R Under development (unstable) (2024-01-20 r85814 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > 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,] 53 2 3.1 [2,] 149 4 2.3 [3,] 145 2 3.3 [4,] 9 2 2.9 [5,] 24 1 5.1 > 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 24 1 5.669062 2 9 2 3.138302 3 53 2 2.938167 4 145 2 3.144924 5 149 4 1.937460 > print(model) **************************************** * model name = gaussian_p_sj * data = Sepal.Length Sepal.Width Petal.Length Petal.Width [1,] 5.100000 3.500000 1.400000 0.200000 [2,] 4.900000 3.000000 1.400000 0.200000 [3,] 4.700000 3.200000 1.300000 0.200000 [4,] 4.600000 3.100000 1.500000 0.200000 [5,] 5.000000 3.600000 1.400000 0.200000 [6,] 5.400000 3.900000 1.700000 0.400000 [7,] 4.600000 3.400000 1.400000 0.300000 [8,] 5.000000 3.400000 1.500000 0.200000 [9,] 4.400000 3.138302 1.400000 0.200000 [10,] 4.900000 3.100000 1.500000 0.100000 [11,] 5.400000 3.700000 1.500000 0.200000 [12,] 4.800000 3.400000 1.600000 0.200000 [13,] 4.800000 3.000000 1.400000 0.100000 [14,] 4.300000 3.000000 1.100000 0.100000 [15,] 5.800000 4.000000 1.200000 0.200000 [16,] 5.700000 4.400000 1.500000 0.400000 [17,] 5.400000 3.900000 1.300000 0.400000 [18,] 5.100000 3.500000 1.400000 0.300000 [19,] 5.700000 3.800000 1.700000 0.300000 [20,] 5.100000 3.800000 1.500000 0.300000 [21,] 5.400000 3.400000 1.700000 0.200000 [22,] 5.100000 3.700000 1.500000 0.400000 [23,] 4.600000 3.600000 1.000000 0.200000 [24,] 5.669062 3.300000 1.700000 0.500000 [25,] 4.800000 3.400000 1.900000 0.200000 [26,] 5.000000 3.000000 1.600000 0.200000 [27,] 5.000000 3.400000 1.600000 0.400000 [28,] 5.200000 3.500000 1.500000 0.200000 [29,] 5.200000 3.400000 1.400000 0.200000 [30,] 4.700000 3.200000 1.600000 0.200000 [31,] 4.800000 3.100000 1.600000 0.200000 [32,] 5.400000 3.400000 1.500000 0.400000 [33,] 5.200000 4.100000 1.500000 0.100000 [34,] 5.500000 4.200000 1.400000 0.200000 [35,] 4.900000 3.100000 1.500000 0.200000 [36,] 5.000000 3.200000 1.200000 0.200000 [37,] 5.500000 3.500000 1.300000 0.200000 [38,] 4.900000 3.600000 1.400000 0.100000 [39,] 4.400000 3.000000 1.300000 0.200000 [40,] 5.100000 3.400000 1.500000 0.200000 [41,] 5.000000 3.500000 1.300000 0.300000 [42,] 4.500000 2.300000 1.300000 0.300000 [43,] 4.400000 3.200000 1.300000 0.200000 [44,] 5.000000 3.500000 1.600000 0.600000 [45,] 5.100000 3.800000 1.900000 0.400000 [46,] 4.800000 3.000000 1.400000 0.300000 [47,] 5.100000 3.800000 1.600000 0.200000 [48,] 4.600000 3.200000 1.400000 0.200000 [49,] 5.300000 3.700000 1.500000 0.200000 [50,] 5.000000 3.300000 1.400000 0.200000 [51,] 7.000000 3.200000 4.700000 1.400000 [52,] 6.400000 3.200000 4.500000 1.500000 [53,] 6.900000 2.938167 4.900000 1.500000 [54,] 5.500000 2.300000 4.000000 1.300000 [55,] 6.500000 2.800000 4.600000 1.500000 [56,] 5.700000 2.800000 4.500000 1.300000 [57,] 6.300000 3.300000 4.700000 1.600000 [58,] 4.900000 2.400000 3.300000 1.000000 [59,] 6.600000 2.900000 4.600000 1.300000 [60,] 5.200000 2.700000 3.900000 1.400000 [61,] 5.000000 2.000000 3.500000 1.000000 [62,] 5.900000 3.000000 4.200000 1.500000 [63,] 6.000000 2.200000 4.000000 1.000000 [64,] 6.100000 2.900000 4.700000 1.400000 [65,] 5.600000 2.900000 3.600000 1.300000 [66,] 6.700000 3.100000 4.400000 1.400000 [67,] 5.600000 3.000000 4.500000 1.500000 [68,] 5.800000 2.700000 4.100000 1.000000 [69,] 6.200000 2.200000 4.500000 1.500000 [70,] 5.600000 2.500000 3.900000 1.100000 [71,] 5.900000 3.200000 4.800000 1.800000 [72,] 6.100000 2.800000 4.000000 1.300000 [73,] 6.300000 2.500000 4.900000 1.500000 [74,] 6.100000 2.800000 4.700000 1.200000 [75,] 6.400000 2.900000 4.300000 1.300000 [76,] 6.600000 3.000000 4.400000 1.400000 [77,] 6.800000 2.800000 4.800000 1.400000 [78,] 6.700000 3.000000 5.000000 1.700000 [79,] 6.000000 2.900000 4.500000 1.500000 [80,] 5.700000 2.600000 3.500000 1.000000 [81,] 5.500000 2.400000 3.800000 1.100000 [82,] 5.500000 2.400000 3.700000 1.000000 [83,] 5.800000 2.700000 3.900000 1.200000 [84,] 6.000000 2.700000 5.100000 1.600000 [85,] 5.400000 3.000000 4.500000 1.500000 [86,] 6.000000 3.400000 4.500000 1.600000 [87,] 6.700000 3.100000 4.700000 1.500000 [88,] 6.300000 2.300000 4.400000 1.300000 [89,] 5.600000 3.000000 4.100000 1.300000 [90,] 5.500000 2.500000 4.000000 1.300000 [91,] 5.500000 2.600000 4.400000 1.200000 [92,] 6.100000 3.000000 4.600000 1.400000 [93,] 5.800000 2.600000 4.000000 1.200000 [94,] 5.000000 2.300000 3.300000 1.000000 [95,] 5.600000 2.700000 4.200000 1.300000 [96,] 5.700000 3.000000 4.200000 1.200000 [97,] 5.700000 2.900000 4.200000 1.300000 [98,] 6.200000 2.900000 4.300000 1.300000 [99,] 5.100000 2.500000 3.000000 1.100000 [100,] 5.700000 2.800000 4.100000 1.300000 [101,] 6.300000 3.300000 6.000000 2.500000 [102,] 5.800000 2.700000 5.100000 1.900000 [103,] 7.100000 3.000000 5.900000 2.100000 [104,] 6.300000 2.900000 5.600000 1.800000 [105,] 6.500000 3.000000 5.800000 2.200000 [106,] 7.600000 3.000000 6.600000 2.100000 [107,] 4.900000 2.500000 4.500000 1.700000 [108,] 7.300000 2.900000 6.300000 1.800000 [109,] 6.700000 2.500000 5.800000 1.800000 [110,] 7.200000 3.600000 6.100000 2.500000 [111,] 6.500000 3.200000 5.100000 2.000000 [112,] 6.400000 2.700000 5.300000 1.900000 [113,] 6.800000 3.000000 5.500000 2.100000 [114,] 5.700000 2.500000 5.000000 2.000000 [115,] 5.800000 2.800000 5.100000 2.400000 [116,] 6.400000 3.200000 5.300000 2.300000 [117,] 6.500000 3.000000 5.500000 1.800000 [118,] 7.700000 3.800000 6.700000 2.200000 [119,] 7.700000 2.600000 6.900000 2.300000 [120,] 6.000000 2.200000 5.000000 1.500000 [121,] 6.900000 3.200000 5.700000 2.300000 [122,] 5.600000 2.800000 4.900000 2.000000 [123,] 7.700000 2.800000 6.700000 2.000000 [124,] 6.300000 2.700000 4.900000 1.800000 [125,] 6.700000 3.300000 5.700000 2.100000 [126,] 7.200000 3.200000 6.000000 1.800000 [127,] 6.200000 2.800000 4.800000 1.800000 [128,] 6.100000 3.000000 4.900000 1.800000 [129,] 6.400000 2.800000 5.600000 2.100000 [130,] 7.200000 3.000000 5.800000 1.600000 [131,] 7.400000 2.800000 6.100000 1.900000 [132,] 7.900000 3.800000 6.400000 2.000000 [133,] 6.400000 2.800000 5.600000 2.200000 [134,] 6.300000 2.800000 5.100000 1.500000 [135,] 6.100000 2.600000 5.600000 1.400000 [136,] 7.700000 3.000000 6.100000 2.300000 [137,] 6.300000 3.400000 5.600000 2.400000 [138,] 6.400000 3.100000 5.500000 1.800000 [139,] 6.000000 3.000000 4.800000 1.800000 [140,] 6.900000 3.100000 5.400000 2.100000 [141,] 6.700000 3.100000 5.600000 2.400000 [142,] 6.900000 3.100000 5.100000 2.300000 [143,] 5.800000 2.700000 5.100000 1.900000 [144,] 6.800000 3.200000 5.900000 2.300000 [145,] 6.700000 3.144924 5.700000 2.500000 [146,] 6.700000 3.000000 5.200000 2.300000 [147,] 6.300000 2.500000 5.000000 1.900000 [148,] 6.500000 3.000000 5.200000 2.000000 [149,] 6.200000 3.400000 5.400000 1.937460 [150,] 5.900000 3.000000 5.100000 1.800000 * missing = row col [1,] 24 1 [2,] 9 2 [3,] 53 2 [4,] 145 2 [5,] 149 4 * nbSample = 150 * nbCluster = 3 * lnLikelihood = -1032.504 * nbFreeParameter= 70 * criterion name = ICL * criterion value= 2424.009 * 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 = 5.017381 3.432766 1.462000 0.246000 * S.D. = 0.5123836 0.3327393 0.4260094 0.2014404 **************************************** *** Cluster: 2 * Proportion = 0.3333333 * Means = 5.936000 2.766763 4.260000 1.326000 * S.D. = 0.5123836 0.3327393 0.4260094 0.2014404 **************************************** *** Cluster: 3 * Proportion = 0.3333333 * Means = 6.588000 2.970898 5.552000 2.018749 * S.D. = 0.5123836 0.3327393 0.4260094 0.2014404 **************************************** > 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,] 5.100000 3.500000 1.400000 0.200000 [2,] 4.900000 3.000000 1.400000 0.200000 [3,] 4.700000 3.200000 1.300000 0.200000 [4,] 4.600000 3.100000 1.500000 0.200000 [5,] 5.000000 3.600000 1.400000 0.200000 [6,] 5.400000 3.900000 1.700000 0.400000 [7,] 4.600000 3.400000 1.400000 0.300000 [8,] 5.000000 3.400000 1.500000 0.200000 [9,] 4.400000 3.138302 1.400000 0.200000 [10,] 4.900000 3.100000 1.500000 0.100000 [11,] 5.400000 3.700000 1.500000 0.200000 [12,] 4.800000 3.400000 1.600000 0.200000 [13,] 4.800000 3.000000 1.400000 0.100000 [14,] 4.300000 3.000000 1.100000 0.100000 [15,] 5.800000 4.000000 1.200000 0.200000 [16,] 5.700000 4.400000 1.500000 0.400000 [17,] 5.400000 3.900000 1.300000 0.400000 [18,] 5.100000 3.500000 1.400000 0.300000 [19,] 5.700000 3.800000 1.700000 0.300000 [20,] 5.100000 3.800000 1.500000 0.300000 [21,] 5.400000 3.400000 1.700000 0.200000 [22,] 5.100000 3.700000 1.500000 0.400000 [23,] 4.600000 3.600000 1.000000 0.200000 [24,] 5.669062 3.300000 1.700000 0.500000 [25,] 4.800000 3.400000 1.900000 0.200000 [26,] 5.000000 3.000000 1.600000 0.200000 [27,] 5.000000 3.400000 1.600000 0.400000 [28,] 5.200000 3.500000 1.500000 0.200000 [29,] 5.200000 3.400000 1.400000 0.200000 [30,] 4.700000 3.200000 1.600000 0.200000 [31,] 4.800000 3.100000 1.600000 0.200000 [32,] 5.400000 3.400000 1.500000 0.400000 [33,] 5.200000 4.100000 1.500000 0.100000 [34,] 5.500000 4.200000 1.400000 0.200000 [35,] 4.900000 3.100000 1.500000 0.200000 [36,] 5.000000 3.200000 1.200000 0.200000 [37,] 5.500000 3.500000 1.300000 0.200000 [38,] 4.900000 3.600000 1.400000 0.100000 [39,] 4.400000 3.000000 1.300000 0.200000 [40,] 5.100000 3.400000 1.500000 0.200000 [41,] 5.000000 3.500000 1.300000 0.300000 [42,] 4.500000 2.300000 1.300000 0.300000 [43,] 4.400000 3.200000 1.300000 0.200000 [44,] 5.000000 3.500000 1.600000 0.600000 [45,] 5.100000 3.800000 1.900000 0.400000 [46,] 4.800000 3.000000 1.400000 0.300000 [47,] 5.100000 3.800000 1.600000 0.200000 [48,] 4.600000 3.200000 1.400000 0.200000 [49,] 5.300000 3.700000 1.500000 0.200000 [50,] 5.000000 3.300000 1.400000 0.200000 [51,] 7.000000 3.200000 4.700000 1.400000 [52,] 6.400000 3.200000 4.500000 1.500000 [53,] 6.900000 2.938167 4.900000 1.500000 [54,] 5.500000 2.300000 4.000000 1.300000 [55,] 6.500000 2.800000 4.600000 1.500000 [56,] 5.700000 2.800000 4.500000 1.300000 [57,] 6.300000 3.300000 4.700000 1.600000 [58,] 4.900000 2.400000 3.300000 1.000000 [59,] 6.600000 2.900000 4.600000 1.300000 [60,] 5.200000 2.700000 3.900000 1.400000 [61,] 5.000000 2.000000 3.500000 1.000000 [62,] 5.900000 3.000000 4.200000 1.500000 [63,] 6.000000 2.200000 4.000000 1.000000 [64,] 6.100000 2.900000 4.700000 1.400000 [65,] 5.600000 2.900000 3.600000 1.300000 [66,] 6.700000 3.100000 4.400000 1.400000 [67,] 5.600000 3.000000 4.500000 1.500000 [68,] 5.800000 2.700000 4.100000 1.000000 [69,] 6.200000 2.200000 4.500000 1.500000 [70,] 5.600000 2.500000 3.900000 1.100000 [71,] 5.900000 3.200000 4.800000 1.800000 [72,] 6.100000 2.800000 4.000000 1.300000 [73,] 6.300000 2.500000 4.900000 1.500000 [74,] 6.100000 2.800000 4.700000 1.200000 [75,] 6.400000 2.900000 4.300000 1.300000 [76,] 6.600000 3.000000 4.400000 1.400000 [77,] 6.800000 2.800000 4.800000 1.400000 [78,] 6.700000 3.000000 5.000000 1.700000 [79,] 6.000000 2.900000 4.500000 1.500000 [80,] 5.700000 2.600000 3.500000 1.000000 [81,] 5.500000 2.400000 3.800000 1.100000 [82,] 5.500000 2.400000 3.700000 1.000000 [83,] 5.800000 2.700000 3.900000 1.200000 [84,] 6.000000 2.700000 5.100000 1.600000 [85,] 5.400000 3.000000 4.500000 1.500000 [86,] 6.000000 3.400000 4.500000 1.600000 [87,] 6.700000 3.100000 4.700000 1.500000 [88,] 6.300000 2.300000 4.400000 1.300000 [89,] 5.600000 3.000000 4.100000 1.300000 [90,] 5.500000 2.500000 4.000000 1.300000 [91,] 5.500000 2.600000 4.400000 1.200000 [92,] 6.100000 3.000000 4.600000 1.400000 [93,] 5.800000 2.600000 4.000000 1.200000 [94,] 5.000000 2.300000 3.300000 1.000000 [95,] 5.600000 2.700000 4.200000 1.300000 [96,] 5.700000 3.000000 4.200000 1.200000 [97,] 5.700000 2.900000 4.200000 1.300000 [98,] 6.200000 2.900000 4.300000 1.300000 [99,] 5.100000 2.500000 3.000000 1.100000 [100,] 5.700000 2.800000 4.100000 1.300000 [101,] 6.300000 3.300000 6.000000 2.500000 [102,] 5.800000 2.700000 5.100000 1.900000 [103,] 7.100000 3.000000 5.900000 2.100000 [104,] 6.300000 2.900000 5.600000 1.800000 [105,] 6.500000 3.000000 5.800000 2.200000 [106,] 7.600000 3.000000 6.600000 2.100000 [107,] 4.900000 2.500000 4.500000 1.700000 [108,] 7.300000 2.900000 6.300000 1.800000 [109,] 6.700000 2.500000 5.800000 1.800000 [110,] 7.200000 3.600000 6.100000 2.500000 [111,] 6.500000 3.200000 5.100000 2.000000 [112,] 6.400000 2.700000 5.300000 1.900000 [113,] 6.800000 3.000000 5.500000 2.100000 [114,] 5.700000 2.500000 5.000000 2.000000 [115,] 5.800000 2.800000 5.100000 2.400000 [116,] 6.400000 3.200000 5.300000 2.300000 [117,] 6.500000 3.000000 5.500000 1.800000 [118,] 7.700000 3.800000 6.700000 2.200000 [119,] 7.700000 2.600000 6.900000 2.300000 [120,] 6.000000 2.200000 5.000000 1.500000 [121,] 6.900000 3.200000 5.700000 2.300000 [122,] 5.600000 2.800000 4.900000 2.000000 [123,] 7.700000 2.800000 6.700000 2.000000 [124,] 6.300000 2.700000 4.900000 1.800000 [125,] 6.700000 3.300000 5.700000 2.100000 [126,] 7.200000 3.200000 6.000000 1.800000 [127,] 6.200000 2.800000 4.800000 1.800000 [128,] 6.100000 3.000000 4.900000 1.800000 [129,] 6.400000 2.800000 5.600000 2.100000 [130,] 7.200000 3.000000 5.800000 1.600000 [131,] 7.400000 2.800000 6.100000 1.900000 [132,] 7.900000 3.800000 6.400000 2.000000 [133,] 6.400000 2.800000 5.600000 2.200000 [134,] 6.300000 2.800000 5.100000 1.500000 [135,] 6.100000 2.600000 5.600000 1.400000 [136,] 7.700000 3.000000 6.100000 2.300000 [137,] 6.300000 3.400000 5.600000 2.400000 [138,] 6.400000 3.100000 5.500000 1.800000 [139,] 6.000000 3.000000 4.800000 1.800000 [140,] 6.900000 3.100000 5.400000 2.100000 [141,] 6.700000 3.100000 5.600000 2.400000 [142,] 6.900000 3.100000 5.100000 2.300000 [143,] 5.800000 2.700000 5.100000 1.900000 [144,] 6.800000 3.200000 5.900000 2.300000 [145,] 6.700000 3.144924 5.700000 2.500000 [146,] 6.700000 3.000000 5.200000 2.300000 [147,] 6.300000 2.500000 5.000000 1.900000 [148,] 6.500000 3.000000 5.200000 2.000000 [149,] 6.200000 3.400000 5.400000 1.937460 [150,] 5.900000 3.000000 5.100000 1.800000 * missing = row col * nbSample = 150 * nbCluster = 3 * lnLikelihood = -1023.962 * nbFreeParameter= 70 * criterion name = ICL * criterion value= 2406.18 * 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 = 5.017381 3.432766 1.462000 0.246000 * S.D. = 0.3609027 0.3699947 0.1719186 0.1043264 **************************************** *** Cluster: 2 * Proportion = 0.3333333 * Means = 5.936000 2.766763 4.260000 1.326000 * S.D. = 0.5109834 0.3080213 0.4651881 0.1957652 **************************************** *** Cluster: 3 * Proportion = 0.3333333 * Means = 6.588000 2.970898 5.552000 2.018749 * S.D. = 0.6294887 0.3168172 0.5463479 0.2693078 **************************************** > set.seed(2) > model <- learnGamma( data=x, labels= z, + , models = clusterGammaNames(prop = "equal") + , algo = "simul", nbIter = 2, epsilon = 1e-08 + ) > missingValues(model) row col value > print(model) **************************************** * model name = gamma_p_ajk_bjk * data = Sepal.Length Sepal.Width Petal.Length Petal.Width [1,] 5.100000 3.500000 1.400000 0.200000 [2,] 4.900000 3.000000 1.400000 0.200000 [3,] 4.700000 3.200000 1.300000 0.200000 [4,] 4.600000 3.100000 1.500000 0.200000 [5,] 5.000000 3.600000 1.400000 0.200000 [6,] 5.400000 3.900000 1.700000 0.400000 [7,] 4.600000 3.400000 1.400000 0.300000 [8,] 5.000000 3.400000 1.500000 0.200000 [9,] 4.400000 3.138302 1.400000 0.200000 [10,] 4.900000 3.100000 1.500000 0.100000 [11,] 5.400000 3.700000 1.500000 0.200000 [12,] 4.800000 3.400000 1.600000 0.200000 [13,] 4.800000 3.000000 1.400000 0.100000 [14,] 4.300000 3.000000 1.100000 0.100000 [15,] 5.800000 4.000000 1.200000 0.200000 [16,] 5.700000 4.400000 1.500000 0.400000 [17,] 5.400000 3.900000 1.300000 0.400000 [18,] 5.100000 3.500000 1.400000 0.300000 [19,] 5.700000 3.800000 1.700000 0.300000 [20,] 5.100000 3.800000 1.500000 0.300000 [21,] 5.400000 3.400000 1.700000 0.200000 [22,] 5.100000 3.700000 1.500000 0.400000 [23,] 4.600000 3.600000 1.000000 0.200000 [24,] 5.669062 3.300000 1.700000 0.500000 [25,] 4.800000 3.400000 1.900000 0.200000 [26,] 5.000000 3.000000 1.600000 0.200000 [27,] 5.000000 3.400000 1.600000 0.400000 [28,] 5.200000 3.500000 1.500000 0.200000 [29,] 5.200000 3.400000 1.400000 0.200000 [30,] 4.700000 3.200000 1.600000 0.200000 [31,] 4.800000 3.100000 1.600000 0.200000 [32,] 5.400000 3.400000 1.500000 0.400000 [33,] 5.200000 4.100000 1.500000 0.100000 [34,] 5.500000 4.200000 1.400000 0.200000 [35,] 4.900000 3.100000 1.500000 0.200000 [36,] 5.000000 3.200000 1.200000 0.200000 [37,] 5.500000 3.500000 1.300000 0.200000 [38,] 4.900000 3.600000 1.400000 0.100000 [39,] 4.400000 3.000000 1.300000 0.200000 [40,] 5.100000 3.400000 1.500000 0.200000 [41,] 5.000000 3.500000 1.300000 0.300000 [42,] 4.500000 2.300000 1.300000 0.300000 [43,] 4.400000 3.200000 1.300000 0.200000 [44,] 5.000000 3.500000 1.600000 0.600000 [45,] 5.100000 3.800000 1.900000 0.400000 [46,] 4.800000 3.000000 1.400000 0.300000 [47,] 5.100000 3.800000 1.600000 0.200000 [48,] 4.600000 3.200000 1.400000 0.200000 [49,] 5.300000 3.700000 1.500000 0.200000 [50,] 5.000000 3.300000 1.400000 0.200000 [51,] 7.000000 3.200000 4.700000 1.400000 [52,] 6.400000 3.200000 4.500000 1.500000 [53,] 6.900000 2.938167 4.900000 1.500000 [54,] 5.500000 2.300000 4.000000 1.300000 [55,] 6.500000 2.800000 4.600000 1.500000 [56,] 5.700000 2.800000 4.500000 1.300000 [57,] 6.300000 3.300000 4.700000 1.600000 [58,] 4.900000 2.400000 3.300000 1.000000 [59,] 6.600000 2.900000 4.600000 1.300000 [60,] 5.200000 2.700000 3.900000 1.400000 [61,] 5.000000 2.000000 3.500000 1.000000 [62,] 5.900000 3.000000 4.200000 1.500000 [63,] 6.000000 2.200000 4.000000 1.000000 [64,] 6.100000 2.900000 4.700000 1.400000 [65,] 5.600000 2.900000 3.600000 1.300000 [66,] 6.700000 3.100000 4.400000 1.400000 [67,] 5.600000 3.000000 4.500000 1.500000 [68,] 5.800000 2.700000 4.100000 1.000000 [69,] 6.200000 2.200000 4.500000 1.500000 [70,] 5.600000 2.500000 3.900000 1.100000 [71,] 5.900000 3.200000 4.800000 1.800000 [72,] 6.100000 2.800000 4.000000 1.300000 [73,] 6.300000 2.500000 4.900000 1.500000 [74,] 6.100000 2.800000 4.700000 1.200000 [75,] 6.400000 2.900000 4.300000 1.300000 [76,] 6.600000 3.000000 4.400000 1.400000 [77,] 6.800000 2.800000 4.800000 1.400000 [78,] 6.700000 3.000000 5.000000 1.700000 [79,] 6.000000 2.900000 4.500000 1.500000 [80,] 5.700000 2.600000 3.500000 1.000000 [81,] 5.500000 2.400000 3.800000 1.100000 [82,] 5.500000 2.400000 3.700000 1.000000 [83,] 5.800000 2.700000 3.900000 1.200000 [84,] 6.000000 2.700000 5.100000 1.600000 [85,] 5.400000 3.000000 4.500000 1.500000 [86,] 6.000000 3.400000 4.500000 1.600000 [87,] 6.700000 3.100000 4.700000 1.500000 [88,] 6.300000 2.300000 4.400000 1.300000 [89,] 5.600000 3.000000 4.100000 1.300000 [90,] 5.500000 2.500000 4.000000 1.300000 [91,] 5.500000 2.600000 4.400000 1.200000 [92,] 6.100000 3.000000 4.600000 1.400000 [93,] 5.800000 2.600000 4.000000 1.200000 [94,] 5.000000 2.300000 3.300000 1.000000 [95,] 5.600000 2.700000 4.200000 1.300000 [96,] 5.700000 3.000000 4.200000 1.200000 [97,] 5.700000 2.900000 4.200000 1.300000 [98,] 6.200000 2.900000 4.300000 1.300000 [99,] 5.100000 2.500000 3.000000 1.100000 [100,] 5.700000 2.800000 4.100000 1.300000 [101,] 6.300000 3.300000 6.000000 2.500000 [102,] 5.800000 2.700000 5.100000 1.900000 [103,] 7.100000 3.000000 5.900000 2.100000 [104,] 6.300000 2.900000 5.600000 1.800000 [105,] 6.500000 3.000000 5.800000 2.200000 [106,] 7.600000 3.000000 6.600000 2.100000 [107,] 4.900000 2.500000 4.500000 1.700000 [108,] 7.300000 2.900000 6.300000 1.800000 [109,] 6.700000 2.500000 5.800000 1.800000 [110,] 7.200000 3.600000 6.100000 2.500000 [111,] 6.500000 3.200000 5.100000 2.000000 [112,] 6.400000 2.700000 5.300000 1.900000 [113,] 6.800000 3.000000 5.500000 2.100000 [114,] 5.700000 2.500000 5.000000 2.000000 [115,] 5.800000 2.800000 5.100000 2.400000 [116,] 6.400000 3.200000 5.300000 2.300000 [117,] 6.500000 3.000000 5.500000 1.800000 [118,] 7.700000 3.800000 6.700000 2.200000 [119,] 7.700000 2.600000 6.900000 2.300000 [120,] 6.000000 2.200000 5.000000 1.500000 [121,] 6.900000 3.200000 5.700000 2.300000 [122,] 5.600000 2.800000 4.900000 2.000000 [123,] 7.700000 2.800000 6.700000 2.000000 [124,] 6.300000 2.700000 4.900000 1.800000 [125,] 6.700000 3.300000 5.700000 2.100000 [126,] 7.200000 3.200000 6.000000 1.800000 [127,] 6.200000 2.800000 4.800000 1.800000 [128,] 6.100000 3.000000 4.900000 1.800000 [129,] 6.400000 2.800000 5.600000 2.100000 [130,] 7.200000 3.000000 5.800000 1.600000 [131,] 7.400000 2.800000 6.100000 1.900000 [132,] 7.900000 3.800000 6.400000 2.000000 [133,] 6.400000 2.800000 5.600000 2.200000 [134,] 6.300000 2.800000 5.100000 1.500000 [135,] 6.100000 2.600000 5.600000 1.400000 [136,] 7.700000 3.000000 6.100000 2.300000 [137,] 6.300000 3.400000 5.600000 2.400000 [138,] 6.400000 3.100000 5.500000 1.800000 [139,] 6.000000 3.000000 4.800000 1.800000 [140,] 6.900000 3.100000 5.400000 2.100000 [141,] 6.700000 3.100000 5.600000 2.400000 [142,] 6.900000 3.100000 5.100000 2.300000 [143,] 5.800000 2.700000 5.100000 1.900000 [144,] 6.800000 3.200000 5.900000 2.300000 [145,] 6.700000 3.144924 5.700000 2.500000 [146,] 6.700000 3.000000 5.200000 2.300000 [147,] 6.300000 2.500000 5.000000 1.900000 [148,] 6.500000 3.000000 5.200000 2.000000 [149,] 6.200000 3.400000 5.400000 1.937460 [150,] 5.900000 3.000000 5.100000 1.800000 * missing = row col * nbSample = 150 * nbCluster = 3 * lnLikelihood = -26635.16 * nbFreeParameter= 142 * criterion name = ICL * criterion value= 53985.39 * 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 * Shapes = 193.43258 84.46590 71.12183 6.24280 * Scales = 0.02593866 0.04064085 0.02055628 0.03940540 **************************************** *** Cluster: 2 * Proportion = 0.3333333 * Shapes = 134.69895 77.46366 79.04209 44.67472 * Scales = 0.04406864 0.03571692 0.05389534 0.02968122 **************************************** *** Cluster: 3 * Proportion = 0.3333333 * Shapes = 108.96926 88.70489 105.82543 54.74545 * Scales = 0.06045741 0.03349194 0.05246376 0.03687519 **************************************** > > ## get data and target from DebTrivedi data set > data(DebTrivedi) > x <- DebTrivedi[, c(1, 6,8, 15)] > z <- DebTrivedi$medicaid; > n <- nrow(x); p <- ncol(x); > model <- learnPoisson( data=x, labels=z + , models = clusterPoissonNames(prop = "equal") + , algo="simul", nbIter = 2, epsilon = 1e-08 + ) > print(model) **************************************** * model name = poisson_p_ljk * data = ofp hosp numchron school 1 5 1 2 6 2 1 0 2 10 3 13 3 4 10 4 16 1 2 3 5 3 0 2 6 6 17 0 5 7 7 9 0 0 8 8 3 0 0 8 9 1 0 0 8 10 0 0 0 8 11 0 0 1 8 12 44 1 5 15 13 2 0 1 8 14 1 0 1 8 15 19 1 1 12 16 19 0 0 8 17 0 0 1 8 18 3 0 2 8 19 2 0 3 10 20 12 0 4 12 21 2 2 1 12 22 3 0 2 8 23 1 0 1 9 24 1 0 2 9 25 5 0 3 8 26 1 0 1 4 27 12 0 5 6 28 6 1 2 8 29 2 0 0 12 30 4 0 3 8 31 2 0 0 16 32 2 0 0 9 33 1 0 1 8 34 0 0 0 9 35 1 0 0 12 36 4 1 3 8 37 26 0 2 12 38 3 1 0 12 39 1 0 0 8 40 1 0 0 12 41 1 0 1 12 42 3 0 1 12 43 0 0 0 13 44 17 0 3 12 45 5 0 3 10 46 5 1 2 13 47 9 0 0 16 48 2 0 1 11 49 1 0 2 8 50 9 2 1 12 51 11 0 1 13 52 3 0 0 14 53 1 0 3 8 54 1 0 0 0 55 0 0 0 0 56 3 0 0 12 57 2 0 2 8 58 4 1 1 8 59 3 0 1 12 60 5 0 1 8 61 5 1 4 13 62 1 0 2 11 63 4 0 2 8 64 0 1 0 18 65 8 0 2 12 66 8 2 1 8 67 1 0 0 12 68 5 1 1 10 69 0 0 0 9 70 4 0 1 7 71 3 1 1 9 72 11 0 1 8 73 13 0 1 10 74 0 0 1 8 75 7 0 2 8 76 5 0 3 10 77 3 0 2 13 78 2 0 3 13 79 22 0 1 5 80 6 0 2 12 81 1 0 1 9 82 8 0 2 12 83 0 0 1 9 84 1 0 1 8 85 5 0 2 12 86 2 0 1 10 87 6 0 5 12 88 10 1 0 10 89 1 0 1 12 90 3 0 1 10 91 7 0 0 12 92 10 0 2 8 93 13 1 2 12 94 1 0 0 12 95 16 0 2 10 96 29 1 4 6 97 8 1 2 12 98 4 1 2 12 99 1 0 3 12 100 8 0 2 12 101 9 0 1 12 102 1 0 1 6 103 0 0 1 7 104 0 0 5 5 105 7 0 1 13 106 0 0 1 9 107 8 1 4 8 108 0 0 0 12 109 22 1 3 8 110 4 0 1 9 111 3 0 3 8 112 1 0 2 12 113 3 0 1 12 114 7 0 1 12 115 0 0 1 9 116 11 2 2 12 117 3 2 1 12 118 0 0 1 6 119 0 0 1 12 120 0 0 1 12 121 7 0 1 9 122 4 1 2 6 123 9 0 4 12 124 15 0 3 3 125 2 0 3 10 126 5 1 0 11 127 0 0 0 9 128 5 0 1 16 129 13 2 1 16 130 1 0 1 12 131 0 0 2 10 132 10 0 5 10 133 10 0 1 9 134 9 0 1 14 135 4 0 0 12 136 7 0 1 11 137 0 0 2 12 138 29 0 2 16 139 39 1 1 16 140 3 0 4 12 141 17 0 1 16 142 0 0 1 0 143 1 0 1 0 144 14 0 2 12 145 0 0 1 12 146 1 0 0 15 147 6 0 3 12 148 4 0 2 8 149 7 1 2 12 150 3 0 0 8 151 2 0 0 12 152 0 0 1 12 153 7 0 3 14 154 7 0 1 12 155 6 1 2 18 156 12 0 1 8 157 8 0 1 7 158 5 0 1 9 159 0 1 3 9 160 5 0 2 9 161 12 2 2 5 162 3 0 0 7 163 4 0 3 10 164 1 0 0 10 165 2 0 1 3 166 2 0 0 10 167 17 3 5 9 168 11 0 1 4 169 3 1 4 9 170 4 0 1 10 171 5 0 0 8 172 4 0 1 6 173 18 0 2 8 174 7 0 2 14 175 31 0 3 13 176 23 0 1 12 177 9 0 2 12 178 8 0 3 16 179 10 0 2 16 180 10 0 2 16 181 2 0 0 16 182 6 1 3 12 183 0 0 5 8 184 7 0 1 14 185 2 0 1 12 186 1 0 1 12 187 4 0 3 12 188 19 0 2 14 189 10 0 1 12 190 5 0 0 16 191 5 1 5 8 192 7 0 2 7 193 3 0 1 10 194 0 0 0 8 195 5 1 0 12 196 2 0 1 11 197 1 0 0 11 198 1 0 2 7 199 1 0 1 5 200 0 0 1 12 201 0 0 0 8 202 0 0 0 7 203 0 2 2 8 204 3 0 0 12 205 8 0 1 12 206 0 0 1 9 207 3 0 1 12 208 3 0 0 8 209 5 0 0 12 210 5 0 3 16 211 7 0 3 14 212 3 0 3 14 213 0 0 0 12 214 1 0 1 12 215 3 0 1 10 216 18 3 1 14 217 3 0 1 12 218 1 0 2 14 219 0 2 3 15 220 1 0 1 12 221 0 0 0 12 222 0 0 0 14 223 3 0 1 15 224 1 0 1 10 225 0 2 0 8 226 2 0 1 7 227 1 3 2 12 228 0 0 1 12 229 17 0 2 4 230 0 0 0 4 231 0 0 0 8 232 1 0 2 8 233 3 0 2 12 234 0 0 1 3 235 2 0 2 14 236 8 2 1 13 237 3 1 0 14 238 5 0 6 12 239 2 0 3 12 240 7 0 2 14 241 1 0 2 12 242 18 0 0 15 243 13 0 3 12 244 3 0 0 12 245 2 0 1 7 246 9 0 1 12 247 8 0 0 12 248 6 0 1 12 249 2 0 0 8 250 4 0 0 6 251 0 0 2 0 252 11 0 0 14 253 14 2 0 14 254 4 0 2 8 255 12 1 1 6 256 2 0 2 12 257 2 0 0 15 258 1 0 2 12 259 40 0 2 13 260 15 1 2 8 261 2 0 2 0 262 6 0 0 12 263 9 0 1 16 264 2 0 0 12 265 2 0 0 12 266 3 1 0 7 267 8 0 1 11 268 5 0 0 12 269 0 0 1 4 270 27 0 3 11 271 0 0 1 12 272 5 0 3 8 273 4 1 3 0 274 7 0 1 6 275 14 0 5 10 276 17 2 3 5 277 4 0 1 14 278 3 0 2 12 279 6 0 2 16 280 1 0 2 12 281 5 0 2 14 282 4 1 2 16 283 4 0 1 10 284 7 0 5 10 285 1 0 1 10 286 2 0 2 12 287 15 0 2 12 288 6 0 2 6 289 9 0 1 18 290 9 0 1 12 291 1 0 1 7 292 4 0 2 12 293 3 0 0 8 294 2 0 2 14 295 3 0 3 8 296 6 0 3 11 297 26 3 6 8 298 8 0 2 12 299 12 1 1 10 300 9 0 2 8 301 4 0 1 10 302 2 0 2 12 303 1 0 1 12 304 20 0 4 10 305 1 0 0 8 306 7 0 4 6 307 5 0 3 12 308 0 0 0 8 309 5 0 0 11 310 1 0 0 8 311 2 0 1 10 312 4 0 2 10 313 9 0 2 8 314 0 0 1 12 315 17 2 1 8 316 0 0 0 8 317 2 0 0 11 318 9 0 2 12 319 3 0 2 13 320 2 0 1 8 321 5 0 3 11 322 58 0 0 12 323 0 0 1 0 324 5 0 3 12 325 1 0 0 12 326 0 0 0 10 327 8 0 0 7 328 7 0 2 12 329 4 1 3 10 330 1 0 0 9 331 7 0 1 16 332 2 0 1 14 333 11 0 5 6 334 16 0 1 12 335 2 1 0 4 336 10 0 3 14 337 16 0 3 12 338 3 0 0 14 339 3 3 4 7 340 4 0 1 3 341 2 0 1 11 342 14 0 5 12 343 24 0 4 10 344 9 0 2 14 345 1 0 0 16 346 6 1 2 13 347 4 0 1 18 348 36 0 0 17 349 6 0 0 16 350 1 0 3 16 351 0 0 1 12 352 10 2 2 10 353 3 0 2 11 354 0 0 1 8 355 19 0 3 12 356 16 1 3 12 357 3 0 1 12 358 6 0 1 13 359 5 0 0 12 360 2 0 1 16 361 4 2 3 12 362 5 0 1 12 363 1 0 1 14 364 5 0 5 12 365 4 0 4 7 366 6 0 1 8 367 6 0 0 12 368 8 1 1 18 369 12 0 3 10 370 2 0 3 14 371 21 4 2 18 372 4 1 0 16 373 2 0 1 16 374 58 1 0 18 375 21 0 1 18 376 11 0 2 11 377 2 0 2 9 378 4 0 3 12 379 6 0 0 14 380 0 0 0 8 381 9 1 2 12 382 7 5 0 16 383 0 0 1 14 384 0 0 0 2 385 8 2 3 0 386 43 0 1 11 387 7 0 0 13 388 2 0 0 12 389 23 0 5 5 390 10 0 1 12 391 6 0 0 12 392 0 0 0 13 393 3 0 4 12 394 17 0 1 12 395 9 0 2 15 396 1 0 3 13 397 16 0 2 8 398 0 0 0 7 399 1 0 0 5 400 0 0 0 9 401 1 0 3 10 402 9 3 2 8 403 4 1 2 18 404 1 0 1 13 405 2 0 0 16 406 17 1 1 12 407 0 3 3 6 408 1 5 3 16 409 8 0 0 16 410 4 0 2 14 411 4 0 2 18 412 2 0 1 12 413 8 1 1 12 414 0 0 1 11 415 1 0 1 10 416 2 0 1 18 417 13 0 3 3 418 8 0 4 12 419 4 0 0 8 420 3 1 1 10 421 3 0 1 15 422 4 0 1 12 423 2 0 2 12 424 0 0 0 9 425 3 4 0 9 426 5 1 2 7 427 6 1 2 9 428 5 2 1 8 429 4 0 0 3 430 2 0 3 6 431 2 0 0 12 432 9 1 3 15 433 10 0 4 12 434 1 0 1 13 435 7 0 2 16 436 5 0 5 8 437 3 0 1 12 438 7 0 1 12 439 12 0 3 10 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7 597 5 2 1 0 598 1 0 1 5 599 12 0 4 6 600 1 0 3 12 601 7 2 1 13 602 4 0 0 7 603 8 0 1 8 604 34 0 6 3 605 0 0 1 4 606 4 0 1 9 607 7 0 3 8 608 0 0 0 11 609 0 2 1 1 610 3 0 0 5 611 0 0 4 8 612 2 0 4 5 613 1 0 1 10 614 14 3 5 9 615 9 0 2 5 616 3 0 1 6 617 11 0 3 12 618 7 0 3 10 619 0 0 0 12 620 2 0 1 7 621 55 0 5 10 622 14 2 1 6 623 61 0 1 16 624 2 0 3 8 625 6 0 1 8 626 5 0 1 18 627 8 1 3 3 628 0 0 3 3 629 2 0 2 10 630 0 0 1 9 631 4 0 1 12 632 3 0 2 10 633 19 1 6 8 634 9 0 4 10 635 0 0 2 9 636 13 0 1 7 637 1 0 1 12 638 8 0 1 12 639 2 0 1 12 640 0 0 1 4 641 3 2 2 10 642 10 0 1 7 643 9 0 0 7 644 1 1 4 9 645 4 0 0 9 646 9 0 2 8 647 0 0 2 12 648 0 0 1 12 649 3 0 0 12 650 7 0 2 12 651 8 2 3 14 652 9 2 6 6 653 1 0 2 8 654 2 0 2 8 655 1 0 1 11 656 6 1 2 12 657 22 0 1 0 658 0 0 0 14 659 0 0 1 3 660 3 0 1 12 661 6 1 2 7 662 4 1 3 7 663 13 1 1 0 664 2 0 1 5 665 5 0 2 8 666 0 0 0 8 667 7 4 2 7 668 3 0 2 8 669 1 0 1 18 670 2 2 3 10 671 0 0 2 8 672 5 0 3 12 673 4 0 5 7 674 1 0 3 3 675 5 0 2 0 676 3 0 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1 0 3 16 4397 2 0 0 12 4398 2 0 1 12 4399 11 1 2 4 4400 2 0 1 8 4401 12 0 3 7 4402 11 0 0 8 4403 12 0 2 11 4404 10 1 5 12 4405 16 0 0 12 4406 0 0 0 0 * missing = row col * nbSample = 4406 * nbCluster = 2 * lnLikelihood = -161277.5 * nbFreeParameter= 17 * criterion name = ICL * criterion value= 324634.3 * zi = [1] 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 [38] 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 [75] 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 [112] 0 0 0 0 0 0 1 0 0 0 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 [149] 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 [186] 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 [223] 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [260] 0 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 [297] 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 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0 0 0 0 0 0 0 0 1 0 0 0 0 0 [2628] 0 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 1 0 1 0 0 0 1 1 [2665] 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 [2702] 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 [2739] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 [2776] 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [2813] 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 [2850] 0 0 0 1 1 0 1 1 1 1 1 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [2887] 0 0 0 0 0 0 0 0 0 0 0 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 [2924] 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 1 0 0 0 0 0 [2961] 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [2998] 0 0 0 0 0 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 1 1 0 0 1 [3035] 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 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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 [3553] 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 1 0 0 0 0 [3590] 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 [3627] 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 [3664] 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 [3701] 0 0 0 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 [3738] 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 [3775] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [3812] 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 [3849] 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 1 1 [3886] 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 [3923] 0 0 0 0 0 0 0 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 [3960] 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 1 0 0 0 0 0 0 0 [3997] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [4034] 0 0 0 0 0 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 [4071] 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 [4108] 0 0 0 0 1 0 0 0 1 0 0 0 1 1 1 1 0 0 0 0 1 0 1 1 1 1 1 1 0 0 1 1 0 0 0 0 0 [4145] 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 [4182] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [4219] 1 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 1 0 0 1 [4256] 1 0 0 0 0 0 0 0 0 0 0 1 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 [4293] 0 0 1 1 0 0 1 0 1 0 1 1 0 0 1 1 0 0 0 0 0 1 1 0 0 1 0 0 0 1 1 1 1 0 0 0 0 [4330] 0 1 1 1 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [4367] 0 0 0 0 0 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 [4404] 0 0 0 **************************************** *** Cluster: 1 * Proportion = 0.9087608 * lambda = 5.680070 0.283966 1.500500 10.615135 **************************************** *** Cluster: 2 * Proportion = 0.09123922 * lambda = 6.7139303 0.4154229 1.9552239 7.0547264 **************************************** > > ## get data and target from bird data set > data(birds) > > ## add 10 missing values > x <- birds[,2:5]; x = as.matrix(x); z <- birds[,1]; n <- nrow(x); p <- ncol(x); > indexes <- matrix(c(round(runif(5,1,n)), round(runif(5,1,p))), ncol=2); > cbind(indexes, x[indexes]) [,1] [,2] [,3] [1,] "14" "4" "none" [2,] "49" "1" "pronounced" [3,] "40" "4" "none" [4,] "12" "2" "dotted" [5,] "65" "3" "white" > x[indexes] <- NA; > model <- learnCategorical( data=x, labels=z + , models = clusterCategoricalNames(prop = "equal") + , algo="simul", nbIter = 2, epsilon = 1e-08 + ) > missingValues(model) row col value 1 49 1 2 2 12 2 2 3 65 3 3 4 14 4 3 5 40 4 3 > print(model) **************************************** * model name = categorical_p_pjk * data = eyebrow collar sub-caudal border 1 2 1 3 1 2 1 1 1 3 3 3 2 3 3 4 3 1 3 3 5 3 1 3 3 6 3 1 3 3 7 3 1 3 3 8 2 1 3 3 9 2 1 3 3 10 2 1 3 3 11 2 1 1 3 12 3 2 3 3 13 2 1 1 3 14 2 1 2 3 15 2 1 3 3 16 2 1 1 3 17 2 1 1 3 18 2 1 1 3 19 2 1 3 3 20 1 1 3 3 21 2 1 1 3 22 3 1 3 3 23 2 1 2 3 24 2 1 1 3 25 2 1 1 3 26 2 2 1 3 27 2 1 2 3 28 2 1 2 3 29 1 1 1 3 30 1 2 3 3 31 2 1 3 3 32 1 2 3 3 33 2 1 3 1 34 1 2 3 3 35 3 2 3 3 36 3 2 3 3 37 3 2 3 3 38 3 2 3 3 39 3 2 3 3 40 3 2 3 3 41 3 2 3 3 42 3 2 3 3 43 3 2 3 3 44 3 2 3 3 45 3 2 3 3 46 3 2 3 3 47 3 2 3 3 48 3 2 3 3 49 2 2 3 3 50 4 2 2 3 51 3 2 3 3 52 3 2 3 3 53 3 2 3 2 54 3 2 3 3 55 3 2 3 3 56 3 2 3 3 57 4 2 3 1 58 4 2 3 3 59 3 2 3 3 60 3 2 3 3 61 3 2 3 3 62 3 2 3 3 63 3 2 3 3 64 3 2 3 3 65 3 2 3 3 66 3 2 3 3 67 3 2 3 3 68 4 2 3 3 69 3 2 3 3 * missing = row col 49 49 1 12 12 2 65 65 3 14 14 4 40 40 4 **************************************** * nbModalities = 4 **************************************** * levels = [1] "none , poor pronounced, pronounced , very pronounced" [2] "dotted, none " [3] "black , black & white, white " [4] "few , many, none" **************************************** * nbSample = 69 * nbCluster = 2 * lnLikelihood = -468.4445 * nbFreeParameter= 23 * criterion name = ICL * criterion value= 1124.04 * zi = [1] 1 0 0 1 1 1 0 0 0 0 0 1 0 1 0 0 0 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1 0 1 0 0 0 1 [39] 1 0 0 1 0 0 0 0 1 1 0 0 0 1 1 0 1 0 0 0 1 0 1 1 1 1 0 0 0 1 1 **************************************** *** Cluster: 1 * Proportion = 0.5217391 * probabilities = [,1] [,2] [,3] [,4] [1,] 0.05555556 0.38888889 0.16666667 0.05555556 [2,] 0.36111111 0.61111111 0.05555556 0.00000000 [3,] 0.50000000 0.00000000 0.77777778 0.94444444 [4,] 0.08333333 0.00000000 0.00000000 0.00000000 * probabilities = [,1] [,2] [,3] [,4] [1,] 0.05555556 0.38888889 0.16666667 0.05555556 [2,] 0.36111111 0.61111111 0.05555556 0.00000000 [3,] 0.50000000 0.00000000 0.77777778 0.94444444 [4,] 0.08333333 0.00000000 0.00000000 0.00000000 **************************************** *** Cluster: 2 * Proportion = 0.4782609 * probabilities = [,1] [,2] [,3] [,4] [1,] 0.12121212 0.42424242 0.15151515 0.03030303 [2,] 0.27272727 0.57575758 0.09090909 0.03030303 [3,] 0.57575758 0.00000000 0.75757576 0.93939394 [4,] 0.03030303 0.00000000 0.00000000 0.00000000 * probabilities = [,1] [,2] [,3] [,4] [1,] 0.12121212 0.42424242 0.15151515 0.03030303 [2,] 0.27272727 0.57575758 0.09090909 0.03030303 [3,] 0.57575758 0.00000000 0.75757576 0.93939394 [4,] 0.03030303 0.00000000 0.00000000 0.00000000 **************************************** > > ## A quantitative example with the heart disease data set > data(HeartDisease.cat) > data(HeartDisease.cont) > data(HeartDisease.target) > ## with default values > lcomponent = list(HeartDisease.cat, HeartDisease.cont); > models = c("categorical_pk_pjk","gaussian_pk_sjk") > z<-HeartDisease.target[[1]]; > model <- learnMixedData(lcomponent, models, z, algo="simul", nbIter=2) > missingValues(model) [[1]] row col value [1,] 167 7 2 [2,] 193 7 2 [3,] 288 7 1 [4,] 303 7 1 [5,] 88 8 1 [6,] 267 8 3 [[2]] row col value > print(model) **************************************** * nbSample = 303 * nbCluster = 5 * lnLikelihood = -7527.302 * nbFreeParameter= 129 * criterion name = ICL * criterion value= 16166.65 * zi = [1] 0 2 1 0 0 0 3 0 2 1 0 0 2 0 0 0 1 0 0 0 0 0 1 3 4 0 0 0 0 3 0 2 1 0 0 0 3 [38] 1 3 0 4 0 0 0 1 4 0 4 0 0 0 0 2 0 1 1 1 1 0 0 2 0 1 0 2 2 1 0 2 1 0 3 1 1 [75] 1 0 1 0 0 3 0 0 0 3 0 0 0 0 0 0 0 3 0 0 0 1 2 3 0 0 0 0 0 0 3 0 2 1 2 3 1 [112] 1 0 2 2 0 0 0 3 2 3 4 0 3 1 0 3 3 0 0 0 0 0 0 0 0 4 3 1 0 0 1 0 1 0 1 4 0 [149] 0 0 0 0 0 4 3 1 1 1 2 0 0 4 0 0 0 0 0 0 1 0 3 0 1 0 4 1 0 1 0 0 3 2 0 0 1 [186] 0 0 2 1 2 0 3 1 2 0 3 0 0 0 1 0 0 0 0 0 3 3 3 0 1 0 4 0 3 1 0 0 0 0 0 0 0 [223] 0 3 1 0 0 0 3 2 0 2 1 0 0 3 2 1 0 0 0 0 0 2 0 2 2 1 3 0 0 1 0 0 0 0 0 0 0 [260] 1 0 3 0 0 4 2 2 2 1 0 1 0 2 0 1 0 0 0 1 0 2 0 3 0 2 4 2 0 0 0 1 0 2 2 1 0 [297] 3 1 1 2 3 1 0 * model name = categorical_pk_pjk sex cp fbs restecg exang slope ca thal [1,] 2 1 2 3 1 3 1 2 [2,] 2 4 1 3 2 2 4 1 [3,] 2 4 1 3 2 2 3 3 [4,] 2 3 1 1 1 3 1 1 [5,] 1 2 1 3 1 1 1 1 [6,] 2 2 1 1 1 1 1 1 [7,] 1 4 1 3 1 3 3 1 [8,] 1 4 1 1 2 1 1 1 [9,] 2 4 1 3 1 2 2 3 [10,] 2 4 2 3 2 3 1 3 [11,] 2 4 1 1 1 2 1 2 [12,] 1 2 1 3 1 2 1 1 [13,] 2 3 2 3 2 2 2 2 [14,] 2 2 1 1 1 1 1 3 [15,] 2 3 2 1 1 1 1 3 [16,] 2 3 1 1 1 1 1 1 [17,] 2 2 1 1 1 3 1 3 [18,] 2 4 1 1 1 1 1 1 [19,] 1 3 1 1 1 1 1 1 [20,] 2 2 1 1 1 1 1 1 [21,] 2 1 1 3 2 2 1 1 [22,] 1 1 2 3 1 1 1 1 [23,] 2 2 1 3 1 2 1 1 [24,] 2 3 1 3 1 1 3 3 [25,] 2 4 1 3 2 2 3 3 [26,] 1 3 1 1 1 2 1 1 [27,] 1 3 1 1 1 1 1 1 [28,] 1 1 1 1 1 3 1 1 [29,] 2 4 1 1 1 1 1 1 [30,] 2 4 1 3 2 2 1 3 [31,] 1 1 1 1 1 1 3 1 [32,] 2 4 2 1 2 1 3 3 [33,] 2 3 1 1 1 1 1 1 [34,] 2 4 1 1 1 2 1 3 [35,] 2 3 1 1 2 1 1 1 [36,] 2 4 1 1 1 1 1 1 [37,] 2 4 1 3 2 2 1 3 [38,] 2 4 1 3 2 2 2 2 [39,] 2 4 1 1 2 2 2 3 [40,] 2 3 2 1 2 2 1 1 [41,] 1 4 1 3 1 2 4 3 [42,] 2 1 1 1 2 1 1 3 [43,] 1 2 1 1 1 1 3 1 [44,] 2 3 2 1 1 1 1 1 [45,] 1 4 1 3 1 1 1 1 [46,] 2 3 1 3 1 2 2 3 [47,] 2 3 1 1 1 1 1 1 [48,] 2 4 1 3 1 2 1 3 [49,] 1 3 2 3 1 1 2 1 [50,] 2 3 2 3 1 3 1 1 [51,] 1 2 1 1 1 1 2 1 [52,] 2 4 1 1 1 1 1 3 [53,] 2 4 1 3 1 1 2 1 [54,] 2 2 1 3 1 1 1 1 [55,] 2 4 1 1 2 1 2 3 [56,] 2 4 1 3 2 2 2 3 [57,] 2 3 1 1 1 2 2 3 [58,] 2 4 1 3 1 1 1 3 [59,] 2 3 1 3 1 3 2 1 [60,] 2 1 1 3 2 1 2 1 [61,] 1 4 1 1 2 2 1 3 [62,] 1 3 1 3 2 3 1 1 [63,] 2 4 1 3 2 2 4 3 [64,] 1 3 2 1 1 1 1 1 [65,] 2 4 1 1 1 2 2 3 [66,] 2 4 1 3 2 2 3 3 [67,] 2 3 1 3 1 2 1 1 [68,] 2 3 1 3 1 1 1 3 [69,] 2 4 1 3 2 3 1 3 [70,] 2 3 1 1 1 2 1 1 [71,] 1 3 1 1 1 1 1 1 [72,] 2 4 2 1 1 2 3 3 [73,] 2 4 1 1 2 2 3 3 [74,] 2 4 1 3 1 1 3 2 [75,] 2 4 1 3 1 1 2 1 [76,] 1 3 1 3 1 1 1 1 [77,] 2 4 1 3 2 2 2 3 [78,] 1 3 1 3 1 1 2 1 [79,] 2 2 1 3 1 2 1 1 [80,] 2 4 1 3 2 1 1 3 [81,] 2 4 1 3 2 2 1 1 [82,] 1 4 1 3 1 2 1 1 [83,] 2 3 1 3 1 1 1 1 [84,] 2 3 2 3 2 2 1 3 [85,] 2 2 1 1 1 1 1 1 [86,] 2 3 1 3 1 1 1 1 [87,] 2 3 1 3 1 1 1 1 [88,] 1 3 1 3 1 1 1 1 [89,] 1 4 1 3 1 1 1 1 [90,] 1 3 1 3 1 1 1 1 [91,] 2 4 1 3 1 2 1 1 [92,] 1 4 1 3 1 3 4 3 [93,] 2 3 1 1 1 2 4 3 [94,] 1 3 1 1 1 2 1 1 [95,] 1 3 1 3 1 1 1 1 [96,] 2 4 1 1 2 1 2 3 [97,] 2 4 1 3 2 2 2 3 [98,] 1 4 1 3 1 2 3 3 [99,] 2 2 1 1 1 1 2 1 [100,] 2 4 1 3 1 1 1 1 [101,] 2 4 1 3 1 1 1 1 [102,] 2 1 1 3 1 1 1 1 [103,] 1 4 1 3 1 1 2 1 [104,] 1 3 2 3 1 1 2 1 [105,] 2 3 1 1 1 2 4 3 [106,] 2 2 1 1 1 1 1 3 [107,] 2 4 1 1 2 1 2 3 [108,] 2 3 1 3 1 2 2 3 [109,] 2 4 1 1 2 2 2 3 [110,] 2 4 1 1 1 2 1 3 [111,] 1 4 1 3 2 2 1 3 [112,] 2 4 2 3 2 2 2 1 [113,] 2 1 1 3 1 2 1 2 [114,] 1 4 2 3 2 2 1 3 [115,] 1 3 1 1 1 2 2 3 [116,] 2 2 1 1 1 2 1 2 [117,] 2 3 2 3 1 1 1 1 [118,] 1 4 1 1 1 1 1 1 [119,] 2 4 2 3 2 1 4 3 [120,] 2 4 1 3 1 2 2 3 [121,] 2 4 2 3 2 1 3 3 [122,] 1 4 1 3 1 2 4 3 [123,] 2 3 1 1 2 2 1 1 [124,] 2 4 1 1 2 3 1 3 [125,] 2 1 2 3 1 2 2 1 [126,] 1 2 1 3 1 2 1 1 [127,] 1 4 2 3 2 3 3 3 [128,] 2 4 1 1 2 2 2 3 [129,] 2 2 1 1 1 1 1 1 [130,] 1 4 1 1 1 1 1 1 [131,] 2 3 1 3 1 2 1 3 [132,] 2 3 1 1 2 1 2 3 [133,] 2 2 1 3 1 1 1 1 [134,] 2 4 1 3 2 1 1 1 [135,] 1 3 1 1 1 2 1 1 [136,] 1 2 1 3 1 2 1 1 [137,] 2 4 1 1 2 3 1 3 [138,] 2 2 1 3 1 2 2 3 [139,] 2 4 1 1 2 2 1 3 [140,] 2 3 2 3 1 2 1 1 [141,] 2 2 1 1 2 1 1 1 [142,] 2 1 1 3 1 2 1 3 [143,] 2 2 2 1 1 1 1 1 [144,] 2 3 1 1 2 2 1 3 [145,] 2 3 1 3 2 2 1 3 [146,] 2 3 1 1 1 1 1 1 [147,] 2 4 2 3 1 2 4 3 [148,] 2 3 1 1 1 1 1 1 [149,] 2 2 1 3 1 1 1 1 [150,] 1 3 1 1 1 1 2 1 [151,] 2 1 2 1 1 2 1 3 [152,] 1 4 1 3 1 2 1 1 [153,] 1 3 1 3 1 2 1 3 [154,] 2 4 1 3 2 2 2 3 [155,] 2 4 1 3 2 3 2 1 [156,] 2 4 1 3 1 2 4 1 [157,] 2 4 1 1 2 1 1 3 [158,] 2 4 1 3 1 1 3 3 [159,] 2 4 1 3 1 2 3 3 [160,] 2 3 1 1 1 1 2 3 [161,] 2 2 2 1 1 1 1 3 [162,] 2 4 1 3 2 1 4 1 [163,] 1 3 1 1 1 2 1 1 [164,] 1 4 1 3 1 2 1 1 [165,] 2 3 2 1 1 1 3 1 [166,] 2 4 1 1 2 1 1 3 [167,] 2 3 1 1 1 1 2 1 [168,] 1 2 2 3 2 1 2 1 [169,] 2 4 1 3 2 1 1 3 [170,] 1 2 1 1 1 2 1 1 [171,] 2 3 1 1 2 2 2 3 [172,] 2 4 1 3 2 1 1 3 [173,] 1 4 1 1 2 2 1 1 [174,] 1 4 1 3 1 2 1 1 [175,] 2 4 1 3 1 2 3 2 [176,] 2 4 1 1 2 2 2 3 [177,] 2 4 2 1 1 1 4 3 [178,] 2 4 1 3 2 2 2 2 [179,] 2 3 1 1 1 1 2 1 [180,] 2 3 2 3 1 1 4 1 [181,] 2 4 1 3 1 2 1 3 [182,] 1 4 1 3 2 2 3 3 [183,] 2 1 1 3 1 1 3 1 [184,] 2 1 1 3 1 3 1 3 [185,] 1 4 1 3 1 1 1 1 [186,] 1 2 1 1 1 1 3 1 [187,] 2 3 2 1 1 3 1 3 [188,] 2 2 1 1 2 2 4 2 [189,] 2 2 1 3 1 1 2 3 [190,] 2 3 1 3 1 2 4 3 [191,] 2 3 1 1 1 1 1 1 [192,] 2 4 1 1 2 2 4 3 [193,] 2 4 2 3 2 2 2 3 [194,] 1 4 2 1 1 2 4 1 [195,] 1 3 1 3 1 2 1 1 [196,] 2 4 1 3 2 2 3 1 [197,] 2 1 2 3 1 2 2 1 [198,] 1 4 1 3 2 2 1 1 [199,] 1 2 1 1 1 1 1 1 [200,] 2 1 1 3 1 1 1 1 [201,] 1 4 1 3 1 1 1 1 [202,] 1 4 1 1 2 1 1 1 [203,] 2 3 2 1 1 1 2 3 [204,] 1 3 1 1 1 1 1 3 [205,] 2 4 1 1 1 1 1 3 [206,] 2 4 1 3 2 2 4 3 [207,] 2 4 1 3 2 2 3 3 [208,] 2 4 1 3 2 2 1 3 [209,] 2 2 1 1 1 1 1 1 [210,] 1 4 1 1 2 2 1 1 [211,] 1 3 1 1 1 1 1 1 [212,] 2 1 1 1 2 2 1 3 [213,] 2 3 1 3 1 2 1 1 [214,] 1 4 2 1 2 2 3 3 [215,] 2 4 1 1 1 1 2 1 [216,] 2 1 1 3 1 2 1 3 [217,] 1 2 1 1 1 1 1 1 [218,] 1 4 1 3 2 2 1 1 [219,] 1 4 1 1 1 2 3 1 [220,] 2 4 1 3 1 1 1 1 [221,] 1 3 1 3 2 1 1 1 [222,] 1 3 1 3 1 1 1 1 [223,] 1 3 1 1 1 1 1 1 [224,] 2 4 1 1 2 2 3 3 [225,] 1 4 1 1 2 2 3 1 [226,] 1 2 1 1 1 1 1 1 [227,] 2 4 1 1 1 1 1 1 [228,] 1 3 1 1 1 1 2 1 [229,] 2 4 1 3 2 2 2 1 [230,] 2 4 1 3 2 1 2 1 [231,] 1 3 1 3 1 2 1 1 [232,] 1 4 1 2 2 2 1 1 [233,] 2 3 1 3 1 1 4 1 [234,] 1 2 1 3 2 1 2 1 [235,] 1 3 1 1 1 1 2 1 [236,] 2 4 1 3 2 2 3 1 [237,] 2 4 2 3 2 3 1 3 [238,] 2 4 1 3 1 1 1 3 [239,] 1 2 1 1 1 2 1 1 [240,] 2 2 1 1 1 1 1 1 [241,] 2 2 1 1 1 1 1 1 [242,] 1 2 1 1 1 1 1 1 [243,] 1 4 1 1 1 1 1 1 [244,] 2 1 1 1 1 2 3 1 [245,] 1 3 2 1 1 1 1 1 [246,] 2 4 1 1 1 2 1 1 [247,] 2 4 1 1 1 1 2 3 [248,] 2 4 1 3 2 2 2 1 [249,] 2 4 1 1 1 1 3 3 [250,] 2 2 2 3 1 1 1 1 [251,] 2 4 1 1 2 2 1 2 [252,] 2 4 1 1 1 2 2 3 [253,] 2 4 1 1 2 2 2 3 [254,] 1 3 1 3 1 1 1 1 [255,] 2 4 1 1 1 2 1 1 [256,] 1 3 1 1 1 2 1 1 [257,] 1 4 1 1 1 1 3 1 [258,] 1 3 1 2 1 2 1 1 [259,] 2 2 1 3 1 1 1 1 [260,] 2 2 1 1 1 1 1 3 [261,] 1 3 1 1 1 2 2 1 [262,] 1 2 2 3 1 1 3 1 [263,] 1 1 1 1 1 1 1 1 [264,] 2 3 1 1 1 1 1 1 [265,] 2 4 1 3 2 2 2 1 [266,] 2 4 1 1 2 2 1 2 [267,] 2 4 2 1 2 2 1 3 [268,] 2 3 2 1 1 2 2 2 [269,] 2 4 1 1 1 1 1 3 [270,] 2 3 1 1 1 1 1 1 [271,] 2 4 1 3 2 1 2 3 [272,] 2 4 1 3 1 1 1 2 [273,] 2 4 1 1 2 2 3 3 [274,] 1 4 1 1 1 2 1 1 [275,] 2 1 1 1 1 1 3 1 [276,] 2 1 1 3 1 2 1 3 [277,] 1 3 1 3 1 2 2 1 [278,] 1 3 1 1 1 2 1 1 [279,] 2 2 1 3 1 1 2 1 [280,] 1 4 1 1 1 2 1 1 [281,] 2 4 1 1 2 2 2 3 [282,] 2 3 1 1 1 1 1 1 [283,] 1 4 1 2 2 2 2 3 [284,] 2 2 1 1 1 1 1 1 [285,] 2 4 1 1 1 1 2 3 [286,] 2 4 1 2 1 3 4 2 [287,] 1 4 2 3 2 2 3 2 [288,] 2 2 1 1 1 2 1 3 [289,] 2 2 1 3 1 1 1 3 [290,] 2 2 1 1 1 3 1 1 [291,] 2 3 1 3 1 2 1 3 [292,] 1 2 1 1 1 1 1 1 [293,] 2 4 1 1 2 3 1 2 [294,] 2 4 1 3 2 1 3 3 [295,] 1 4 1 1 2 2 1 1 [296,] 2 2 1 1 1 1 1 1 [297,] 2 4 2 3 1 2 3 2 [298,] 1 4 1 1 2 2 1 3 [299,] 2 1 1 1 1 2 1 3 [300,] 2 4 2 1 1 2 3 3 [301,] 2 4 1 1 2 2 2 3 [302,] 1 2 1 3 1 2 2 1 [303,] 2 3 1 1 1 1 1 1 * model name = gaussian_pk_sjk age trestbps chol thalach oldpeak [1,] 63.0 145.0 233.0 150.0 2.3 [2,] 67.0 160.0 286.0 108.0 1.5 [3,] 67.0 120.0 229.0 129.0 2.6 [4,] 37.0 130.0 250.0 187.0 3.5 [5,] 41.0 130.0 204.0 172.0 1.4 [6,] 56.0 120.0 236.0 178.0 0.8 [7,] 62.0 140.0 268.0 160.0 3.6 [8,] 57.0 120.0 354.0 163.0 0.6 [9,] 63.0 130.0 254.0 147.0 1.4 [10,] 53.0 140.0 203.0 155.0 3.1 [11,] 57.0 140.0 192.0 148.0 0.4 [12,] 56.0 140.0 294.0 153.0 1.3 [13,] 56.0 130.0 256.0 142.0 0.6 [14,] 44.0 120.0 263.0 173.0 0.0 [15,] 52.0 172.0 199.0 162.0 0.5 [16,] 57.0 150.0 168.0 174.0 1.6 [17,] 48.0 110.0 229.0 168.0 1.0 [18,] 54.0 140.0 239.0 160.0 1.2 [19,] 48.0 130.0 275.0 139.0 0.2 [20,] 49.0 130.0 266.0 171.0 0.6 [21,] 64.0 110.0 211.0 144.0 1.8 [22,] 58.0 150.0 283.0 162.0 1.0 [23,] 58.0 120.0 284.0 160.0 1.8 [24,] 58.0 132.0 224.0 173.0 3.2 [25,] 60.0 130.0 206.0 132.0 2.4 [26,] 50.0 120.0 219.0 158.0 1.6 [27,] 58.0 120.0 340.0 172.0 0.0 [28,] 66.0 150.0 226.0 114.0 2.6 [29,] 43.0 150.0 247.0 171.0 1.5 [30,] 40.0 110.0 167.0 114.0 2.0 [31,] 69.0 140.0 239.0 151.0 1.8 [32,] 60.0 117.0 230.0 160.0 1.4 [33,] 64.0 140.0 335.0 158.0 0.0 [34,] 59.0 135.0 234.0 161.0 0.5 [35,] 44.0 130.0 233.0 179.0 0.4 [36,] 42.0 140.0 226.0 178.0 0.0 [37,] 43.0 120.0 177.0 120.0 2.5 [38,] 57.0 150.0 276.0 112.0 0.6 [39,] 55.0 132.0 353.0 132.0 1.2 [40,] 61.0 150.0 243.0 137.0 1.0 [41,] 65.0 150.0 225.0 114.0 1.0 [42,] 40.0 140.0 199.0 178.0 1.4 [43,] 71.0 160.0 302.0 162.0 0.4 [44,] 59.0 150.0 212.0 157.0 1.6 [45,] 61.0 130.0 330.0 169.0 0.0 [46,] 58.0 112.0 230.0 165.0 2.5 [47,] 51.0 110.0 175.0 123.0 0.6 [48,] 50.0 150.0 243.0 128.0 2.6 [49,] 65.0 140.0 417.0 157.0 0.8 [50,] 53.0 130.0 197.0 152.0 1.2 [51,] 41.0 105.0 198.0 168.0 0.0 [52,] 65.0 120.0 177.0 140.0 0.4 [53,] 44.0 112.0 290.0 153.0 0.0 [54,] 44.0 130.0 219.0 188.0 0.0 [55,] 60.0 130.0 253.0 144.0 1.4 [56,] 54.0 124.0 266.0 109.0 2.2 [57,] 50.0 140.0 233.0 163.0 0.6 [58,] 41.0 110.0 172.0 158.0 0.0 [59,] 54.0 125.0 273.0 152.0 0.5 [60,] 51.0 125.0 213.0 125.0 1.4 [61,] 51.0 130.0 305.0 142.0 1.2 [62,] 46.0 142.0 177.0 160.0 1.4 [63,] 58.0 128.0 216.0 131.0 2.2 [64,] 54.0 135.0 304.0 170.0 0.0 [65,] 54.0 120.0 188.0 113.0 1.4 [66,] 60.0 145.0 282.0 142.0 2.8 [67,] 60.0 140.0 185.0 155.0 3.0 [68,] 54.0 150.0 232.0 165.0 1.6 [69,] 59.0 170.0 326.0 140.0 3.4 [70,] 46.0 150.0 231.0 147.0 3.6 [71,] 65.0 155.0 269.0 148.0 0.8 [72,] 67.0 125.0 254.0 163.0 0.2 [73,] 62.0 120.0 267.0 99.0 1.8 [74,] 65.0 110.0 248.0 158.0 0.6 [75,] 44.0 110.0 197.0 177.0 0.0 [76,] 65.0 160.0 360.0 151.0 0.8 [77,] 60.0 125.0 258.0 141.0 2.8 [78,] 51.0 140.0 308.0 142.0 1.5 [79,] 48.0 130.0 245.0 180.0 0.2 [80,] 58.0 150.0 270.0 111.0 0.8 [81,] 45.0 104.0 208.0 148.0 3.0 [82,] 53.0 130.0 264.0 143.0 0.4 [83,] 39.0 140.0 321.0 182.0 0.0 [84,] 68.0 180.0 274.0 150.0 1.6 [85,] 52.0 120.0 325.0 172.0 0.2 [86,] 44.0 140.0 235.0 180.0 0.0 [87,] 47.0 138.0 257.0 156.0 0.0 [88,] 53.0 128.0 216.0 115.0 0.0 [89,] 53.0 138.0 234.0 160.0 0.0 [90,] 51.0 130.0 256.0 149.0 0.5 [91,] 66.0 120.0 302.0 151.0 0.4 [92,] 62.0 160.0 164.0 145.0 6.2 [93,] 62.0 130.0 231.0 146.0 1.8 [94,] 44.0 108.0 141.0 175.0 0.6 [95,] 63.0 135.0 252.0 172.0 0.0 [96,] 52.0 128.0 255.0 161.0 0.0 [97,] 59.0 110.0 239.0 142.0 1.2 [98,] 60.0 150.0 258.0 157.0 2.6 [99,] 52.0 134.0 201.0 158.0 0.8 [100,] 48.0 122.0 222.0 186.0 0.0 [101,] 45.0 115.0 260.0 185.0 0.0 [102,] 34.0 118.0 182.0 174.0 0.0 [103,] 57.0 128.0 303.0 159.0 0.0 [104,] 71.0 110.0 265.0 130.0 0.0 [105,] 49.0 120.0 188.0 139.0 2.0 [106,] 54.0 108.0 309.0 156.0 0.0 [107,] 59.0 140.0 177.0 162.0 0.0 [108,] 57.0 128.0 229.0 150.0 0.4 [109,] 61.0 120.0 260.0 140.0 3.6 [110,] 39.0 118.0 219.0 140.0 1.2 [111,] 61.0 145.0 307.0 146.0 1.0 [112,] 56.0 125.0 249.0 144.0 1.2 [113,] 52.0 118.0 186.0 190.0 0.0 [114,] 43.0 132.0 341.0 136.0 3.0 [115,] 62.0 130.0 263.0 97.0 1.2 [116,] 41.0 135.0 203.0 132.0 0.0 [117,] 58.0 140.0 211.0 165.0 0.0 [118,] 35.0 138.0 183.0 182.0 1.4 [119,] 63.0 130.0 330.0 132.0 1.8 [120,] 65.0 135.0 254.0 127.0 2.8 [121,] 48.0 130.0 256.0 150.0 0.0 [122,] 63.0 150.0 407.0 154.0 4.0 [123,] 51.0 100.0 222.0 143.0 1.2 [124,] 55.0 140.0 217.0 111.0 5.6 [125,] 65.0 138.0 282.0 174.0 1.4 [126,] 45.0 130.0 234.0 175.0 0.6 [127,] 56.0 200.0 288.0 133.0 4.0 [128,] 54.0 110.0 239.0 126.0 2.8 [129,] 44.0 120.0 220.0 170.0 0.0 [130,] 62.0 124.0 209.0 163.0 0.0 [131,] 54.0 120.0 258.0 147.0 0.4 [132,] 51.0 94.0 227.0 154.0 0.0 [133,] 29.0 130.0 204.0 202.0 0.0 [134,] 51.0 140.0 261.0 186.0 0.0 [135,] 43.0 122.0 213.0 165.0 0.2 [136,] 55.0 135.0 250.0 161.0 1.4 [137,] 70.0 145.0 174.0 125.0 2.6 [138,] 62.0 120.0 281.0 103.0 1.4 [139,] 35.0 120.0 198.0 130.0 1.6 [140,] 51.0 125.0 245.0 166.0 2.4 [141,] 59.0 140.0 221.0 164.0 0.0 [142,] 59.0 170.0 288.0 159.0 0.2 [143,] 52.0 128.0 205.0 184.0 0.0 [144,] 64.0 125.0 309.0 131.0 1.8 [145,] 58.0 105.0 240.0 154.0 0.6 [146,] 47.0 108.0 243.0 152.0 0.0 [147,] 57.0 165.0 289.0 124.0 1.0 [148,] 41.0 112.0 250.0 179.0 0.0 [149,] 45.0 128.0 308.0 170.0 0.0 [150,] 60.0 102.0 318.0 160.0 0.0 [151,] 52.0 152.0 298.0 178.0 1.2 [152,] 42.0 102.0 265.0 122.0 0.6 [153,] 67.0 115.0 564.0 160.0 1.6 [154,] 55.0 160.0 289.0 145.0 0.8 [155,] 64.0 120.0 246.0 96.0 2.2 [156,] 70.0 130.0 322.0 109.0 2.4 [157,] 51.0 140.0 299.0 173.0 1.6 [158,] 58.0 125.0 300.0 171.0 0.0 [159,] 60.0 140.0 293.0 170.0 1.2 [160,] 68.0 118.0 277.0 151.0 1.0 [161,] 46.0 101.0 197.0 156.0 0.0 [162,] 77.0 125.0 304.0 162.0 0.0 [163,] 54.0 110.0 214.0 158.0 1.6 [164,] 58.0 100.0 248.0 122.0 1.0 [165,] 48.0 124.0 255.0 175.0 0.0 [166,] 57.0 132.0 207.0 168.0 0.0 [167,] 52.0 138.0 223.0 169.0 0.0 [168,] 54.0 132.0 288.0 159.0 0.0 [169,] 35.0 126.0 282.0 156.0 0.0 [170,] 45.0 112.0 160.0 138.0 0.0 [171,] 70.0 160.0 269.0 112.0 2.9 [172,] 53.0 142.0 226.0 111.0 0.0 [173,] 59.0 174.0 249.0 143.0 0.0 [174,] 62.0 140.0 394.0 157.0 1.2 [175,] 64.0 145.0 212.0 132.0 2.0 [176,] 57.0 152.0 274.0 88.0 1.2 [177,] 52.0 108.0 233.0 147.0 0.1 [178,] 56.0 132.0 184.0 105.0 2.1 [179,] 43.0 130.0 315.0 162.0 1.9 [180,] 53.0 130.0 246.0 173.0 0.0 [181,] 48.0 124.0 274.0 166.0 0.5 [182,] 56.0 134.0 409.0 150.0 1.9 [183,] 42.0 148.0 244.0 178.0 0.8 [184,] 59.0 178.0 270.0 145.0 4.2 [185,] 60.0 158.0 305.0 161.0 0.0 [186,] 63.0 140.0 195.0 179.0 0.0 [187,] 42.0 120.0 240.0 194.0 0.8 [188,] 66.0 160.0 246.0 120.0 0.0 [189,] 54.0 192.0 283.0 195.0 0.0 [190,] 69.0 140.0 254.0 146.0 2.0 [191,] 50.0 129.0 196.0 163.0 0.0 [192,] 51.0 140.0 298.0 122.0 4.2 [193,] 43.0 132.0 247.0 143.0 0.1 [194,] 62.0 138.0 294.0 106.0 1.9 [195,] 68.0 120.0 211.0 115.0 1.5 [196,] 67.0 100.0 299.0 125.0 0.9 [197,] 69.0 160.0 234.0 131.0 0.1 [198,] 45.0 138.0 236.0 152.0 0.2 [199,] 50.0 120.0 244.0 162.0 1.1 [200,] 59.0 160.0 273.0 125.0 0.0 [201,] 50.0 110.0 254.0 159.0 0.0 [202,] 64.0 180.0 325.0 154.0 0.0 [203,] 57.0 150.0 126.0 173.0 0.2 [204,] 64.0 140.0 313.0 133.0 0.2 [205,] 43.0 110.0 211.0 161.0 0.0 [206,] 45.0 142.0 309.0 147.0 0.0 [207,] 58.0 128.0 259.0 130.0 3.0 [208,] 50.0 144.0 200.0 126.0 0.9 [209,] 55.0 130.0 262.0 155.0 0.0 [210,] 62.0 150.0 244.0 154.0 1.4 [211,] 37.0 120.0 215.0 170.0 0.0 [212,] 38.0 120.0 231.0 182.0 3.8 [213,] 41.0 130.0 214.0 168.0 2.0 [214,] 66.0 178.0 228.0 165.0 1.0 [215,] 52.0 112.0 230.0 160.0 0.0 [216,] 56.0 120.0 193.0 162.0 1.9 [217,] 46.0 105.0 204.0 172.0 0.0 [218,] 46.0 138.0 243.0 152.0 0.0 [219,] 64.0 130.0 303.0 122.0 2.0 [220,] 59.0 138.0 271.0 182.0 0.0 [221,] 41.0 112.0 268.0 172.0 0.0 [222,] 54.0 108.0 267.0 167.0 0.0 [223,] 39.0 94.0 199.0 179.0 0.0 [224,] 53.0 123.0 282.0 95.0 2.0 [225,] 63.0 108.0 269.0 169.0 1.8 [226,] 34.0 118.0 210.0 192.0 0.7 [227,] 47.0 112.0 204.0 143.0 0.1 [228,] 67.0 152.0 277.0 172.0 0.0 [229,] 54.0 110.0 206.0 108.0 0.0 [230,] 66.0 112.0 212.0 132.0 0.1 [231,] 52.0 136.0 196.0 169.0 0.1 [232,] 55.0 180.0 327.0 117.0 3.4 [233,] 49.0 118.0 149.0 126.0 0.8 [234,] 74.0 120.0 269.0 121.0 0.2 [235,] 54.0 160.0 201.0 163.0 0.0 [236,] 54.0 122.0 286.0 116.0 3.2 [237,] 56.0 130.0 283.0 103.0 1.6 [238,] 46.0 120.0 249.0 144.0 0.8 [239,] 49.0 134.0 271.0 162.0 0.0 [240,] 42.0 120.0 295.0 162.0 0.0 [241,] 41.0 110.0 235.0 153.0 0.0 [242,] 41.0 126.0 306.0 163.0 0.0 [243,] 49.0 130.0 269.0 163.0 0.0 [244,] 61.0 134.0 234.0 145.0 2.6 [245,] 60.0 120.0 178.0 96.0 0.0 [246,] 67.0 120.0 237.0 71.0 1.0 [247,] 58.0 100.0 234.0 156.0 0.1 [248,] 47.0 110.0 275.0 118.0 1.0 [249,] 52.0 125.0 212.0 168.0 1.0 [250,] 62.0 128.0 208.0 140.0 0.0 [251,] 57.0 110.0 201.0 126.0 1.5 [252,] 58.0 146.0 218.0 105.0 2.0 [253,] 64.0 128.0 263.0 105.0 0.2 [254,] 51.0 120.0 295.0 157.0 0.6 [255,] 43.0 115.0 303.0 181.0 1.2 [256,] 42.0 120.0 209.0 173.0 0.0 [257,] 67.0 106.0 223.0 142.0 0.3 [258,] 76.0 140.0 197.0 116.0 1.1 [259,] 70.0 156.0 245.0 143.0 0.0 [260,] 57.0 124.0 261.0 141.0 0.3 [261,] 44.0 118.0 242.0 149.0 0.3 [262,] 58.0 136.0 319.0 152.0 0.0 [263,] 60.0 150.0 240.0 171.0 0.9 [264,] 44.0 120.0 226.0 169.0 0.0 [265,] 61.0 138.0 166.0 125.0 3.6 [266,] 42.0 136.0 315.0 125.0 1.8 [267,] 52.0 128.0 204.0 156.0 1.0 [268,] 59.0 126.0 218.0 134.0 2.2 [269,] 40.0 152.0 223.0 181.0 0.0 [270,] 42.0 130.0 180.0 150.0 0.0 [271,] 61.0 140.0 207.0 138.0 1.9 [272,] 66.0 160.0 228.0 138.0 2.3 [273,] 46.0 140.0 311.0 120.0 1.8 [274,] 71.0 112.0 149.0 125.0 1.6 [275,] 59.0 134.0 204.0 162.0 0.8 [276,] 64.0 170.0 227.0 155.0 0.6 [277,] 66.0 146.0 278.0 152.0 0.0 [278,] 39.0 138.0 220.0 152.0 0.0 [279,] 57.0 154.0 232.0 164.0 0.0 [280,] 58.0 130.0 197.0 131.0 0.6 [281,] 57.0 110.0 335.0 143.0 3.0 [282,] 47.0 130.0 253.0 179.0 0.0 [283,] 55.0 128.0 205.0 130.0 2.0 [284,] 35.0 122.0 192.0 174.0 0.0 [285,] 61.0 148.0 203.0 161.0 0.0 [286,] 58.0 114.0 318.0 140.0 4.4 [287,] 58.0 170.0 225.0 146.0 2.8 [288,] 58.0 125.0 220.0 144.0 0.4 [289,] 56.0 130.0 221.0 163.0 0.0 [290,] 56.0 120.0 240.0 169.0 0.0 [291,] 67.0 152.0 212.0 150.0 0.8 [292,] 55.0 132.0 342.0 166.0 1.2 [293,] 44.0 120.0 169.0 144.0 2.8 [294,] 63.0 140.0 187.0 144.0 4.0 [295,] 63.0 124.0 197.0 136.0 0.0 [296,] 41.0 120.0 157.0 182.0 0.0 [297,] 59.0 164.0 176.0 90.0 1.0 [298,] 57.0 140.0 241.0 123.0 0.2 [299,] 45.0 110.0 264.0 132.0 1.2 [300,] 68.0 144.0 193.0 141.0 3.4 [301,] 57.0 130.0 131.0 115.0 1.2 [302,] 57.0 130.0 236.0 174.0 0.0 [303,] 38.0 138.0 175.0 173.0 0.0 **************************************** * model name = categorical_pk_pjk *** Cluster: 1 * Proportion = 0.5412541 * probabilities = [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.439024390 0.097560976 0.859756098 0.579268293 0.859756098 0.646341463 [2,] 0.560975610 0.250000000 0.140243902 0.006097561 0.140243902 0.298780488 [3,] 0.000000000 0.414634146 0.000000000 0.414634146 0.000000000 0.054878049 [4,] 0.000000000 0.237804878 0.000000000 0.000000000 0.000000000 0.000000000 [,7] [,8] [1,] 0.804878049 0.792682927 [2,] 0.134146341 0.036585366 [3,] 0.042682927 0.170731707 [4,] 0.018292683 0.000000000 *** Cluster: 2 * Proportion = 0.1815182 * probabilities = [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.16363636 0.09090909 0.92727273 0.41818182 0.54545455 0.40000000 [2,] 0.83636364 0.10909091 0.07272727 0.00000000 0.45454545 0.56363636 [3,] 0.00000000 0.16363636 0.00000000 0.58181818 0.00000000 0.03636364 [4,] 0.00000000 0.63636364 0.00000000 0.00000000 0.00000000 0.00000000 [,7] [,8] [1,] 0.47272727 0.40000000 [2,] 0.36363636 0.05454545 [3,] 0.10909091 0.54545455 [4,] 0.05454545 0.00000000 *** Cluster: 3 * Proportion = 0.1188119 * probabilities = [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.19444444 0.02777778 0.75000000 0.52777778 0.38888889 0.19444444 [2,] 0.80555556 0.02777778 0.25000000 0.02777778 0.61111111 0.72222222 [3,] 0.00000000 0.11111111 0.00000000 0.44444444 0.00000000 0.08333333 [4,] 0.00000000 0.83333333 0.00000000 0.00000000 0.00000000 0.00000000 [,7] [,8] [1,] 0.25000000 0.19444444 [2,] 0.38888889 0.16666667 [3,] 0.25000000 0.63888889 [4,] 0.11111111 0.00000000 *** Cluster: 4 * Proportion = 0.1155116 * probabilities = [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.20000000 0.00000000 0.77142857 0.34285714 0.34285714 0.17142857 [2,] 0.80000000 0.05714286 0.22857143 0.02857143 0.65714286 0.68571429 [3,] 0.00000000 0.11428571 0.00000000 0.62857143 0.00000000 0.14285714 [4,] 0.00000000 0.82857143 0.00000000 0.00000000 0.00000000 0.00000000 [,7] [,8] [1,] 0.22857143 0.17142857 [2,] 0.22857143 0.02857143 [3,] 0.40000000 0.80000000 [4,] 0.14285714 0.00000000 *** Cluster: 5 * Proportion = 0.04290429 * probabilities = [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.15384615 0.07692308 0.92307692 0.15384615 0.53846154 0.07692308 [2,] 0.84615385 0.00000000 0.07692308 0.07692308 0.46153846 0.76923077 [3,] 0.00000000 0.07692308 0.00000000 0.76923077 0.00000000 0.15384615 [4,] 0.00000000 0.84615385 0.00000000 0.00000000 0.00000000 0.00000000 [,7] [,8] [1,] 0.23076923 0.15384615 [2,] 0.23076923 0.15384615 [3,] 0.15384615 0.69230769 [4,] 0.38461538 0.00000000 * model name = gaussian_pk_sjk *** Cluster: 1 * Proportion = 0.5412541 * Means = 52.5853659 129.2500000 242.6402439 158.3780488 0.3536585 * S.D. = 9.482913 16.155259 53.293354 19.140457 0.669400 *** Cluster: 2 * Proportion = 0.1815182 * Means = 55.3818182 133.2545455 249.1090909 145.9272727 0.7090909 * S.D. = 7.9280650 17.8633380 40.3789651 22.6337275 0.8877495 *** Cluster: 3 * Proportion = 0.1188119 * Means = 58.027778 134.194444 259.277778 135.583333 1.416667 * S.D. = 7.119949 17.645930 52.993506 20.571522 1.063929 *** Cluster: 4 * Proportion = 0.1155116 * Means = 56.000000 135.457143 246.457143 132.057143 1.657143 * S.D. = 7.668116 21.349263 50.996551 22.372104 1.491746 *** Cluster: 5 * Proportion = 0.04290429 * Means = 59.69231 138.76923 253.38462 140.61538 2.00000 * S.D. = 9.050156 16.511785 63.687384 19.101101 1.240347 **************************************** > > > proc.time() user system elapsed 1.35 0.07 1.42