5.00000000 1.60998473
     [121,] 6.90000000 3.20000000 5.70000000 2.30000000
     [122,] 5.60000000 2.80000000 4.90000000 2.00000000
     [123,] 7.70000000 2.80000000 6.70000000 2.00000000
     [124,] 6.30000000 2.70000000 4.90000000 1.80000000
     [125,] 6.70000000 3.30000000 5.70000000 2.10000000
     [126,] 7.20000000 3.20000000 6.00000000 1.77714576
     [127,] 6.20000000 2.80000000 4.80000000 1.80000000
     [128,] 6.10000000 3.00000000 4.90000000 1.80000000
     [129,] 6.40000000 2.80000000 5.60000000 2.10000000
     [130,] 7.20000000 3.00000000 5.80000000 1.60000000
     [131,] 7.40000000 2.80000000 6.10000000 1.90000000
     [132,] 7.90000000 3.80000000 6.40000000 2.00000000
     [133,] 6.40000000 2.80000000 5.60000000 2.20000000
     [134,] 6.30000000 2.80000000 5.10000000 1.50000000
     [135,] 6.10000000 2.60000000 5.60000000 1.40000000
     [136,] 7.70000000 3.00000000 6.10000000 2.30000000
     [137,] 6.30000000 3.40000000 5.60000000 2.40000000
     [138,] 6.40000000 3.10000000 5.50000000 1.80000000
     [139,] 6.00000000 3.00000000 4.80000000 1.80000000
     [140,] 6.90000000 3.10000000 5.40000000 2.10000000
     [141,] 6.70000000 3.10000000 5.60000000 2.40000000
     [142,] 6.90000000 3.10000000 5.10000000 2.30000000
     [143,] 5.80000000 2.70000000 5.10000000 1.90000000
     [144,] 6.80000000 3.20000000 5.90000000 2.30000000
     [145,] 6.70000000 3.30000000 5.70000000 2.50000000
     [146,] 6.70000000 3.00000000 5.20000000 2.30000000
     [147,] 6.30000000 2.50000000 5.00000000 1.90000000
     [148,] 6.50000000 3.00000000 5.20000000 2.00000000
     [149,] 6.20000000 3.40000000 5.40000000 2.30000000
     [150,] 5.90000000 3.00000000 5.10000000 1.80000000
     * missing =
     row col
     [1,] 98 2
     [2,] 8 4
     [3,] 9 4
     [4,] 120 4
     [5,] 126 4
     * nbSample = 150
     * nbCluster = 3
     * lnLikelihood = -1034.915
     * nbFreeParameter= 70
     * criterion name = ICL
     * criterion value= 2427.374
     * zi =
     [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
     [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
     [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
     [112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
     [149] 2 2
     ****************************************
     *** Cluster: 1
     * Proportion = 0.3333333
     * Means = 5.0060000 3.4280000 1.4620000 0.2337976
     * S.D. = 0.3861042 0.3861042 0.3861042 0.3861042
     ****************************************
     *** Cluster: 2
     * Proportion = 0.3333333
     * Means = 5.936000 2.770227 4.260000 1.326000
     * S.D. = 0.3861042 0.3861042 0.3861042 0.3861042
     ****************************************
     *** Cluster: 3
     * Proportion = 0.3333333
     * Means = 6.588000 2.974000 5.552000 2.027743
     * S.D. = 0.3861042 0.3861042 0.3861042 0.3861042
     ****************************************
     > model <- learnDiagGaussian( data=x, labels= z,
     + , models = clusterDiagGaussianNames(prop = "equal")
     + , algo = "impute", nbIter = 2, epsilon = 1e-08)
     > missingValues(model)
     row col value
     > print(model)
     ****************************************
     * model name = gaussian_p_sjk
     * data =
     Sepal.Length Sepal.Width Petal.Length Petal.Width
     [1,] 5.10000000 3.50000000 1.40000000 0.20000000
     [2,] 4.90000000 3.00000000 1.40000000 0.20000000
     [3,] 4.70000000 3.20000000 1.30000000 0.20000000
     [4,] 4.60000000 3.10000000 1.50000000 0.20000000
     [5,] 5.00000000 3.60000000 1.40000000 0.20000000
     [6,] 5.40000000 3.90000000 1.70000000 0.40000000
     [7,] 4.60000000 3.40000000 1.40000000 0.30000000
     [8,] 5.00000000 3.40000000 1.50000000 -0.14681092
     [9,] 4.40000000 2.90000000 1.40000000 -0.06330799
     [10,] 4.90000000 3.10000000 1.50000000 0.10000000
     [11,] 5.40000000 3.70000000 1.50000000 0.20000000
     [12,] 4.80000000 3.40000000 1.60000000 0.20000000
     [13,] 4.80000000 3.00000000 1.40000000 0.10000000
     [14,] 4.30000000 3.00000000 1.10000000 0.10000000
     [15,] 5.80000000 4.00000000 1.20000000 0.20000000
     [16,] 5.70000000 4.40000000 1.50000000 0.40000000
     [17,] 5.40000000 3.90000000 1.30000000 0.40000000
     [18,] 5.10000000 3.50000000 1.40000000 0.30000000
     [19,] 5.70000000 3.80000000 1.70000000 0.30000000
     [20,] 5.10000000 3.80000000 1.50000000 0.30000000
     [21,] 5.40000000 3.40000000 1.70000000 0.20000000
     [22,] 5.10000000 3.70000000 1.50000000 0.40000000
     [23,] 4.60000000 3.60000000 1.00000000 0.20000000
     [24,] 5.10000000 3.30000000 1.70000000 0.50000000
     [25,] 4.80000000 3.40000000 1.90000000 0.20000000
     [26,] 5.00000000 3.00000000 1.60000000 0.20000000
     [27,] 5.00000000 3.40000000 1.60000000 0.40000000
     [28,] 5.20000000 3.50000000 1.50000000 0.20000000
     [29,] 5.20000000 3.40000000 1.40000000 0.20000000
     [30,] 4.70000000 3.20000000 1.60000000 0.20000000
     [31,] 4.80000000 3.10000000 1.60000000 0.20000000
     [32,] 5.40000000 3.40000000 1.50000000 0.40000000
     [33,] 5.20000000 4.10000000 1.50000000 0.10000000
     [34,] 5.50000000 4.20000000 1.40000000 0.20000000
     [35,] 4.90000000 3.10000000 1.50000000 0.20000000
     [36,] 5.00000000 3.20000000 1.20000000 0.20000000
     [37,] 5.50000000 3.50000000 1.30000000 0.20000000
     [38,] 4.90000000 3.60000000 1.40000000 0.10000000
     [39,] 4.40000000 3.00000000 1.30000000 0.20000000
     [40,] 5.10000000 3.40000000 1.50000000 0.20000000
     [41,] 5.00000000 3.50000000 1.30000000 0.30000000
     [42,] 4.50000000 2.30000000 1.30000000 0.30000000
     [43,] 4.40000000 3.20000000 1.30000000 0.20000000
     [44,] 5.00000000 3.50000000 1.60000000 0.60000000
     [45,] 5.10000000 3.80000000 1.90000000 0.40000000
     [46,] 4.80000000 3.00000000 1.40000000 0.30000000
     [47,] 5.10000000 3.80000000 1.60000000 0.20000000
     [48,] 4.60000000 3.20000000 1.40000000 0.20000000
     [49,] 5.30000000 3.70000000 1.50000000 0.20000000
     [50,] 5.00000000 3.30000000 1.40000000 0.20000000
     [51,] 7.00000000 3.20000000 4.70000000 1.40000000
     [52,] 6.40000000 3.20000000 4.50000000 1.50000000
     [53,] 6.90000000 3.10000000 4.90000000 1.50000000
     [54,] 5.50000000 2.30000000 4.00000000 1.30000000
     [55,] 6.50000000 2.80000000 4.60000000 1.50000000
     [56,] 5.70000000 2.80000000 4.50000000 1.30000000
     [57,] 6.30000000 3.30000000 4.70000000 1.60000000
     [58,] 4.90000000 2.40000000 3.30000000 1.00000000
     [59,] 6.60000000 2.90000000 4.60000000 1.30000000
     [60,] 5.20000000 2.70000000 3.90000000 1.40000000
     [61,] 5.00000000 2.00000000 3.50000000 1.00000000
     [62,] 5.90000000 3.00000000 4.20000000 1.50000000
     [63,] 6.00000000 2.20000000 4.00000000 1.00000000
     [64,] 6.10000000 2.90000000 4.70000000 1.40000000
     [65,] 5.60000000 2.90000000 3.60000000 1.30000000
     [66,] 6.70000000 3.10000000 4.40000000 1.40000000
     [67,] 5.60000000 3.00000000 4.50000000 1.50000000
     [68,] 5.80000000 2.70000000 4.10000000 1.00000000
     [69,] 6.20000000 2.20000000 4.50000000 1.50000000
     [70,] 5.60000000 2.50000000 3.90000000 1.10000000
     [71,] 5.90000000 3.20000000 4.80000000 1.80000000
     [72,] 6.10000000 2.80000000 4.00000000 1.30000000
     [73,] 6.30000000 2.50000000 4.90000000 1.50000000
     [74,] 6.10000000 2.80000000 4.70000000 1.20000000
     [75,] 6.40000000 2.90000000 4.30000000 1.30000000
     [76,] 6.60000000 3.00000000 4.40000000 1.40000000
     [77,] 6.80000000 2.80000000 4.80000000 1.40000000
     [78,] 6.70000000 3.00000000 5.00000000 1.70000000
     [79,] 6.00000000 2.90000000 4.50000000 1.50000000
     [80,] 5.70000000 2.60000000 3.50000000 1.00000000
     [81,] 5.50000000 2.40000000 3.80000000 1.10000000
     [82,] 5.50000000 2.40000000 3.70000000 1.00000000
     [83,] 5.80000000 2.70000000 3.90000000 1.20000000
     [84,] 6.00000000 2.70000000 5.10000000 1.60000000
     [85,] 5.40000000 3.00000000 4.50000000 1.50000000
     [86,] 6.00000000 3.40000000 4.50000000 1.60000000
     [87,] 6.70000000 3.10000000 4.70000000 1.50000000
     [88,] 6.30000000 2.30000000 4.40000000 1.30000000
     [89,] 5.60000000 3.00000000 4.10000000 1.30000000
     [90,] 5.50000000 2.50000000 4.00000000 1.30000000
     [91,] 5.50000000 2.60000000 4.40000000 1.20000000
     [92,] 6.10000000 3.00000000 4.60000000 1.40000000
     [93,] 5.80000000 2.60000000 4.00000000 1.20000000
     [94,] 5.00000000 2.30000000 3.30000000 1.00000000
     [95,] 5.60000000 2.70000000 4.20000000 1.30000000
     [96,] 5.70000000 3.00000000 4.20000000 1.20000000
     [97,] 5.70000000 2.90000000 4.20000000 1.30000000
     [98,] 6.20000000 2.91136488 4.30000000 1.30000000
     [99,] 5.10000000 2.50000000 3.00000000 1.10000000
     [100,] 5.70000000 2.80000000 4.10000000 1.30000000
     [101,] 6.30000000 3.30000000 6.00000000 2.50000000
     [102,] 5.80000000 2.70000000 5.10000000 1.90000000
     [103,] 7.10000000 3.00000000 5.90000000 2.10000000
     [104,] 6.30000000 2.90000000 5.60000000 1.80000000
     [105,] 6.50000000 3.00000000 5.80000000 2.20000000
     [106,] 7.60000000 3.00000000 6.60000000 2.10000000
     [107,] 4.90000000 2.50000000 4.50000000 1.70000000
     [108,] 7.30000000 2.90000000 6.30000000 1.80000000
     [109,] 6.70000000 2.50000000 5.80000000 1.80000000
     [110,] 7.20000000 3.60000000 6.10000000 2.50000000
     [111,] 6.50000000 3.20000000 5.10000000 2.00000000
     [112,] 6.40000000 2.70000000 5.30000000 1.90000000
     [113,] 6.80000000 3.00000000 5.50000000 2.10000000
     [114,] 5.70000000 2.50000000 5.00000000 2.00000000
     [115,] 5.80000000 2.80000000 5.10000000 2.40000000
     [116,] 6.40000000 3.20000000 5.30000000 2.30000000
     [117,] 6.50000000 3.00000000 5.50000000 1.80000000
     [118,] 7.70000000 3.80000000 6.70000000 2.20000000
     [119,] 7.70000000 2.60000000 6.90000000 2.30000000
     [120,] 6.00000000 2.20000000 5.00000000 1.60998473
     [121,] 6.90000000 3.20000000 5.70000000 2.30000000
     [122,] 5.60000000 2.80000000 4.90000000 2.00000000
     [123,] 7.70000000 2.80000000 6.70000000 2.00000000
     [124,] 6.30000000 2.70000000 4.90000000 1.80000000
     [125,] 6.70000000 3.30000000 5.70000000 2.10000000
     [126,] 7.20000000 3.20000000 6.00000000 1.77714576
     [127,] 6.20000000 2.80000000 4.80000000 1.80000000
     [128,] 6.10000000 3.00000000 4.90000000 1.80000000
     [129,] 6.40000000 2.80000000 5.60000000 2.10000000
     [130,] 7.20000000 3.00000000 5.80000000 1.60000000
     [131,] 7.40000000 2.80000000 6.10000000 1.90000000
     [132,] 7.90000000 3.80000000 6.40000000 2.00000000
     [133,] 6.40000000 2.80000000 5.60000000 2.20000000
     [134,] 6.30000000 2.80000000 5.10000000 1.50000000
     [135,] 6.10000000 2.60000000 5.60000000 1.40000000
     [136,] 7.70000000 3.00000000 6.10000000 2.30000000
     [137,] 6.30000000 3.40000000 5.60000000 2.40000000
     [138,] 6.40000000 3.10000000 5.50000000 1.80000000
     [139,] 6.00000000 3.00000000 4.80000000 1.80000000
     [140,] 6.90000000 3.10000000 5.40000000 2.10000000
     [141,] 6.70000000 3.10000000 5.60000000 2.40000000
     [142,] 6.90000000 3.10000000 5.10000000 2.30000000
     [143,] 5.80000000 2.70000000 5.10000000 1.90000000
     [144,] 6.80000000 3.20000000 5.90000000 2.30000000
     [145,] 6.70000000 3.30000000 5.70000000 2.50000000
     [146,] 6.70000000 3.00000000 5.20000000 2.30000000
     [147,] 6.30000000 2.50000000 5.00000000 1.90000000
     [148,] 6.50000000 3.00000000 5.20000000 2.00000000
     [149,] 6.20000000 3.40000000 5.40000000 2.30000000
     [150,] 5.90000000 3.00000000 5.10000000 1.80000000
     * missing =
     row col
     * nbSample = 150
     * nbCluster = 3
     * lnLikelihood = -1040.166
     * nbFreeParameter= 70
     * criterion name = ICL
     * criterion value= 2438.357
     * zi =
     [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
     [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
     [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
     [112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
     [149] 2 2
     ****************************************
     *** Cluster: 1
     * Proportion = 0.3333333
     * Means = 5.0060000 3.4280000 1.4620000 0.2337976
     * S.D. = 0.3489470 0.3752546 0.1719186 0.1250996
     ****************************************
     *** Cluster: 2
     * Proportion = 0.3333333
     * Means = 5.936000 2.770227 4.260000 1.326000
     * S.D. = 0.5109834 0.3107437 0.4651881 0.1957652
     ****************************************
     *** Cluster: 3
     * Proportion = 0.3333333
     * Means = 6.588000 2.974000 5.552000 2.027743
     * S.D. = 0.6294887 0.3192554 0.5463479 0.2684509
     ****************************************
     > model <- learnGamma( data=x, labels= z,
     + , models = clusterGammaNames(prop = "equal")
     + , algo = "simul", nbIter = 2, epsilon = 1e-08
     + )
Flavor: r-oldrel-windows-ix86+x86_64