* using log directory 'd:/Rcompile/CRANpkg/local/3.6/MixAll.Rcheck' * using R version 3.6.3 (2020-02-29) * using platform: x86_64-w64-mingw32 (64-bit) * using session charset: ISO8859-1 * checking for file 'MixAll/DESCRIPTION' ... OK * this is package 'MixAll' version '1.5.1' * package encoding: UTF-8 * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking whether package 'MixAll' can be installed ... OK * checking installed package size ... NOTE installed size is 6.8Mb sub-directories of 1Mb or more: libs 4.7Mb * checking package directory ... OK * checking 'build' directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking R files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * loading checks for arch 'i386' ** checking whether the package can be loaded ... OK ** checking whether the package can be loaded with stated dependencies ... OK ** checking whether the package can be unloaded cleanly ... OK ** checking whether the namespace can be loaded with stated dependencies ... OK ** checking whether the namespace can be unloaded cleanly ... OK ** checking loading without being on the library search path ... OK ** checking use of S3 registration ... OK * loading checks for arch 'x64' ** checking whether the package can be loaded ... OK ** checking whether the package can be loaded with stated dependencies ... OK ** checking whether the package can be unloaded cleanly ... OK ** checking whether the namespace can be loaded with stated dependencies ... OK ** checking whether the namespace can be unloaded cleanly ... OK ** checking loading without being on the library search path ... OK ** checking use of S3 registration ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... [10s] OK * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking contents of 'data' directory ... OK * checking data for non-ASCII characters ... OK * checking data for ASCII and uncompressed saves ... OK * checking line endings in shell scripts ... OK * checking line endings in C/C++/Fortran sources/headers ... OK * checking line endings in Makefiles ... OK * checking compilation flags in Makevars ... OK * checking for GNU extensions in Makefiles ... OK * checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK * checking include directives in Makefiles ... OK * checking pragmas in C/C++ headers and code ... OK * checking compiled code ... OK * checking sizes of PDF files under 'inst/doc' ... OK * checking installed files from 'inst/doc' ... OK * checking files in 'vignettes' ... OK * checking examples ... ** running examples for arch 'i386' ... [14s] OK ** running examples for arch 'x64' ... [15s] OK * checking for unstated dependencies in 'tests' ... OK * checking tests ... ** running tests for arch 'i386' ... [17s] OK Running 'ClusterSimul.R' [0s] Running 'clusterDiagGaussianLikelihood.R' [1s] Running 'clusterGammaLikelihood.R' [1s] Running 'simulHeterogeneous.R' [0s] Running 'simulNonLinear.R' [1s] Running 'testAllLearners.R' [2s] Running 'testPoissonExample.R' [1s] Running 'testPredict.R' [10s] ** running tests for arch 'x64' ... [16s] ERROR Running 'ClusterSimul.R' [0s] Running 'clusterDiagGaussianLikelihood.R' [1s] Running 'clusterGammaLikelihood.R' [1s] Running 'simulHeterogeneous.R' [0s] Running 'simulNonLinear.R' [1s] Running 'testAllLearners.R' [1s] Running 'testPoissonExample.R' [1s] Running 'testPredict.R' [10s] 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,] 72 2 2.8 [2,] 134 2 2.8 [3,] 4 4 0.2 [4,] 20 2 3.8 [5,] 56 1 5.7 > 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 56 1 5.05756247 2 20 2 3.38943132 3 72 2 3.01088148 4 134 2 3.91766676 5 4 4 -0.03351271 > print(model) **************************************** * model name = gaussian_p_sj * 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 -0.03351271 [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 3.38943132 1.50000000 0.30000000 [21,] 5.40000000 3.40000000 1.70000000 0.20000000 [22,] 5.10000000 3.70000000 1.50000000 0.40000000 [23,] 4.60000000 3.60000000 1.00000000 0.20000000 [24,] 5.10000000 3.30000000 1.70000000 0.50000000 [25,] 4.80000000 3.40000000 1.90000000 0.20000000 [26,] 5.00000000 3.00000000 1.60000000 0.20000000 [27,] 5.00000000 3.40000000 1.60000000 0.40000000 [28,] 5.20000000 3.50000000 1.50000000 0.20000000 [29,] 5.20000000 3.40000000 1.40000000 0.20000000 [30,] 4.70000000 3.20000000 1.60000000 0.20000000 [31,] 4.80000000 3.10000000 1.60000000 0.20000000 [32,] 5.40000000 3.40000000 1.50000000 0.40000000 [33,] 5.20000000 4.10000000 1.50000000 0.10000000 [34,] 5.50000000 4.20000000 1.40000000 0.20000000 [35,] 4.90000000 3.10000000 1.50000000 0.20000000 [36,] 5.00000000 3.20000000 1.20000000 0.20000000 [37,] 5.50000000 3.50000000 1.30000000 0.20000000 [38,] 4.90000000 3.60000000 1.40000000 0.10000000 [39,] 4.40000000 3.00000000 1.30000000 0.20000000 [40,] 5.10000000 3.40000000 1.50000000 0.20000000 [41,] 5.00000000 3.50000000 1.30000000 0.30000000 [42,] 4.50000000 2.30000000 1.30000000 0.30000000 [43,] 4.40000000 3.20000000 1.30000000 0.20000000 [44,] 5.00000000 3.50000000 1.60000000 0.60000000 [45,] 5.10000000 3.80000000 1.90000000 0.40000000 [46,] 4.80000000 3.00000000 1.40000000 0.30000000 [47,] 5.10000000 3.80000000 1.60000000 0.20000000 [48,] 4.60000000 3.20000000 1.40000000 0.20000000 [49,] 5.30000000 3.70000000 1.50000000 0.20000000 [50,] 5.00000000 3.30000000 1.40000000 0.20000000 [51,] 7.00000000 3.20000000 4.70000000 1.40000000 [52,] 6.40000000 3.20000000 4.50000000 1.50000000 [53,] 6.90000000 3.10000000 4.90000000 1.50000000 [54,] 5.50000000 2.30000000 4.00000000 1.30000000 [55,] 6.50000000 2.80000000 4.60000000 1.50000000 [56,] 5.05756247 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 3.01088148 4.00000000 1.30000000 [73,] 6.30000000 2.50000000 4.90000000 1.50000000 [74,] 6.10000000 2.80000000 4.70000000 1.20000000 [75,] 6.40000000 2.90000000 4.30000000 1.30000000 [76,] 6.60000000 3.00000000 4.40000000 1.40000000 [77,] 6.80000000 2.80000000 4.80000000 1.40000000 [78,] 6.70000000 3.00000000 5.00000000 1.70000000 [79,] 6.00000000 2.90000000 4.50000000 1.50000000 [80,] 5.70000000 2.60000000 3.50000000 1.00000000 [81,] 5.50000000 2.40000000 3.80000000 1.10000000 [82,] 5.50000000 2.40000000 3.70000000 1.00000000 [83,] 5.80000000 2.70000000 3.90000000 1.20000000 [84,] 6.00000000 2.70000000 5.10000000 1.60000000 [85,] 5.40000000 3.00000000 4.50000000 1.50000000 [86,] 6.00000000 3.40000000 4.50000000 1.60000000 [87,] 6.70000000 3.10000000 4.70000000 1.50000000 [88,] 6.30000000 2.30000000 4.40000000 1.30000000 [89,] 5.60000000 3.00000000 4.10000000 1.30000000 [90,] 5.50000000 2.50000000 4.00000000 1.30000000 [91,] 5.50000000 2.60000000 4.40000000 1.20000000 [92,] 6.10000000 3.00000000 4.60000000 1.40000000 [93,] 5.80000000 2.60000000 4.00000000 1.20000000 [94,] 5.00000000 2.30000000 3.30000000 1.00000000 [95,] 5.60000000 2.70000000 4.20000000 1.30000000 [96,] 5.70000000 3.00000000 4.20000000 1.20000000 [97,] 5.70000000 2.90000000 4.20000000 1.30000000 [98,] 6.20000000 2.90000000 4.30000000 1.30000000 [99,] 5.10000000 2.50000000 3.00000000 1.10000000 [100,] 5.70000000 2.80000000 4.10000000 1.30000000 [101,] 6.30000000 3.30000000 6.00000000 2.50000000 [102,] 5.80000000 2.70000000 5.10000000 1.90000000 [103,] 7.10000000 3.00000000 5.90000000 2.10000000 [104,] 6.30000000 2.90000000 5.60000000 1.80000000 [105,] 6.50000000 3.00000000 5.80000000 2.20000000 [106,] 7.60000000 3.00000000 6.60000000 2.10000000 [107,] 4.90000000 2.50000000 4.50000000 1.70000000 [108,] 7.30000000 2.90000000 6.30000000 1.80000000 [109,] 6.70000000 2.50000000 5.80000000 1.80000000 [110,] 7.20000000 3.60000000 6.10000000 2.50000000 [111,] 6.50000000 3.20000000 5.10000000 2.00000000 [112,] 6.40000000 2.70000000 5.30000000 1.90000000 [113,] 6.80000000 3.00000000 5.50000000 2.10000000 [114,] 5.70000000 2.50000000 5.00000000 2.00000000 [115,] 5.80000000 2.80000000 5.10000000 2.40000000 [116,] 6.40000000 3.20000000 5.30000000 2.30000000 [117,] 6.50000000 3.00000000 5.50000000 1.80000000 [118,] 7.70000000 3.80000000 6.70000000 2.20000000 [119,] 7.70000000 2.60000000 6.90000000 2.30000000 [120,] 6.00000000 2.20000000 5.00000000 1.50000000 [121,] 6.90000000 3.20000000 5.70000000 2.30000000 [122,] 5.60000000 2.80000000 4.90000000 2.00000000 [123,] 7.70000000 2.80000000 6.70000000 2.00000000 [124,] 6.30000000 2.70000000 4.90000000 1.80000000 [125,] 6.70000000 3.30000000 5.70000000 2.10000000 [126,] 7.20000000 3.20000000 6.00000000 1.80000000 [127,] 6.20000000 2.80000000 4.80000000 1.80000000 [128,] 6.10000000 3.00000000 4.90000000 1.80000000 [129,] 6.40000000 2.80000000 5.60000000 2.10000000 [130,] 7.20000000 3.00000000 5.80000000 1.60000000 [131,] 7.40000000 2.80000000 6.10000000 1.90000000 [132,] 7.90000000 3.80000000 6.40000000 2.00000000 [133,] 6.40000000 2.80000000 5.60000000 2.20000000 [134,] 6.30000000 3.91766676 5.10000000 1.50000000 [135,] 6.10000000 2.60000000 5.60000000 1.40000000 [136,] 7.70000000 3.00000000 6.10000000 2.30000000 [137,] 6.30000000 3.40000000 5.60000000 2.40000000 [138,] 6.40000000 3.10000000 5.50000000 1.80000000 [139,] 6.00000000 3.00000000 4.80000000 1.80000000 [140,] 6.90000000 3.10000000 5.40000000 2.10000000 [141,] 6.70000000 3.10000000 5.60000000 2.40000000 [142,] 6.90000000 3.10000000 5.10000000 2.30000000 [143,] 5.80000000 2.70000000 5.10000000 1.90000000 [144,] 6.80000000 3.20000000 5.90000000 2.30000000 [145,] 6.70000000 3.30000000 5.70000000 2.50000000 [146,] 6.70000000 3.00000000 5.20000000 2.30000000 [147,] 6.30000000 2.50000000 5.00000000 1.90000000 [148,] 6.50000000 3.00000000 5.20000000 2.00000000 [149,] 6.20000000 3.40000000 5.40000000 2.30000000 [150,] 5.90000000 3.00000000 5.10000000 1.80000000 * missing = row col [1,] 56 1 [2,] 20 2 [3,] 72 2 [4,] 134 2 [5,] 4 4 * nbSample = 150 * nbCluster = 3 * lnLikelihood = -1034.532 * nbFreeParameter= 70 * criterion name = ICL * criterion value= 2427.142 * 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 3.4197886 1.4620000 0.2413297 * S.D. = 0.5142237 0.3436395 0.4260094 0.2038222 **************************************** *** Cluster: 2 * Proportion = 0.3333333 * Means = 5.923151 2.774218 4.260000 1.326000 * S.D. = 0.5142237 0.3436395 0.4260094 0.2038222 **************************************** *** Cluster: 3 * Proportion = 0.3333333 * Means = 6.588000 2.996353 5.552000 2.026000 * S.D. = 0.5142237 0.3436395 0.4260094 0.2038222 **************************************** > 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 -0.03351271 [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 3.38943132 1.50000000 0.30000000 [21,] 5.40000000 3.40000000 1.70000000 0.20000000 [22,] 5.10000000 3.70000000 1.50000000 0.40000000 [23,] 4.60000000 3.60000000 1.00000000 0.20000000 [24,] 5.10000000 3.30000000 1.70000000 0.50000000 [25,] 4.80000000 3.40000000 1.90000000 0.20000000 [26,] 5.00000000 3.00000000 1.60000000 0.20000000 [27,] 5.00000000 3.40000000 1.60000000 0.40000000 [28,] 5.20000000 3.50000000 1.50000000 0.20000000 [29,] 5.20000000 3.40000000 1.40000000 0.20000000 [30,] 4.70000000 3.20000000 1.60000000 0.20000000 [31,] 4.80000000 3.10000000 1.60000000 0.20000000 [32,] 5.40000000 3.40000000 1.50000000 0.40000000 [33,] 5.20000000 4.10000000 1.50000000 0.10000000 [34,] 5.50000000 4.20000000 1.40000000 0.20000000 [35,] 4.90000000 3.10000000 1.50000000 0.20000000 [36,] 5.00000000 3.20000000 1.20000000 0.20000000 [37,] 5.50000000 3.50000000 1.30000000 0.20000000 [38,] 4.90000000 3.60000000 1.40000000 0.10000000 [39,] 4.40000000 3.00000000 1.30000000 0.20000000 [40,] 5.10000000 3.40000000 1.50000000 0.20000000 [41,] 5.00000000 3.50000000 1.30000000 0.30000000 [42,] 4.50000000 2.30000000 1.30000000 0.30000000 [43,] 4.40000000 3.20000000 1.30000000 0.20000000 [44,] 5.00000000 3.50000000 1.60000000 0.60000000 [45,] 5.10000000 3.80000000 1.90000000 0.40000000 [46,] 4.80000000 3.00000000 1.40000000 0.30000000 [47,] 5.10000000 3.80000000 1.60000000 0.20000000 [48,] 4.60000000 3.20000000 1.40000000 0.20000000 [49,] 5.30000000 3.70000000 1.50000000 0.20000000 [50,] 5.00000000 3.30000000 1.40000000 0.20000000 [51,] 7.00000000 3.20000000 4.70000000 1.40000000 [52,] 6.40000000 3.20000000 4.50000000 1.50000000 [53,] 6.90000000 3.10000000 4.90000000 1.50000000 [54,] 5.50000000 2.30000000 4.00000000 1.30000000 [55,] 6.50000000 2.80000000 4.60000000 1.50000000 [56,] 5.05756247 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 3.01088148 4.00000000 1.30000000 [73,] 6.30000000 2.50000000 4.90000000 1.50000000 [74,] 6.10000000 2.80000000 4.70000000 1.20000000 [75,] 6.40000000 2.90000000 4.30000000 1.30000000 [76,] 6.60000000 3.00000000 4.40000000 1.40000000 [77,] 6.80000000 2.80000000 4.80000000 1.40000000 [78,] 6.70000000 3.00000000 5.00000000 1.70000000 [79,] 6.00000000 2.90000000 4.50000000 1.50000000 [80,] 5.70000000 2.60000000 3.50000000 1.00000000 [81,] 5.50000000 2.40000000 3.80000000 1.10000000 [82,] 5.50000000 2.40000000 3.70000000 1.00000000 [83,] 5.80000000 2.70000000 3.90000000 1.20000000 [84,] 6.00000000 2.70000000 5.10000000 1.60000000 [85,] 5.40000000 3.00000000 4.50000000 1.50000000 [86,] 6.00000000 3.40000000 4.50000000 1.60000000 [87,] 6.70000000 3.10000000 4.70000000 1.50000000 [88,] 6.30000000 2.30000000 4.40000000 1.30000000 [89,] 5.60000000 3.00000000 4.10000000 1.30000000 [90,] 5.50000000 2.50000000 4.00000000 1.30000000 [91,] 5.50000000 2.60000000 4.40000000 1.20000000 [92,] 6.10000000 3.00000000 4.60000000 1.40000000 [93,] 5.80000000 2.60000000 4.00000000 1.20000000 [94,] 5.00000000 2.30000000 3.30000000 1.00000000 [95,] 5.60000000 2.70000000 4.20000000 1.30000000 [96,] 5.70000000 3.00000000 4.20000000 1.20000000 [97,] 5.70000000 2.90000000 4.20000000 1.30000000 [98,] 6.20000000 2.90000000 4.30000000 1.30000000 [99,] 5.10000000 2.50000000 3.00000000 1.10000000 [100,] 5.70000000 2.80000000 4.10000000 1.30000000 [101,] 6.30000000 3.30000000 6.00000000 2.50000000 [102,] 5.80000000 2.70000000 5.10000000 1.90000000 [103,] 7.10000000 3.00000000 5.90000000 2.10000000 [104,] 6.30000000 2.90000000 5.60000000 1.80000000 [105,] 6.50000000 3.00000000 5.80000000 2.20000000 [106,] 7.60000000 3.00000000 6.60000000 2.10000000 [107,] 4.90000000 2.50000000 4.50000000 1.70000000 [108,] 7.30000000 2.90000000 6.30000000 1.80000000 [109,] 6.70000000 2.50000000 5.80000000 1.80000000 [110,] 7.20000000 3.60000000 6.10000000 2.50000000 [111,] 6.50000000 3.20000000 5.10000000 2.00000000 [112,] 6.40000000 2.70000000 5.30000000 1.90000000 [113,] 6.80000000 3.00000000 5.50000000 2.10000000 [114,] 5.70000000 2.50000000 5.00000000 2.00000000 [115,] 5.80000000 2.80000000 5.10000000 2.40000000 [116,] 6.40000000 3.20000000 5.30000000 2.30000000 [117,] 6.50000000 3.00000000 5.50000000 1.80000000 [118,] 7.70000000 3.80000000 6.70000000 2.20000000 [119,] 7.70000000 2.60000000 6.90000000 2.30000000 [120,] 6.00000000 2.20000000 5.00000000 1.50000000 [121,] 6.90000000 3.20000000 5.70000000 2.30000000 [122,] 5.60000000 2.80000000 4.90000000 2.00000000 [123,] 7.70000000 2.80000000 6.70000000 2.00000000 [124,] 6.30000000 2.70000000 4.90000000 1.80000000 [125,] 6.70000000 3.30000000 5.70000000 2.10000000 [126,] 7.20000000 3.20000000 6.00000000 1.80000000 [127,] 6.20000000 2.80000000 4.80000000 1.80000000 [128,] 6.10000000 3.00000000 4.90000000 1.80000000 [129,] 6.40000000 2.80000000 5.60000000 2.10000000 [130,] 7.20000000 3.00000000 5.80000000 1.60000000 [131,] 7.40000000 2.80000000 6.10000000 1.90000000 [132,] 7.90000000 3.80000000 6.40000000 2.00000000 [133,] 6.40000000 2.80000000 5.60000000 2.20000000 [134,] 6.30000000 3.91766676 5.10000000 1.50000000 [135,] 6.10000000 2.60000000 5.60000000 1.40000000 [136,] 7.70000000 3.00000000 6.10000000 2.30000000 [137,] 6.30000000 3.40000000 5.60000000 2.40000000 [138,] 6.40000000 3.10000000 5.50000000 1.80000000 [139,] 6.00000000 3.00000000 4.80000000 1.80000000 [140,] 6.90000000 3.10000000 5.40000000 2.10000000 [141,] 6.70000000 3.10000000 5.60000000 2.40000000 [142,] 6.90000000 3.10000000 5.10000000 2.30000000 [143,] 5.80000000 2.70000000 5.10000000 1.90000000 [144,] 6.80000000 3.20000000 5.90000000 2.30000000 [145,] 6.70000000 3.30000000 5.70000000 2.50000000 [146,] 6.70000000 3.00000000 5.20000000 2.30000000 [147,] 6.30000000 2.50000000 5.00000000 1.90000000 [148,] 6.50000000 3.00000000 5.20000000 2.00000000 [149,] 6.20000000 3.40000000 5.40000000 2.30000000 [150,] 5.90000000 3.00000000 5.10000000 1.80000000 * missing = row col * nbSample = 150 * nbCluster = 3 * lnLikelihood = -1047.738 * nbFreeParameter= 70 * criterion name = ICL * criterion value= 2452.309 * 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 3.4197886 1.4620000 0.2413297 * S.D. = 0.3489470 0.3714978 0.1719186 0.1112763 **************************************** *** Cluster: 2 * Proportion = 0.3333333 * Means = 5.923151 2.774218 4.260000 1.326000 * S.D. = 0.5246504 0.3124495 0.4651881 0.1957652 **************************************** *** Cluster: 3 * Proportion = 0.3333333 * Means = 6.588000 2.996353 5.552000 2.026000 * S.D. = 0.6294887 0.3444255 0.5463479 0.2718897 **************************************** > model <- learnGamma( data=x, labels= z, + , models = clusterGammaNames(prop = "equal") + , algo = "simul", nbIter = 2, epsilon = 1e-08 + ) * checking for unstated dependencies in vignettes ... OK * checking package vignettes in 'inst/doc' ... OK * checking re-building of vignette outputs ... [34s] OK * checking PDF version of manual ... OK * DONE Status: 1 ERROR, 1 NOTE