- using R version 4.3.1 (2023-06-16)
- using platform: x86_64-pc-linux-gnu (64-bit)
- R was compiled by
gcc-12 (Debian 12.3.0-9) 12.3.0
GNU Fortran (Debian 12.3.0-9) 12.3.0
- running under: Debian GNU/Linux trixie/sid
- using session charset: UTF-8
- 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 executable files ... OK
- checking for hidden files and directories ... OK
- checking for portable file names ... OK
- checking for sufficient/correct file permissions ... OK
- checking serialization versions ... OK
- checking whether package ‘MixAll’ can be installed ... OK
See the install log for details.
- used C compiler: ‘gcc-12 (Debian 12.3.0-9) 12.3.0’
- used C++ compiler: ‘g++-12 (Debian 12.3.0-9) 12.3.0’
- checking C++ specification ... NOTE
Specified C++11: please drop specification unless essential
- checking package directory ... OK
- checking for future file timestamps ... 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
- checking whether the package can be loaded ... [1s/1s] OK
- checking whether the package can be loaded with stated dependencies ... [1s/1s] OK
- checking whether the package can be unloaded cleanly ... [1s/1s] OK
- checking whether the namespace can be loaded with stated dependencies ... [1s/1s] OK
- checking whether the namespace can be unloaded cleanly ... [1s/1s] OK
- checking loading without being on the library search path ... [1s/1s] 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 ... [15s/19s] OK
- checking Rd files ... [1s/1s] OK
- checking Rd metadata ... OK
- checking Rd line widths ... 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 ... [0s/1s] 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 use of PKG_*FLAGS in Makefiles ... OK
- checking use of SHLIB_OPENMP_*FLAGS in Makefiles ... OK
- checking include directives in Makefiles ... OK
- checking pragmas in C/C++ headers and code ... OK
- checking compilation flags used ... 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 ... [9s/13s] OK
Examples with CPU (user + system) or elapsed time > 5s
user system elapsed
kmm 4.228 0 5.777
- checking for unstated dependencies in ‘tests’ ... OK
- checking tests ... [16s/22s] ERROR
Running ‘ClusterSimul.R’ [0s/1s]
Running ‘clusterDiagGaussianLikelihood.R’ [1s/1s]
Running ‘clusterGammaLikelihood.R’ [1s/2s]
Running ‘simulHeterogeneous.R’ [0s/1s]
Running ‘simulNonLinear.R’ [1s/1s]
Running ‘testAllLearners.R’ [1s/1s]
Running ‘testPoissonExample.R’ [1s/2s]
Running ‘testPredict.R’ [10s/13s]
Running the tests in ‘tests/testAllLearners.R’ failed.
Complete output:
> library(MixAll)
Loading required package: rtkore
Loading required package: Rcpp
Attaching package: 'rtkore'
The following object is masked from 'package:Rcpp':
LdFlags
> ## get data and target from iris data set
> data(iris)
> x <- as.matrix(iris[,1:4]); z <- as.vector(iris[,5]); n <- nrow(x); p <- ncol(x)
> ## add missing values at random
> indexes <- matrix(c(round(runif(5,1,n)), round(runif(5,1,p))), ncol=2)
> cbind(indexes, x[indexes])
[,1] [,2] [,3]
[1,] 129 1 6.4
[2,] 66 3 4.4
[3,] 15 4 0.2
[4,] 132 1 7.9
[5,] 28 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
1 129 1 6.775399
2 132 1 6.834306
3 66 3 3.806706
4 15 4 1.029148
5 28 4 -0.906886
> print(model)
****************************************
* model name = gaussian_p_s
* 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 2.900000 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 1.029148
[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,] 5.100000 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 -0.906886
[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 3.100000 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 3.806706 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,] 6.775399 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,] 6.834306 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 3.300000 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 2.300000
[150,] 5.900000 3.000000 5.100000 1.800000
* missing =
row col
[1,] 129 1
[2,] 132 1
[3,] 66 3
[4,] 15 4
[5,] 28 4
* nbSample = 150
* nbCluster = 3
* lnLikelihood = -1028.575
* nbFreeParameter= 70
* criterion name = ICL
* criterion value= 2415.098
* zi =
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[38] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[149] 2 2
****************************************
*** Cluster: 1
* Proportion = 0.3333333
* Means = 5.0060000 3.4280000 1.4620000 0.2404452
* S.D. = 0.3867439 0.3867439 0.3867439 0.3867439
****************************************
*** Cluster: 2
* Proportion = 0.3333333
* Means = 5.936000 2.770000 4.248134 1.326000
* S.D. = 0.3867439 0.3867439 0.3867439 0.3867439
****************************************
*** Cluster: 3
* Proportion = 0.3333333
* Means = 6.574194 2.974000 5.552000 2.026000
* S.D. = 0.3867439 0.3867439 0.3867439 0.3867439
****************************************
> 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 2.900000 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 1.029148
[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,] 5.100000 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 -0.906886
[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 3.100000 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 3.806706 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,] 6.775399 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,] 6.834306 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 3.300000 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 2.300000
[150,] 5.900000 3.000000 5.100000 1.800000
* missing =
row col
* nbSample = 150
* nbCluster = 3
* lnLikelihood = -1087.91
* nbFreeParameter= 70
* criterion name = ICL
* criterion value= 2534.881
* zi =
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[38] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[149] 2 2
****************************************
*** Cluster: 1
* Proportion = 0.3333333
* Means = 5.0060000 3.4280000 1.4620000 0.2404452
* S.D. = 0.3489470 0.3752546 0.1719186 0.2227506
****************************************
*** Cluster: 2
* Proportion = 0.3333333
* Means = 5.936000 2.770000 4.248134 1.326000
* S.D. = 0.5109834 0.3106445 0.4690167 0.1957652
****************************************
*** Cluster: 3
* Proportion = 0.3333333
* Means = 6.574194 2.974000 5.552000 2.026000
* S.D. = 0.6023681 0.3192554 0.5463479 0.2718897
****************************************
> model <- learnGamma( data=x, labels= z,
+ , models = clusterGammaNames(prop = "equal")
+ , algo = "simul", nbIter = 2, epsilon = 1e-08
+ )
*** caught segfault ***
address 0xe0, cause 'memory not mapped'
Traceback:
1: learnGamma(data = x, labels = z, , models = clusterGammaNames(prop = "equal"), algo = "simul", nbIter = 2, epsilon = 1e-08)
An irrecoverable exception occurred. R is aborting now ...
Segmentation fault
- checking for unstated dependencies in vignettes ... OK
- checking package vignettes in ‘inst/doc’ ... OK
- checking re-building of vignette outputs ... [21s/32s] OK
- checking PDF version of manual ... [8s/13s] OK
- checking HTML version of manual ... [3s/7s] OK
- checking for non-standard things in the check directory ... OK
- DONE
Status: 1 ERROR, 1 NOTE