CRAN Package Check Results for Package MixAll

Last updated on 2020-12-04 00:48:27 CET.

Check Details

Version: 1.5.1
Check: tests
Result: ERROR
     Running ‘ClusterSimul.R’ [0s/1s]
     Running ‘clusterDiagGaussianLikelihood.R’ [1s/1s]
     Running ‘clusterGammaLikelihood.R’ [1s/2s]
     Running ‘simulHeterogeneous.R’ [0s/0s]
     Running ‘simulNonLinear.R’ [0s/1s]
     Running ‘testAllLearners.R’ [1s/1s]
     Running ‘testPoissonExample.R’ [1s/2s]
     Running ‘testPredict.R’ [6s/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,] 145 3 5.7
     [2,] 61 3 3.5
     [3,] 149 3 5.4
     [4,] 96 3 4.2
     [5,] 26 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 61 3 3.6716278
     2 96 3 3.5737922
     3 145 3 4.9110918
     4 149 3 5.4618991
     5 26 4 -0.6602505
     > print(model)
     ****************************************
     * model name = gaussian_p_s
     * data =
     Sepal.Length Sepal.Width Petal.Length Petal.Width
     [1,] 5.1000000 3.5000000 1.4000000 0.2000000
     [2,] 4.9000000 3.0000000 1.4000000 0.2000000
     [3,] 4.7000000 3.2000000 1.3000000 0.2000000
     [4,] 4.6000000 3.1000000 1.5000000 0.2000000
     [5,] 5.0000000 3.6000000 1.4000000 0.2000000
     [6,] 5.4000000 3.9000000 1.7000000 0.4000000
     [7,] 4.6000000 3.4000000 1.4000000 0.3000000
     [8,] 5.0000000 3.4000000 1.5000000 0.2000000
     [9,] 4.4000000 2.9000000 1.4000000 0.2000000
     [10,] 4.9000000 3.1000000 1.5000000 0.1000000
     [11,] 5.4000000 3.7000000 1.5000000 0.2000000
     [12,] 4.8000000 3.4000000 1.6000000 0.2000000
     [13,] 4.8000000 3.0000000 1.4000000 0.1000000
     [14,] 4.3000000 3.0000000 1.1000000 0.1000000
     [15,] 5.8000000 4.0000000 1.2000000 0.2000000
     [16,] 5.7000000 4.4000000 1.5000000 0.4000000
     [17,] 5.4000000 3.9000000 1.3000000 0.4000000
     [18,] 5.1000000 3.5000000 1.4000000 0.3000000
     [19,] 5.7000000 3.8000000 1.7000000 0.3000000
     [20,] 5.1000000 3.8000000 1.5000000 0.3000000
     [21,] 5.4000000 3.4000000 1.7000000 0.2000000
     [22,] 5.1000000 3.7000000 1.5000000 0.4000000
     [23,] 4.6000000 3.6000000 1.0000000 0.2000000
     [24,] 5.1000000 3.3000000 1.7000000 0.5000000
     [25,] 4.8000000 3.4000000 1.9000000 0.2000000
     [26,] 5.0000000 3.0000000 1.6000000 -0.6602505
     [27,] 5.0000000 3.4000000 1.6000000 0.4000000
     [28,] 5.2000000 3.5000000 1.5000000 0.2000000
     [29,] 5.2000000 3.4000000 1.4000000 0.2000000
     [30,] 4.7000000 3.2000000 1.6000000 0.2000000
     [31,] 4.8000000 3.1000000 1.6000000 0.2000000
     [32,] 5.4000000 3.4000000 1.5000000 0.4000000
     [33,] 5.2000000 4.1000000 1.5000000 0.1000000
     [34,] 5.5000000 4.2000000 1.4000000 0.2000000
     [35,] 4.9000000 3.1000000 1.5000000 0.2000000
     [36,] 5.0000000 3.2000000 1.2000000 0.2000000
     [37,] 5.5000000 3.5000000 1.3000000 0.2000000
     [38,] 4.9000000 3.6000000 1.4000000 0.1000000
     [39,] 4.4000000 3.0000000 1.3000000 0.2000000
     [40,] 5.1000000 3.4000000 1.5000000 0.2000000
     [41,] 5.0000000 3.5000000 1.3000000 0.3000000
     [42,] 4.5000000 2.3000000 1.3000000 0.3000000
     [43,] 4.4000000 3.2000000 1.3000000 0.2000000
     [44,] 5.0000000 3.5000000 1.6000000 0.6000000
     [45,] 5.1000000 3.8000000 1.9000000 0.4000000
     [46,] 4.8000000 3.0000000 1.4000000 0.3000000
     [47,] 5.1000000 3.8000000 1.6000000 0.2000000
     [48,] 4.6000000 3.2000000 1.4000000 0.2000000
     [49,] 5.3000000 3.7000000 1.5000000 0.2000000
     [50,] 5.0000000 3.3000000 1.4000000 0.2000000
     [51,] 7.0000000 3.2000000 4.7000000 1.4000000
     [52,] 6.4000000 3.2000000 4.5000000 1.5000000
     [53,] 6.9000000 3.1000000 4.9000000 1.5000000
     [54,] 5.5000000 2.3000000 4.0000000 1.3000000
     [55,] 6.5000000 2.8000000 4.6000000 1.5000000
     [56,] 5.7000000 2.8000000 4.5000000 1.3000000
     [57,] 6.3000000 3.3000000 4.7000000 1.6000000
     [58,] 4.9000000 2.4000000 3.3000000 1.0000000
     [59,] 6.6000000 2.9000000 4.6000000 1.3000000
     [60,] 5.2000000 2.7000000 3.9000000 1.4000000
     [61,] 5.0000000 2.0000000 3.6716278 1.0000000
     [62,] 5.9000000 3.0000000 4.2000000 1.5000000
     [63,] 6.0000000 2.2000000 4.0000000 1.0000000
     [64,] 6.1000000 2.9000000 4.7000000 1.4000000
     [65,] 5.6000000 2.9000000 3.6000000 1.3000000
     [66,] 6.7000000 3.1000000 4.4000000 1.4000000
     [67,] 5.6000000 3.0000000 4.5000000 1.5000000
     [68,] 5.8000000 2.7000000 4.1000000 1.0000000
     [69,] 6.2000000 2.2000000 4.5000000 1.5000000
     [70,] 5.6000000 2.5000000 3.9000000 1.1000000
     [71,] 5.9000000 3.2000000 4.8000000 1.8000000
     [72,] 6.1000000 2.8000000 4.0000000 1.3000000
     [73,] 6.3000000 2.5000000 4.9000000 1.5000000
     [74,] 6.1000000 2.8000000 4.7000000 1.2000000
     [75,] 6.4000000 2.9000000 4.3000000 1.3000000
     [76,] 6.6000000 3.0000000 4.4000000 1.4000000
     [77,] 6.8000000 2.8000000 4.8000000 1.4000000
     [78,] 6.7000000 3.0000000 5.0000000 1.7000000
     [79,] 6.0000000 2.9000000 4.5000000 1.5000000
     [80,] 5.7000000 2.6000000 3.5000000 1.0000000
     [81,] 5.5000000 2.4000000 3.8000000 1.1000000
     [82,] 5.5000000 2.4000000 3.7000000 1.0000000
     [83,] 5.8000000 2.7000000 3.9000000 1.2000000
     [84,] 6.0000000 2.7000000 5.1000000 1.6000000
     [85,] 5.4000000 3.0000000 4.5000000 1.5000000
     [86,] 6.0000000 3.4000000 4.5000000 1.6000000
     [87,] 6.7000000 3.1000000 4.7000000 1.5000000
     [88,] 6.3000000 2.3000000 4.4000000 1.3000000
     [89,] 5.6000000 3.0000000 4.1000000 1.3000000
     [90,] 5.5000000 2.5000000 4.0000000 1.3000000
     [91,] 5.5000000 2.6000000 4.4000000 1.2000000
     [92,] 6.1000000 3.0000000 4.6000000 1.4000000
     [93,] 5.8000000 2.6000000 4.0000000 1.2000000
     [94,] 5.0000000 2.3000000 3.3000000 1.0000000
     [95,] 5.6000000 2.7000000 4.2000000 1.3000000
     [96,] 5.7000000 3.0000000 3.5737922 1.2000000
     [97,] 5.7000000 2.9000000 4.2000000 1.3000000
     [98,] 6.2000000 2.9000000 4.3000000 1.3000000
     [99,] 5.1000000 2.5000000 3.0000000 1.1000000
     [100,] 5.7000000 2.8000000 4.1000000 1.3000000
     [101,] 6.3000000 3.3000000 6.0000000 2.5000000
     [102,] 5.8000000 2.7000000 5.1000000 1.9000000
     [103,] 7.1000000 3.0000000 5.9000000 2.1000000
     [104,] 6.3000000 2.9000000 5.6000000 1.8000000
     [105,] 6.5000000 3.0000000 5.8000000 2.2000000
     [106,] 7.6000000 3.0000000 6.6000000 2.1000000
     [107,] 4.9000000 2.5000000 4.5000000 1.7000000
     [108,] 7.3000000 2.9000000 6.3000000 1.8000000
     [109,] 6.7000000 2.5000000 5.8000000 1.8000000
     [110,] 7.2000000 3.6000000 6.1000000 2.5000000
     [111,] 6.5000000 3.2000000 5.1000000 2.0000000
     [112,] 6.4000000 2.7000000 5.3000000 1.9000000
     [113,] 6.8000000 3.0000000 5.5000000 2.1000000
     [114,] 5.7000000 2.5000000 5.0000000 2.0000000
     [115,] 5.8000000 2.8000000 5.1000000 2.4000000
     [116,] 6.4000000 3.2000000 5.3000000 2.3000000
     [117,] 6.5000000 3.0000000 5.5000000 1.8000000
     [118,] 7.7000000 3.8000000 6.7000000 2.2000000
     [119,] 7.7000000 2.6000000 6.9000000 2.3000000
     [120,] 6.0000000 2.2000000 5.0000000 1.5000000
     [121,] 6.9000000 3.2000000 5.7000000 2.3000000
     [122,] 5.6000000 2.8000000 4.9000000 2.0000000
     [123,] 7.7000000 2.8000000 6.7000000 2.0000000
     [124,] 6.3000000 2.7000000 4.9000000 1.8000000
     [125,] 6.7000000 3.3000000 5.7000000 2.1000000
     [126,] 7.2000000 3.2000000 6.0000000 1.8000000
     [127,] 6.2000000 2.8000000 4.8000000 1.8000000
     [128,] 6.1000000 3.0000000 4.9000000 1.8000000
     [129,] 6.4000000 2.8000000 5.6000000 2.1000000
     [130,] 7.2000000 3.0000000 5.8000000 1.6000000
     [131,] 7.4000000 2.8000000 6.1000000 1.9000000
     [132,] 7.9000000 3.8000000 6.4000000 2.0000000
     [133,] 6.4000000 2.8000000 5.6000000 2.2000000
     [134,] 6.3000000 2.8000000 5.1000000 1.5000000
     [135,] 6.1000000 2.6000000 5.6000000 1.4000000
     [136,] 7.7000000 3.0000000 6.1000000 2.3000000
     [137,] 6.3000000 3.4000000 5.6000000 2.4000000
     [138,] 6.4000000 3.1000000 5.5000000 1.8000000
     [139,] 6.0000000 3.0000000 4.8000000 1.8000000
     [140,] 6.9000000 3.1000000 5.4000000 2.1000000
     [141,] 6.7000000 3.1000000 5.6000000 2.4000000
     [142,] 6.9000000 3.1000000 5.1000000 2.3000000
     [143,] 5.8000000 2.7000000 5.1000000 1.9000000
     [144,] 6.8000000 3.2000000 5.9000000 2.3000000
     [145,] 6.7000000 3.3000000 4.9110918 2.5000000
     [146,] 6.7000000 3.0000000 5.2000000 2.3000000
     [147,] 6.3000000 2.5000000 5.0000000 1.9000000
     [148,] 6.5000000 3.0000000 5.2000000 2.0000000
     [149,] 6.2000000 3.4000000 5.4618991 2.3000000
     [150,] 5.9000000 3.0000000 5.1000000 1.8000000
     * missing =
     row col
     [1,] 61 3
     [2,] 96 3
     [3,] 145 3
     [4,] 149 3
     [5,] 26 4
     * nbSample = 150
     * nbCluster = 3
     * lnLikelihood = -1035.894
     * nbFreeParameter= 70
     * criterion name = ICL
     * criterion value= 2429.652
     * 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 = 5.006000 3.428000 1.462000 0.228795
     * S.D. = 0.3887947 0.3887947 0.3887947 0.3887947
     ****************************************
     *** Cluster: 2
     * Proportion = 0.3333333
     * Means = 5.936000 2.770000 4.250908 1.326000
     * S.D. = 0.3887947 0.3887947 0.3887947 0.3887947
     ****************************************
     *** Cluster: 3
     * Proportion = 0.3333333
     * Means = 6.58800 2.97400 5.53746 2.02600
     * S.D. = 0.3887947 0.3887947 0.3887947 0.3887947
     ****************************************
     > 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,] 5.1000000 3.5000000 1.4000000 0.2000000
     [2,] 4.9000000 3.0000000 1.4000000 0.2000000
     [3,] 4.7000000 3.2000000 1.3000000 0.2000000
     [4,] 4.6000000 3.1000000 1.5000000 0.2000000
     [5,] 5.0000000 3.6000000 1.4000000 0.2000000
     [6,] 5.4000000 3.9000000 1.7000000 0.4000000
     [7,] 4.6000000 3.4000000 1.4000000 0.3000000
     [8,] 5.0000000 3.4000000 1.5000000 0.2000000
     [9,] 4.4000000 2.9000000 1.4000000 0.2000000
     [10,] 4.9000000 3.1000000 1.5000000 0.1000000
     [11,] 5.4000000 3.7000000 1.5000000 0.2000000
     [12,] 4.8000000 3.4000000 1.6000000 0.2000000
     [13,] 4.8000000 3.0000000 1.4000000 0.1000000
     [14,] 4.3000000 3.0000000 1.1000000 0.1000000
     [15,] 5.8000000 4.0000000 1.2000000 0.2000000
     [16,] 5.7000000 4.4000000 1.5000000 0.4000000
     [17,] 5.4000000 3.9000000 1.3000000 0.4000000
     [18,] 5.1000000 3.5000000 1.4000000 0.3000000
     [19,] 5.7000000 3.8000000 1.7000000 0.3000000
     [20,] 5.1000000 3.8000000 1.5000000 0.3000000
     [21,] 5.4000000 3.4000000 1.7000000 0.2000000
     [22,] 5.1000000 3.7000000 1.5000000 0.4000000
     [23,] 4.6000000 3.6000000 1.0000000 0.2000000
     [24,] 5.1000000 3.3000000 1.7000000 0.5000000
     [25,] 4.8000000 3.4000000 1.9000000 0.2000000
     [26,] 5.0000000 3.0000000 1.6000000 -0.6602505
     [27,] 5.0000000 3.4000000 1.6000000 0.4000000
     [28,] 5.2000000 3.5000000 1.5000000 0.2000000
     [29,] 5.2000000 3.4000000 1.4000000 0.2000000
     [30,] 4.7000000 3.2000000 1.6000000 0.2000000
     [31,] 4.8000000 3.1000000 1.6000000 0.2000000
     [32,] 5.4000000 3.4000000 1.5000000 0.4000000
     [33,] 5.2000000 4.1000000 1.5000000 0.1000000
     [34,] 5.5000000 4.2000000 1.4000000 0.2000000
     [35,] 4.9000000 3.1000000 1.5000000 0.2000000
     [36,] 5.0000000 3.2000000 1.2000000 0.2000000
     [37,] 5.5000000 3.5000000 1.3000000 0.2000000
     [38,] 4.9000000 3.6000000 1.4000000 0.1000000
     [39,] 4.4000000 3.0000000 1.3000000 0.2000000
     [40,] 5.1000000 3.4000000 1.5000000 0.2000000
     [41,] 5.0000000 3.5000000 1.3000000 0.3000000
     [42,] 4.5000000 2.3000000 1.3000000 0.3000000
     [43,] 4.4000000 3.2000000 1.3000000 0.2000000
     [44,] 5.0000000 3.5000000 1.6000000 0.6000000
     [45,] 5.1000000 3.8000000 1.9000000 0.4000000
     [46,] 4.8000000 3.0000000 1.4000000 0.3000000
     [47,] 5.1000000 3.8000000 1.6000000 0.2000000
     [48,] 4.6000000 3.2000000 1.4000000 0.2000000
     [49,] 5.3000000 3.7000000 1.5000000 0.2000000
     [50,] 5.0000000 3.3000000 1.4000000 0.2000000
     [51,] 7.0000000 3.2000000 4.7000000 1.4000000
     [52,] 6.4000000 3.2000000 4.5000000 1.5000000
     [53,] 6.9000000 3.1000000 4.9000000 1.5000000
     [54,] 5.5000000 2.3000000 4.0000000 1.3000000
     [55,] 6.5000000 2.8000000 4.6000000 1.5000000
     [56,] 5.7000000 2.8000000 4.5000000 1.3000000
     [57,] 6.3000000 3.3000000 4.7000000 1.6000000
     [58,] 4.9000000 2.4000000 3.3000000 1.0000000
     [59,] 6.6000000 2.9000000 4.6000000 1.3000000
     [60,] 5.2000000 2.7000000 3.9000000 1.4000000
     [61,] 5.0000000 2.0000000 3.6716278 1.0000000
     [62,] 5.9000000 3.0000000 4.2000000 1.5000000
     [63,] 6.0000000 2.2000000 4.0000000 1.0000000
     [64,] 6.1000000 2.9000000 4.7000000 1.4000000
     [65,] 5.6000000 2.9000000 3.6000000 1.3000000
     [66,] 6.7000000 3.1000000 4.4000000 1.4000000
     [67,] 5.6000000 3.0000000 4.5000000 1.5000000
     [68,] 5.8000000 2.7000000 4.1000000 1.0000000
     [69,] 6.2000000 2.2000000 4.5000000 1.5000000
     [70,] 5.6000000 2.5000000 3.9000000 1.1000000
     [71,] 5.9000000 3.2000000 4.8000000 1.8000000
     [72,] 6.1000000 2.8000000 4.0000000 1.3000000
     [73,] 6.3000000 2.5000000 4.9000000 1.5000000
     [74,] 6.1000000 2.8000000 4.7000000 1.2000000
     [75,] 6.4000000 2.9000000 4.3000000 1.3000000
     [76,] 6.6000000 3.0000000 4.4000000 1.4000000
     [77,] 6.8000000 2.8000000 4.8000000 1.4000000
     [78,] 6.7000000 3.0000000 5.0000000 1.7000000
     [79,] 6.0000000 2.9000000 4.5000000 1.5000000
     [80,] 5.7000000 2.6000000 3.5000000 1.0000000
     [81,] 5.5000000 2.4000000 3.8000000 1.1000000
     [82,] 5.5000000 2.4000000 3.7000000 1.0000000
     [83,] 5.8000000 2.7000000 3.9000000 1.2000000
     [84,] 6.0000000 2.7000000 5.1000000 1.6000000
     [85,] 5.4000000 3.0000000 4.5000000 1.5000000
     [86,] 6.0000000 3.4000000 4.5000000 1.6000000
     [87,] 6.7000000 3.1000000 4.7000000 1.5000000
     [88,] 6.3000000 2.3000000 4.4000000 1.3000000
     [89,] 5.6000000 3.0000000 4.1000000 1.3000000
     [90,] 5.5000000 2.5000000 4.0000000 1.3000000
     [91,] 5.5000000 2.6000000 4.4000000 1.2000000
     [92,] 6.1000000 3.0000000 4.6000000 1.4000000
     [93,] 5.8000000 2.6000000 4.0000000 1.2000000
     [94,] 5.0000000 2.3000000 3.3000000 1.0000000
     [95,] 5.6000000 2.7000000 4.2000000 1.3000000
     [96,] 5.7000000 3.0000000 3.5737922 1.2000000
     [97,] 5.7000000 2.9000000 4.2000000 1.3000000
     [98,] 6.2000000 2.9000000 4.3000000 1.3000000
     [99,] 5.1000000 2.5000000 3.0000000 1.1000000
     [100,] 5.7000000 2.8000000 4.1000000 1.3000000
     [101,] 6.3000000 3.3000000 6.0000000 2.5000000
     [102,] 5.8000000 2.7000000 5.1000000 1.9000000
     [103,] 7.1000000 3.0000000 5.9000000 2.1000000
     [104,] 6.3000000 2.9000000 5.6000000 1.8000000
     [105,] 6.5000000 3.0000000 5.8000000 2.2000000
     [106,] 7.6000000 3.0000000 6.6000000 2.1000000
     [107,] 4.9000000 2.5000000 4.5000000 1.7000000
     [108,] 7.3000000 2.9000000 6.3000000 1.8000000
     [109,] 6.7000000 2.5000000 5.8000000 1.8000000
     [110,] 7.2000000 3.6000000 6.1000000 2.5000000
     [111,] 6.5000000 3.2000000 5.1000000 2.0000000
     [112,] 6.4000000 2.7000000 5.3000000 1.9000000
     [113,] 6.8000000 3.0000000 5.5000000 2.1000000
     [114,] 5.7000000 2.5000000 5.0000000 2.0000000
     [115,] 5.8000000 2.8000000 5.1000000 2.4000000
     [116,] 6.4000000 3.2000000 5.3000000 2.3000000
     [117,] 6.5000000 3.0000000 5.5000000 1.8000000
     [118,] 7.7000000 3.8000000 6.7000000 2.2000000
     [119,] 7.7000000 2.6000000 6.9000000 2.3000000
     [120,] 6.0000000 2.2000000 5.0000000 1.5000000
     [121,] 6.9000000 3.2000000 5.7000000 2.3000000
     [122,] 5.6000000 2.8000000 4.9000000 2.0000000
     [123,] 7.7000000 2.8000000 6.7000000 2.0000000
     [124,] 6.3000000 2.7000000 4.9000000 1.8000000
     [125,] 6.7000000 3.3000000 5.7000000 2.1000000
     [126,] 7.2000000 3.2000000 6.0000000 1.8000000
     [127,] 6.2000000 2.8000000 4.8000000 1.8000000
     [128,] 6.1000000 3.0000000 4.9000000 1.8000000
     [129,] 6.4000000 2.8000000 5.6000000 2.1000000
     [130,] 7.2000000 3.0000000 5.8000000 1.6000000
     [131,] 7.4000000 2.8000000 6.1000000 1.9000000
     [132,] 7.9000000 3.8000000 6.4000000 2.0000000
     [133,] 6.4000000 2.8000000 5.6000000 2.2000000
     [134,] 6.3000000 2.8000000 5.1000000 1.5000000
     [135,] 6.1000000 2.6000000 5.6000000 1.4000000
     [136,] 7.7000000 3.0000000 6.1000000 2.3000000
     [137,] 6.3000000 3.4000000 5.6000000 2.4000000
     [138,] 6.4000000 3.1000000 5.5000000 1.8000000
     [139,] 6.0000000 3.0000000 4.8000000 1.8000000
     [140,] 6.9000000 3.1000000 5.4000000 2.1000000
     [141,] 6.7000000 3.1000000 5.6000000 2.4000000
     [142,] 6.9000000 3.1000000 5.1000000 2.3000000
     [143,] 5.8000000 2.7000000 5.1000000 1.9000000
     [144,] 6.8000000 3.2000000 5.9000000 2.3000000
     [145,] 6.7000000 3.3000000 4.9110918 2.5000000
     [146,] 6.7000000 3.0000000 5.2000000 2.3000000
     [147,] 6.3000000 2.5000000 5.0000000 1.9000000
     [148,] 6.5000000 3.0000000 5.2000000 2.0000000
     [149,] 6.2000000 3.4000000 5.4618991 2.3000000
     [150,] 5.9000000 3.0000000 5.1000000 1.8000000
     * missing =
     row col
     * nbSample = 150
     * nbCluster = 3
     * lnLikelihood = -1070.76
     * nbFreeParameter= 70
     * criterion name = ICL
     * criterion value= 2500.053
     * 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 = 5.006000 3.428000 1.462000 0.228795
     * S.D. = 0.3489470 0.3752546 0.1719186 0.1642299
     ****************************************
     *** Cluster: 2
     * Proportion = 0.3333333
     * Means = 5.936000 2.770000 4.250908 1.326000
     * S.D. = 0.5109834 0.3106445 0.4701432 0.1957652
     ****************************************
     *** Cluster: 3
     * Proportion = 0.3333333
     * Means = 6.58800 2.97400 5.53746 2.02600
     * S.D. = 0.6294887 0.3192554 0.5529576 0.2718897
     ****************************************
     > model <- learnGamma( data=x, labels= z,
     + , models = clusterGammaNames(prop = "equal")
     + , algo = "simul", nbIter = 2, epsilon = 1e-08
     + )
    
     *** caught segfault ***
     address 0x120, cause 'memory not mapped'
    
     Traceback:
     1: learnGamma(data = x, labels = z, , models = clusterGammaNames(prop = "equal"), algo = "simul", nbIter = 2, epsilon = 1e-08)
     An irrecoverable exception occurred. R is aborting now ...
     Segmentation fault
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 1.5.1
Check: installed package size
Result: NOTE
     installed size is 40.6Mb
     sub-directories of 1Mb or more:
     libs 38.3Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-windows-ix86+x86_64, r-release-macos-x86_64, r-release-windows-ix86+x86_64, r-oldrel-macos-x86_64, r-oldrel-windows-ix86+x86_64

Version: 1.5.1
Check: tests
Result: ERROR
     Running ‘ClusterSimul.R’
     Running ‘clusterDiagGaussianLikelihood.R’
     Running ‘clusterGammaLikelihood.R’
     Running ‘simulHeterogeneous.R’
     Running ‘simulNonLinear.R’
     Running ‘testAllLearners.R’
     Running ‘testPoissonExample.R’
     Running ‘testPredict.R’ [10s/12s]
    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,] 31 4 0.2
     [2,] 105 2 3.0
     [3,] 101 3 6.0
     [4,] 129 4 2.1
     [5,] 143 2 2.7
     > 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
     1 105 2 3.3429273
     2 143 2 2.3899493
     3 101 3 4.9437216
     4 31 4 -0.0605292
     5 129 4 1.6524928
     > print(model)
     ****************************************
     * model name = gaussian_p_sj
     * data =
     Sepal.Length Sepal.Width Petal.Length Petal.Width
     [1,] 5.1000000 3.5000000 1.4000000 0.2000000
     [2,] 4.9000000 3.0000000 1.4000000 0.2000000
     [3,] 4.7000000 3.2000000 1.3000000 0.2000000
     [4,] 4.6000000 3.1000000 1.5000000 0.2000000
     [5,] 5.0000000 3.6000000 1.4000000 0.2000000
     [6,] 5.4000000 3.9000000 1.7000000 0.4000000
     [7,] 4.6000000 3.4000000 1.4000000 0.3000000
     [8,] 5.0000000 3.4000000 1.5000000 0.2000000
     [9,] 4.4000000 2.9000000 1.4000000 0.2000000
     [10,] 4.9000000 3.1000000 1.5000000 0.1000000
     [11,] 5.4000000 3.7000000 1.5000000 0.2000000
     [12,] 4.8000000 3.4000000 1.6000000 0.2000000
     [13,] 4.8000000 3.0000000 1.4000000 0.1000000
     [14,] 4.3000000 3.0000000 1.1000000 0.1000000
     [15,] 5.8000000 4.0000000 1.2000000 0.2000000
     [16,] 5.7000000 4.4000000 1.5000000 0.4000000
     [17,] 5.4000000 3.9000000 1.3000000 0.4000000
     [18,] 5.1000000 3.5000000 1.4000000 0.3000000
     [19,] 5.7000000 3.8000000 1.7000000 0.3000000
     [20,] 5.1000000 3.8000000 1.5000000 0.3000000
     [21,] 5.4000000 3.4000000 1.7000000 0.2000000
     [22,] 5.1000000 3.7000000 1.5000000 0.4000000
     [23,] 4.6000000 3.6000000 1.0000000 0.2000000
     [24,] 5.1000000 3.3000000 1.7000000 0.5000000
     [25,] 4.8000000 3.4000000 1.9000000 0.2000000
     [26,] 5.0000000 3.0000000 1.6000000 0.2000000
     [27,] 5.0000000 3.4000000 1.6000000 0.4000000
     [28,] 5.2000000 3.5000000 1.5000000 0.2000000
     [29,] 5.2000000 3.4000000 1.4000000 0.2000000
     [30,] 4.7000000 3.2000000 1.6000000 0.2000000
     [31,] 4.8000000 3.1000000 1.6000000 -0.0605292
     [32,] 5.4000000 3.4000000 1.5000000 0.4000000
     [33,] 5.2000000 4.1000000 1.5000000 0.1000000
     [34,] 5.5000000 4.2000000 1.4000000 0.2000000
     [35,] 4.9000000 3.1000000 1.5000000 0.2000000
     [36,] 5.0000000 3.2000000 1.2000000 0.2000000
     [37,] 5.5000000 3.5000000 1.3000000 0.2000000
     [38,] 4.9000000 3.6000000 1.4000000 0.1000000
     [39,] 4.4000000 3.0000000 1.3000000 0.2000000
     [40,] 5.1000000 3.4000000 1.5000000 0.2000000
     [41,] 5.0000000 3.5000000 1.3000000 0.3000000
     [42,] 4.5000000 2.3000000 1.3000000 0.3000000
     [43,] 4.4000000 3.2000000 1.3000000 0.2000000
     [44,] 5.0000000 3.5000000 1.6000000 0.6000000
     [45,] 5.1000000 3.8000000 1.9000000 0.4000000
     [46,] 4.8000000 3.0000000 1.4000000 0.3000000
     [47,] 5.1000000 3.8000000 1.6000000 0.2000000
     [48,] 4.6000000 3.2000000 1.4000000 0.2000000
     [49,] 5.3000000 3.7000000 1.5000000 0.2000000
     [50,] 5.0000000 3.3000000 1.4000000 0.2000000
     [51,] 7.0000000 3.2000000 4.7000000 1.4000000
     [52,] 6.4000000 3.2000000 4.5000000 1.5000000
     [53,] 6.9000000 3.1000000 4.9000000 1.5000000
     [54,] 5.5000000 2.3000000 4.0000000 1.3000000
     [55,] 6.5000000 2.8000000 4.6000000 1.5000000
     [56,] 5.7000000 2.8000000 4.5000000 1.3000000
     [57,] 6.3000000 3.3000000 4.7000000 1.6000000
     [58,] 4.9000000 2.4000000 3.3000000 1.0000000
     [59,] 6.6000000 2.9000000 4.6000000 1.3000000
     [60,] 5.2000000 2.7000000 3.9000000 1.4000000
     [61,] 5.0000000 2.0000000 3.5000000 1.0000000
     [62,] 5.9000000 3.0000000 4.2000000 1.5000000
     [63,] 6.0000000 2.2000000 4.0000000 1.0000000
     [64,] 6.1000000 2.9000000 4.7000000 1.4000000
     [65,] 5.6000000 2.9000000 3.6000000 1.3000000
     [66,] 6.7000000 3.1000000 4.4000000 1.4000000
     [67,] 5.6000000 3.0000000 4.5000000 1.5000000
     [68,] 5.8000000 2.7000000 4.1000000 1.0000000
     [69,] 6.2000000 2.2000000 4.5000000 1.5000000
     [70,] 5.6000000 2.5000000 3.9000000 1.1000000
     [71,] 5.9000000 3.2000000 4.8000000 1.8000000
     [72,] 6.1000000 2.8000000 4.0000000 1.3000000
     [73,] 6.3000000 2.5000000 4.9000000 1.5000000
     [74,] 6.1000000 2.8000000 4.7000000 1.2000000
     [75,] 6.4000000 2.9000000 4.3000000 1.3000000
     [76,] 6.6000000 3.0000000 4.4000000 1.4000000
     [77,] 6.8000000 2.8000000 4.8000000 1.4000000
     [78,] 6.7000000 3.0000000 5.0000000 1.7000000
     [79,] 6.0000000 2.9000000 4.5000000 1.5000000
     [80,] 5.7000000 2.6000000 3.5000000 1.0000000
     [81,] 5.5000000 2.4000000 3.8000000 1.1000000
     [82,] 5.5000000 2.4000000 3.7000000 1.0000000
     [83,] 5.8000000 2.7000000 3.9000000 1.2000000
     [84,] 6.0000000 2.7000000 5.1000000 1.6000000
     [85,] 5.4000000 3.0000000 4.5000000 1.5000000
     [86,] 6.0000000 3.4000000 4.5000000 1.6000000
     [87,] 6.7000000 3.1000000 4.7000000 1.5000000
     [88,] 6.3000000 2.3000000 4.4000000 1.3000000
     [89,] 5.6000000 3.0000000 4.1000000 1.3000000
     [90,] 5.5000000 2.5000000 4.0000000 1.3000000
     [91,] 5.5000000 2.6000000 4.4000000 1.2000000
     [92,] 6.1000000 3.0000000 4.6000000 1.4000000
     [93,] 5.8000000 2.6000000 4.0000000 1.2000000
     [94,] 5.0000000 2.3000000 3.3000000 1.0000000
     [95,] 5.6000000 2.7000000 4.2000000 1.3000000
     [96,] 5.7000000 3.0000000 4.2000000 1.2000000
     [97,] 5.7000000 2.9000000 4.2000000 1.3000000
     [98,] 6.2000000 2.9000000 4.3000000 1.3000000
     [99,] 5.1000000 2.5000000 3.0000000 1.1000000
     [100,] 5.7000000 2.8000000 4.1000000 1.3000000
     [101,] 6.3000000 3.3000000 4.9437216 2.5000000
     [102,] 5.8000000 2.7000000 5.1000000 1.9000000
     [103,] 7.1000000 3.0000000 5.9000000 2.1000000
     [104,] 6.3000000 2.9000000 5.6000000 1.8000000
     [105,] 6.5000000 3.3429273 5.8000000 2.2000000
     [106,] 7.6000000 3.0000000 6.6000000 2.1000000
     [107,] 4.9000000 2.5000000 4.5000000 1.7000000
     [108,] 7.3000000 2.9000000 6.3000000 1.8000000
     [109,] 6.7000000 2.5000000 5.8000000 1.8000000
     [110,] 7.2000000 3.6000000 6.1000000 2.5000000
     [111,] 6.5000000 3.2000000 5.1000000 2.0000000
     [112,] 6.4000000 2.7000000 5.3000000 1.9000000
     [113,] 6.8000000 3.0000000 5.5000000 2.1000000
     [114,] 5.7000000 2.5000000 5.0000000 2.0000000
     [115,] 5.8000000 2.8000000 5.1000000 2.4000000
     [116,] 6.4000000 3.2000000 5.3000000 2.3000000
     [117,] 6.5000000 3.0000000 5.5000000 1.8000000
     [118,] 7.7000000 3.8000000 6.7000000 2.2000000
     [119,] 7.7000000 2.6000000 6.9000000 2.3000000
     [120,] 6.0000000 2.2000000 5.0000000 1.5000000
     [121,] 6.9000000 3.2000000 5.7000000 2.3000000
     [122,] 5.6000000 2.8000000 4.9000000 2.0000000
     [123,] 7.7000000 2.8000000 6.7000000 2.0000000
     [124,] 6.3000000 2.7000000 4.9000000 1.8000000
     [125,] 6.7000000 3.3000000 5.7000000 2.1000000
     [126,] 7.2000000 3.2000000 6.0000000 1.8000000
     [127,] 6.2000000 2.8000000 4.8000000 1.8000000
     [128,] 6.1000000 3.0000000 4.9000000 1.8000000
     [129,] 6.4000000 2.8000000 5.6000000 1.6524928
     [130,] 7.2000000 3.0000000 5.8000000 1.6000000
     [131,] 7.4000000 2.8000000 6.1000000 1.9000000
     [132,] 7.9000000 3.8000000 6.4000000 2.0000000
     [133,] 6.4000000 2.8000000 5.6000000 2.2000000
     [134,] 6.3000000 2.8000000 5.1000000 1.5000000
     [135,] 6.1000000 2.6000000 5.6000000 1.4000000
     [136,] 7.7000000 3.0000000 6.1000000 2.3000000
     [137,] 6.3000000 3.4000000 5.6000000 2.4000000
     [138,] 6.4000000 3.1000000 5.5000000 1.8000000
     [139,] 6.0000000 3.0000000 4.8000000 1.8000000
     [140,] 6.9000000 3.1000000 5.4000000 2.1000000
     [141,] 6.7000000 3.1000000 5.6000000 2.4000000
     [142,] 6.9000000 3.1000000 5.1000000 2.3000000
     [143,] 5.8000000 2.3899493 5.1000000 1.9000000
     [144,] 6.8000000 3.2000000 5.9000000 2.3000000
     [145,] 6.7000000 3.3000000 5.7000000 2.5000000
     [146,] 6.7000000 3.0000000 5.2000000 2.3000000
     [147,] 6.3000000 2.5000000 5.0000000 1.9000000
     [148,] 6.5000000 3.0000000 5.2000000 2.0000000
     [149,] 6.2000000 3.4000000 5.4000000 2.3000000
     [150,] 5.9000000 3.0000000 5.1000000 1.8000000
     * missing =
     row col
     [1,] 105 2
     [2,] 143 2
     [3,] 101 3
     [4,] 31 4
     [5,] 129 4
     * nbSample = 150
     * nbCluster = 3
     * lnLikelihood = -1036.574
     * nbFreeParameter= 70
     * criterion name = ICL
     * criterion value= 2431.382
     * 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 0.2407894
     * S.D. = 0.5096155 0.3402298 0.4271579 0.2061894
     ****************************************
     *** Cluster: 2
     * Proportion = 0.3333333
     * Means = 5.936 2.770 4.260 1.326
     * S.D. = 0.5096155 0.3402298 0.4271579 0.2061894
     ****************************************
     *** Cluster: 3
     * Proportion = 0.3333333
     * Means = 6.588000 2.974658 5.530874 2.017050
     * S.D. = 0.5096155 0.3402298 0.4271579 0.2061894
     ****************************************
     > 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,] 5.1000000 3.5000000 1.4000000 0.2000000
     [2,] 4.9000000 3.0000000 1.4000000 0.2000000
     [3,] 4.7000000 3.2000000 1.3000000 0.2000000
     [4,] 4.6000000 3.1000000 1.5000000 0.2000000
     [5,] 5.0000000 3.6000000 1.4000000 0.2000000
     [6,] 5.4000000 3.9000000 1.7000000 0.4000000
     [7,] 4.6000000 3.4000000 1.4000000 0.3000000
     [8,] 5.0000000 3.4000000 1.5000000 0.2000000
     [9,] 4.4000000 2.9000000 1.4000000 0.2000000
     [10,] 4.9000000 3.1000000 1.5000000 0.1000000
     [11,] 5.4000000 3.7000000 1.5000000 0.2000000
     [12,] 4.8000000 3.4000000 1.6000000 0.2000000
     [13,] 4.8000000 3.0000000 1.4000000 0.1000000
     [14,] 4.3000000 3.0000000 1.1000000 0.1000000
     [15,] 5.8000000 4.0000000 1.2000000 0.2000000
     [16,] 5.7000000 4.4000000 1.5000000 0.4000000
     [17,] 5.4000000 3.9000000 1.3000000 0.4000000
     [18,] 5.1000000 3.5000000 1.4000000 0.3000000
     [19,] 5.7000000 3.8000000 1.7000000 0.3000000
     [20,] 5.1000000 3.8000000 1.5000000 0.3000000
     [21,] 5.4000000 3.4000000 1.7000000 0.2000000
     [22,] 5.1000000 3.7000000 1.5000000 0.4000000
     [23,] 4.6000000 3.6000000 1.0000000 0.2000000
     [24,] 5.1000000 3.3000000 1.7000000 0.5000000
     [25,] 4.8000000 3.4000000 1.9000000 0.2000000
     [26,] 5.0000000 3.0000000 1.6000000 0.2000000
     [27,] 5.0000000 3.4000000 1.6000000 0.4000000
     [28,] 5.2000000 3.5000000 1.5000000 0.2000000
     [29,] 5.2000000 3.4000000 1.4000000 0.2000000
     [30,] 4.7000000 3.2000000 1.6000000 0.2000000
     [31,] 4.8000000 3.1000000 1.6000000 -0.0605292
     [32,] 5.4000000 3.4000000 1.5000000 0.4000000
     [33,] 5.2000000 4.1000000 1.5000000 0.1000000
     [34,] 5.5000000 4.2000000 1.4000000 0.2000000
     [35,] 4.9000000 3.1000000 1.5000000 0.2000000
     [36,] 5.0000000 3.2000000 1.2000000 0.2000000
     [37,] 5.5000000 3.5000000 1.3000000 0.2000000
     [38,] 4.9000000 3.6000000 1.4000000 0.1000000
     [39,] 4.4000000 3.0000000 1.3000000 0.2000000
     [40,] 5.1000000 3.4000000 1.5000000 0.2000000
     [41,] 5.0000000 3.5000000 1.3000000 0.3000000
     [42,] 4.5000000 2.3000000 1.3000000 0.3000000
     [43,] 4.4000000 3.2000000 1.3000000 0.2000000
     [44,] 5.0000000 3.5000000 1.6000000 0.6000000
     [45,] 5.1000000 3.8000000 1.9000000 0.4000000
     [46,] 4.8000000 3.0000000 1.4000000 0.3000000
     [47,] 5.1000000 3.8000000 1.6000000 0.2000000
     [48,] 4.6000000 3.2000000 1.4000000 0.2000000
     [49,] 5.3000000 3.7000000 1.5000000 0.2000000
     [50,] 5.0000000 3.3000000 1.4000000 0.2000000
     [51,] 7.0000000 3.2000000 4.7000000 1.4000000
     [52,] 6.4000000 3.2000000 4.5000000 1.5000000
     [53,] 6.9000000 3.1000000 4.9000000 1.5000000
     [54,] 5.5000000 2.3000000 4.0000000 1.3000000
     [55,] 6.5000000 2.8000000 4.6000000 1.5000000
     [56,] 5.7000000 2.8000000 4.5000000 1.3000000
     [57,] 6.3000000 3.3000000 4.7000000 1.6000000
     [58,] 4.9000000 2.4000000 3.3000000 1.0000000
     [59,] 6.6000000 2.9000000 4.6000000 1.3000000
     [60,] 5.2000000 2.7000000 3.9000000 1.4000000
     [61,] 5.0000000 2.0000000 3.5000000 1.0000000
     [62,] 5.9000000 3.0000000 4.2000000 1.5000000
     [63,] 6.0000000 2.2000000 4.0000000 1.0000000
     [64,] 6.1000000 2.9000000 4.7000000 1.4000000
     [65,] 5.6000000 2.9000000 3.6000000 1.3000000
     [66,] 6.7000000 3.1000000 4.4000000 1.4000000
     [67,] 5.6000000 3.0000000 4.5000000 1.5000000
     [68,] 5.8000000 2.7000000 4.1000000 1.0000000
     [69,] 6.2000000 2.2000000 4.5000000 1.5000000
     [70,] 5.6000000 2.5000000 3.9000000 1.1000000
     [71,] 5.9000000 3.2000000 4.8000000 1.8000000
     [72,] 6.1000000 2.8000000 4.0000000 1.3000000
     [73,] 6.3000000 2.5000000 4.9000000 1.5000000
     [74,] 6.1000000 2.8000000 4.7000000 1.2000000
     [75,] 6.4000000 2.9000000 4.3000000 1.3000000
     [76,] 6.6000000 3.0000000 4.4000000 1.4000000
     [77,] 6.8000000 2.8000000 4.8000000 1.4000000
     [78,] 6.7000000 3.0000000 5.0000000 1.7000000
     [79,] 6.0000000 2.9000000 4.5000000 1.5000000
     [80,] 5.7000000 2.6000000 3.5000000 1.0000000
     [81,] 5.5000000 2.4000000 3.8000000 1.1000000
     [82,] 5.5000000 2.4000000 3.7000000 1.0000000
     [83,] 5.8000000 2.7000000 3.9000000 1.2000000
     [84,] 6.0000000 2.7000000 5.1000000 1.6000000
     [85,] 5.4000000 3.0000000 4.5000000 1.5000000
     [86,] 6.0000000 3.4000000 4.5000000 1.6000000
     [87,] 6.7000000 3.1000000 4.7000000 1.5000000
     [88,] 6.3000000 2.3000000 4.4000000 1.3000000
     [89,] 5.6000000 3.0000000 4.1000000 1.3000000
     [90,] 5.5000000 2.5000000 4.0000000 1.3000000
     [91,] 5.5000000 2.6000000 4.4000000 1.2000000
     [92,] 6.1000000 3.0000000 4.6000000 1.4000000
     [93,] 5.8000000 2.6000000 4.0000000 1.2000000
     [94,] 5.0000000 2.3000000 3.3000000 1.0000000
     [95,] 5.6000000 2.7000000 4.2000000 1.3000000
     [96,] 5.7000000 3.0000000 4.2000000 1.2000000
     [97,] 5.7000000 2.9000000 4.2000000 1.3000000
     [98,] 6.2000000 2.9000000 4.3000000 1.3000000
     [99,] 5.1000000 2.5000000 3.0000000 1.1000000
     [100,] 5.7000000 2.8000000 4.1000000 1.3000000
     [101,] 6.3000000 3.3000000 4.9437216 2.5000000
     [102,] 5.8000000 2.7000000 5.1000000 1.9000000
     [103,] 7.1000000 3.0000000 5.9000000 2.1000000
     [104,] 6.3000000 2.9000000 5.6000000 1.8000000
     [105,] 6.5000000 3.3429273 5.8000000 2.2000000
     [106,] 7.6000000 3.0000000 6.6000000 2.1000000
     [107,] 4.9000000 2.5000000 4.5000000 1.7000000
     [108,] 7.3000000 2.9000000 6.3000000 1.8000000
     [109,] 6.7000000 2.5000000 5.8000000 1.8000000
     [110,] 7.2000000 3.6000000 6.1000000 2.5000000
     [111,] 6.5000000 3.2000000 5.1000000 2.0000000
     [112,] 6.4000000 2.7000000 5.3000000 1.9000000
     [113,] 6.8000000 3.0000000 5.5000000 2.1000000
     [114,] 5.7000000 2.5000000 5.0000000 2.0000000
     [115,] 5.8000000 2.8000000 5.1000000 2.4000000
     [116,] 6.4000000 3.2000000 5.3000000 2.3000000
     [117,] 6.5000000 3.0000000 5.5000000 1.8000000
     [118,] 7.7000000 3.8000000 6.7000000 2.2000000
     [119,] 7.7000000 2.6000000 6.9000000 2.3000000
     [120,] 6.0000000 2.2000000 5.0000000 1.5000000
     [121,] 6.9000000 3.2000000 5.7000000 2.3000000
     [122,] 5.6000000 2.8000000 4.9000000 2.0000000
     [123,] 7.7000000 2.8000000 6.7000000 2.0000000
     [124,] 6.3000000 2.7000000 4.9000000 1.8000000
     [125,] 6.7000000 3.3000000 5.7000000 2.1000000
     [126,] 7.2000000 3.2000000 6.0000000 1.8000000
     [127,] 6.2000000 2.8000000 4.8000000 1.8000000
     [128,] 6.1000000 3.0000000 4.9000000 1.8000000
     [129,] 6.4000000 2.8000000 5.6000000 1.6524928
     [130,] 7.2000000 3.0000000 5.8000000 1.6000000
     [131,] 7.4000000 2.8000000 6.1000000 1.9000000
     [132,] 7.9000000 3.8000000 6.4000000 2.0000000
     [133,] 6.4000000 2.8000000 5.6000000 2.2000000
     [134,] 6.3000000 2.8000000 5.1000000 1.5000000
     [135,] 6.1000000 2.6000000 5.6000000 1.4000000
     [136,] 7.7000000 3.0000000 6.1000000 2.3000000
     [137,] 6.3000000 3.4000000 5.6000000 2.4000000
     [138,] 6.4000000 3.1000000 5.5000000 1.8000000
     [139,] 6.0000000 3.0000000 4.8000000 1.8000000
     [140,] 6.9000000 3.1000000 5.4000000 2.1000000
     [141,] 6.7000000 3.1000000 5.6000000 2.4000000
     [142,] 6.9000000 3.1000000 5.1000000 2.3000000
     [143,] 5.8000000 2.3899493 5.1000000 1.9000000
     [144,] 6.8000000 3.2000000 5.9000000 2.3000000
     [145,] 6.7000000 3.3000000 5.7000000 2.5000000
     [146,] 6.7000000 3.0000000 5.2000000 2.3000000
     [147,] 6.3000000 2.5000000 5.0000000 1.9000000
     [148,] 6.5000000 3.0000000 5.2000000 2.0000000
     [149,] 6.2000000 3.4000000 5.4000000 2.3000000
     [150,] 5.9000000 3.0000000 5.1000000 1.8000000
     * missing =
     row col
     * nbSample = 150
     * nbCluster = 3
     * lnLikelihood = -1041.336
     * nbFreeParameter= 70
     * criterion name = ICL
     * criterion value= 2440.986
     * 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 0.2407894
     * S.D. = 0.3489470 0.3752546 0.1719186 0.1126665
     ****************************************
     *** Cluster: 2
     * Proportion = 0.3333333
     * Means = 5.936 2.770 4.260 1.326
     * S.D. = 0.5109834 0.3106445 0.4651881 0.1957652
     ****************************************
     *** Cluster: 3
     * Proportion = 0.3333333
     * Means = 6.588000 2.974658 5.530874 2.017050
     * S.D. = 0.6294887 0.3315916 0.5490316 0.2766307
     ****************************************
     > model <- learnGamma( data=x, labels= z,
     + , models = clusterGammaNames(prop = "equal")
     + , algo = "simul", nbIter = 2, epsilon = 1e-08
     + )
    
     *** caught segfault ***
     address 0x120, cause 'memory not mapped'
    
     Traceback:
     1: learnGamma(data = x, labels = z, , models = clusterGammaNames(prop = "equal"), algo = "simul", nbIter = 2, epsilon = 1e-08)
     An irrecoverable exception occurred. R is aborting now ...
Flavor: r-devel-linux-x86_64-fedora-gcc