CRAN Package Check Results for Package MixAll

Last updated on 2022-05-27 07:50:09 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.5.1 618.44 113.58 732.02 OK
r-devel-linux-x86_64-debian-gcc 1.5.1 580.21 86.11 666.32 OK
r-devel-linux-x86_64-fedora-clang 1.5.1 1211.38 NOTE
r-devel-linux-x86_64-fedora-gcc 1.5.1 1123.38 OK
r-devel-windows-x86_64 1.5.1 647.00 221.00 868.00 OK
r-patched-linux-x86_64 1.5.1 613.06 109.29 722.35 OK
r-release-linux-x86_64 1.5.1 604.86 109.04 713.90 ERROR
r-release-macos-arm64 1.5.1 248.00 NOTE
r-release-macos-x86_64 1.5.1 374.00 NOTE
r-release-windows-x86_64 1.5.1 702.00 224.00 926.00 OK
r-oldrel-macos-arm64 1.5.1 288.00 NOTE
r-oldrel-macos-x86_64 1.5.1 493.00 NOTE
r-oldrel-windows-ix86+x86_64 1.5.1 1698.00 231.00 1929.00 NOTE

Check Details

Version: 1.5.1
Check: installed package size
Result: NOTE
     installed size is 22.6Mb
     sub-directories of 1Mb or more:
     libs 20.3Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-release-macos-arm64, r-release-macos-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-ix86+x86_64

Version: 1.5.1
Check: tests
Result: ERROR
     Running ‘ClusterSimul.R’ [0s/1s]
     Running ‘clusterDiagGaussianLikelihood.R’ [1s/1s]
     Running ‘clusterGammaLikelihood.R’ [1s/1s]
     Running ‘simulHeterogeneous.R’ [0s/1s]
     Running ‘simulNonLinear.R’ [1s/1s]
     Running ‘testAllLearners.R’ [1s/1s]
     Running ‘testPoissonExample.R’ [1s/2s]
     Running ‘testPredict.R’ [10s/13s]
    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 4 1.3
     [2,] 147 1 6.3
     [3,] 23 4 0.2
     [4,] 103 2 3.0
     [5,] 41 2 3.5
     > 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
     1 147 1 6.15475641
     2 41 2 2.86469801
     3 103 2 2.93905576
     4 23 4 -0.03495172
     5 98 4 0.67595543
     > 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 0.20000000
     [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 0.20000000
     [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 -0.03495172
     [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 2.86469801 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 2.90000000 4.30000000 0.67595543
     [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 2.93905576 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 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,] 6.15475641 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,] 147 1
     [2,] 41 2
     [3,] 103 2
     [4,] 23 4
     [5,] 98 4
     * nbSample = 150
     * nbCluster = 3
     * lnLikelihood = -1035.946
     * nbFreeParameter= 70
     * criterion name = ICL
     * criterion value= 2429.885
     * 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 3.415294 1.462000 0.241301
     * S.D. = 0.38772 0.38772 0.38772 0.38772
     ****************************************
     *** Cluster: 2
     * Proportion = 0.3333333
     * Means = 5.936000 2.770000 4.260000 1.313519
     * S.D. = 0.38772 0.38772 0.38772 0.38772
     ****************************************
     *** Cluster: 3
     * Proportion = 0.3333333
     * Means = 6.585095 2.972781 5.552000 2.026000
     * S.D. = 0.38772 0.38772 0.38772 0.38772
     ****************************************
     > 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 0.20000000
     [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 -0.03495172
     [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 2.86469801 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 2.90000000 4.30000000 0.67595543
     [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 2.93905576 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 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,] 6.15475641 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 = -1048.242
     * nbFreeParameter= 70
     * criterion name = ICL
     * criterion value= 2454.99
     * 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 3.415294 1.462000 0.241301
     * S.D. = 0.3489470 0.3832715 0.1719186 0.1113475
     ****************************************
     *** Cluster: 2
     * Proportion = 0.3333333
     * Means = 5.936000 2.770000 4.260000 1.313519
     * S.D. = 0.5109834 0.3106445 0.4651881 0.2158839
     ****************************************
     *** Cluster: 3
     * Proportion = 0.3333333
     * Means = 6.585095 2.972781 5.552000 2.026000
     * S.D. = 0.6311439 0.3192701 0.5463479 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-release-linux-x86_64