- 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' ... [15s] OK
- running examples for arch 'x64' ... [14s] OK
- checking for unstated dependencies in 'tests' ... OK
- checking tests ...
- running tests for arch 'i386' ... [18s] 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' [2s]
Running 'testPredict.R' [10s]
- running tests for arch 'x64' ... [17s] 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' [2s]
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,] 1 3 1.4
[2,] 59 3 4.6
[3,] 22 2 3.7
[4,] 12 3 1.6
[5,] 11 4 0.2
> x[indexes] <- NA
> ## learn continuous model
> model <- learnDiagGaussian( data=x, labels= z, prop = c(1/3,1/3,1/3)
+ , models = clusterDiagGaussianNames(prop = "equal")
+ , algo = "simul", nbIter = 2, epsilon = 1e-08
+ )
> missingValues(model)
row col value
1 22 2 2.83124151
2 1 3 1.29522460
3 12 3 1.08062155
4 59 3 4.51286262
5 11 4 -0.06608789
> print(model)
****************************************
* model name = gaussian_p_sj
* data =
Sepal.Length Sepal.Width Petal.Length Petal.Width
[1,] 5.10000000 3.50000000 1.29522460 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 -0.06608789
[12,] 4.80000000 3.40000000 1.08062155 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 2.83124151 1.50000000 0.40000000
[23,] 4.60000000 3.60000000 1.00000000 0.20000000
[24,] 5.10000000 3.30000000 1.70000000 0.50000000
[25,] 4.80000000 3.40000000 1.90000000 0.20000000
[26,] 5.00000000 3.00000000 1.60000000 0.20000000
[27,] 5.00000000 3.40000000 1.60000000 0.40000000
[28,] 5.20000000 3.50000000 1.50000000 0.20000000
[29,] 5.20000000 3.40000000 1.40000000 0.20000000
[30,] 4.70000000 3.20000000 1.60000000 0.20000000
[31,] 4.80000000 3.10000000 1.60000000 0.20000000
[32,] 5.40000000 3.40000000 1.50000000 0.40000000
[33,] 5.20000000 4.10000000 1.50000000 0.10000000
[34,] 5.50000000 4.20000000 1.40000000 0.20000000
[35,] 4.90000000 3.10000000 1.50000000 0.20000000
[36,] 5.00000000 3.20000000 1.20000000 0.20000000
[37,] 5.50000000 3.50000000 1.30000000 0.20000000
[38,] 4.90000000 3.60000000 1.40000000 0.10000000
[39,] 4.40000000 3.00000000 1.30000000 0.20000000
[40,] 5.10000000 3.40000000 1.50000000 0.20000000
[41,] 5.00000000 3.50000000 1.30000000 0.30000000
[42,] 4.50000000 2.30000000 1.30000000 0.30000000
[43,] 4.40000000 3.20000000 1.30000000 0.20000000
[44,] 5.00000000 3.50000000 1.60000000 0.60000000
[45,] 5.10000000 3.80000000 1.90000000 0.40000000
[46,] 4.80000000 3.00000000 1.40000000 0.30000000
[47,] 5.10000000 3.80000000 1.60000000 0.20000000
[48,] 4.60000000 3.20000000 1.40000000 0.20000000
[49,] 5.30000000 3.70000000 1.50000000 0.20000000
[50,] 5.00000000 3.30000000 1.40000000 0.20000000
[51,] 7.00000000 3.20000000 4.70000000 1.40000000
[52,] 6.40000000 3.20000000 4.50000000 1.50000000
[53,] 6.90000000 3.10000000 4.90000000 1.50000000
[54,] 5.50000000 2.30000000 4.00000000 1.30000000
[55,] 6.50000000 2.80000000 4.60000000 1.50000000
[56,] 5.70000000 2.80000000 4.50000000 1.30000000
[57,] 6.30000000 3.30000000 4.70000000 1.60000000
[58,] 4.90000000 2.40000000 3.30000000 1.00000000
[59,] 6.60000000 2.90000000 4.51286262 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 1.30000000
[99,] 5.10000000 2.50000000 3.00000000 1.10000000
[100,] 5.70000000 2.80000000 4.10000000 1.30000000
[101,] 6.30000000 3.30000000 6.00000000 2.50000000
[102,] 5.80000000 2.70000000 5.10000000 1.90000000
[103,] 7.10000000 3.00000000 5.90000000 2.10000000
[104,] 6.30000000 2.90000000 5.60000000 1.80000000
[105,] 6.50000000 3.00000000 5.80000000 2.20000000
[106,] 7.60000000 3.00000000 6.60000000 2.10000000
[107,] 4.90000000 2.50000000 4.50000000 1.70000000
[108,] 7.30000000 2.90000000 6.30000000 1.80000000
[109,] 6.70000000 2.50000000 5.80000000 1.80000000
[110,] 7.20000000 3.60000000 6.10000000 2.50000000
[111,] 6.50000000 3.20000000 5.10000000 2.00000000
[112,] 6.40000000 2.70000000 5.30000000 1.90000000
[113,] 6.80000000 3.00000000 5.50000000 2.10000000
[114,] 5.70000000 2.50000000 5.00000000 2.00000000
[115,] 5.80000000 2.80000000 5.10000000 2.40000000
[116,] 6.40000000 3.20000000 5.30000000 2.30000000
[117,] 6.50000000 3.00000000 5.50000000 1.80000000
[118,] 7.70000000 3.80000000 6.70000000 2.20000000
[119,] 7.70000000 2.60000000 6.90000000 2.30000000
[120,] 6.00000000 2.20000000 5.00000000 1.50000000
[121,] 6.90000000 3.20000000 5.70000000 2.30000000
[122,] 5.60000000 2.80000000 4.90000000 2.00000000
[123,] 7.70000000 2.80000000 6.70000000 2.00000000
[124,] 6.30000000 2.70000000 4.90000000 1.80000000
[125,] 6.70000000 3.30000000 5.70000000 2.10000000
[126,] 7.20000000 3.20000000 6.00000000 1.80000000
[127,] 6.20000000 2.80000000 4.80000000 1.80000000
[128,] 6.10000000 3.00000000 4.90000000 1.80000000
[129,] 6.40000000 2.80000000 5.60000000 2.10000000
[130,] 7.20000000 3.00000000 5.80000000 1.60000000
[131,] 7.40000000 2.80000000 6.10000000 1.90000000
[132,] 7.90000000 3.80000000 6.40000000 2.00000000
[133,] 6.40000000 2.80000000 5.60000000 2.20000000
[134,] 6.30000000 2.80000000 5.10000000 1.50000000
[135,] 6.10000000 2.60000000 5.60000000 1.40000000
[136,] 7.70000000 3.00000000 6.10000000 2.30000000
[137,] 6.30000000 3.40000000 5.60000000 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,] 22 2
[2,] 1 3
[3,] 12 3
[4,] 59 3
[5,] 11 4
* nbSample = 150
* nbCluster = 3
* lnLikelihood = -1037.496
* nbFreeParameter= 70
* criterion name = ICL
* criterion value= 2433.029
* 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 3.4106248 1.4495169 0.2406782
* S.D. = 0.5096155 0.3389104 0.4267190 0.2041318
****************************************
*** Cluster: 2
* Proportion = 0.3333333
* Means = 5.936000 2.770000 4.258257 1.326000
* S.D. = 0.5096155 0.3389104 0.4267190 0.2041318
****************************************
*** Cluster: 3
* Proportion = 0.3333333
* Means = 6.588 2.974 5.552 2.026
* S.D. = 0.5096155 0.3389104 0.4267190 0.2041318
****************************************
> 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 1.29522460 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 -0.06608789
[12,] 4.80000000 3.40000000 1.08062155 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 2.83124151 1.50000000 0.40000000
[23,] 4.60000000 3.60000000 1.00000000 0.20000000
[24,] 5.10000000 3.30000000 1.70000000 0.50000000
[25,] 4.80000000 3.40000000 1.90000000 0.20000000
[26,] 5.00000000 3.00000000 1.60000000 0.20000000
[27,] 5.00000000 3.40000000 1.60000000 0.40000000
[28,] 5.20000000 3.50000000 1.50000000 0.20000000
[29,] 5.20000000 3.40000000 1.40000000 0.20000000
[30,] 4.70000000 3.20000000 1.60000000 0.20000000
[31,] 4.80000000 3.10000000 1.60000000 0.20000000
[32,] 5.40000000 3.40000000 1.50000000 0.40000000
[33,] 5.20000000 4.10000000 1.50000000 0.10000000
[34,] 5.50000000 4.20000000 1.40000000 0.20000000
[35,] 4.90000000 3.10000000 1.50000000 0.20000000
[36,] 5.00000000 3.20000000 1.20000000 0.20000000
[37,] 5.50000000 3.50000000 1.30000000 0.20000000
[38,] 4.90000000 3.60000000 1.40000000 0.10000000
[39,] 4.40000000 3.00000000 1.30000000 0.20000000
[40,] 5.10000000 3.40000000 1.50000000 0.20000000
[41,] 5.00000000 3.50000000 1.30000000 0.30000000
[42,] 4.50000000 2.30000000 1.30000000 0.30000000
[43,] 4.40000000 3.20000000 1.30000000 0.20000000
[44,] 5.00000000 3.50000000 1.60000000 0.60000000
[45,] 5.10000000 3.80000000 1.90000000 0.40000000
[46,] 4.80000000 3.00000000 1.40000000 0.30000000
[47,] 5.10000000 3.80000000 1.60000000 0.20000000
[48,] 4.60000000 3.20000000 1.40000000 0.20000000
[49,] 5.30000000 3.70000000 1.50000000 0.20000000
[50,] 5.00000000 3.30000000 1.40000000 0.20000000
[51,] 7.00000000 3.20000000 4.70000000 1.40000000
[52,] 6.40000000 3.20000000 4.50000000 1.50000000
[53,] 6.90000000 3.10000000 4.90000000 1.50000000
[54,] 5.50000000 2.30000000 4.00000000 1.30000000
[55,] 6.50000000 2.80000000 4.60000000 1.50000000
[56,] 5.70000000 2.80000000 4.50000000 1.30000000
[57,] 6.30000000 3.30000000 4.70000000 1.60000000
[58,] 4.90000000 2.40000000 3.30000000 1.00000000
[59,] 6.60000000 2.90000000 4.51286262 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 1.30000000
[99,] 5.10000000 2.50000000 3.00000000 1.10000000
[100,] 5.70000000 2.80000000 4.10000000 1.30000000
[101,] 6.30000000 3.30000000 6.00000000 2.50000000
[102,] 5.80000000 2.70000000 5.10000000 1.90000000
[103,] 7.10000000 3.00000000 5.90000000 2.10000000
[104,] 6.30000000 2.90000000 5.60000000 1.80000000
[105,] 6.50000000 3.00000000 5.80000000 2.20000000
[106,] 7.60000000 3.00000000 6.60000000 2.10000000
[107,] 4.90000000 2.50000000 4.50000000 1.70000000
[108,] 7.30000000 2.90000000 6.30000000 1.80000000
[109,] 6.70000000 2.50000000 5.80000000 1.80000000
[110,] 7.20000000 3.60000000 6.10000000 2.50000000
[111,] 6.50000000 3.20000000 5.10000000 2.00000000
[112,] 6.40000000 2.70000000 5.30000000 1.90000000
[113,] 6.80000000 3.00000000 5.50000000 2.10000000
[114,] 5.70000000 2.50000000 5.00000000 2.00000000
[115,] 5.80000000 2.80000000 5.10000000 2.40000000
[116,] 6.40000000 3.20000000 5.30000000 2.30000000
[117,] 6.50000000 3.00000000 5.50000000 1.80000000
[118,] 7.70000000 3.80000000 6.70000000 2.20000000
[119,] 7.70000000 2.60000000 6.90000000 2.30000000
[120,] 6.00000000 2.20000000 5.00000000 1.50000000
[121,] 6.90000000 3.20000000 5.70000000 2.30000000
[122,] 5.60000000 2.80000000 4.90000000 2.00000000
[123,] 7.70000000 2.80000000 6.70000000 2.00000000
[124,] 6.30000000 2.70000000 4.90000000 1.80000000
[125,] 6.70000000 3.30000000 5.70000000 2.10000000
[126,] 7.20000000 3.20000000 6.00000000 1.80000000
[127,] 6.20000000 2.80000000 4.80000000 1.80000000
[128,] 6.10000000 3.00000000 4.90000000 1.80000000
[129,] 6.40000000 2.80000000 5.60000000 2.10000000
[130,] 7.20000000 3.00000000 5.80000000 1.60000000
[131,] 7.40000000 2.80000000 6.10000000 1.90000000
[132,] 7.90000000 3.80000000 6.40000000 2.00000000
[133,] 6.40000000 2.80000000 5.60000000 2.20000000
[134,] 6.30000000 2.80000000 5.10000000 1.50000000
[135,] 6.10000000 2.60000000 5.60000000 1.40000000
[136,] 7.70000000 3.00000000 6.10000000 2.30000000
[137,] 6.30000000 3.40000000 5.60000000 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 = -1038.367
* nbFreeParameter= 70
* criterion name = ICL
* criterion value= 2434.776
* 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 3.4106248 1.4495169 0.2406782
* S.D. = 0.3489470 0.3823046 0.1800213 0.1129661
****************************************
*** Cluster: 2
* Proportion = 0.3333333
* Means = 5.936000 2.770000 4.258257 1.326000
* S.D. = 0.5109834 0.3106445 0.4640730 0.1957652
****************************************
*** Cluster: 3
* Proportion = 0.3333333
* Means = 6.588 2.974 5.552 2.026
* S.D. = 0.6294887 0.3192554 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 ... [35s] OK
- checking PDF version of manual ... OK
- DONE
Status: 1 ERROR, 1 NOTE