Last updated on 2019-12-13 07:52:30 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.1 | 648.26 | 95.82 | 744.08 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.1 | 479.33 | 77.31 | 556.64 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.1 | 1093.55 | NOTE | |||
| r-devel-linux-x86_64-fedora-gcc | 1.5.1 | 1110.78 | OK | |||
| r-devel-windows-ix86+x86_64 | 1.5.1 | 1333.00 | 169.00 | 1502.00 | ERROR | |
| r-devel-windows-ix86+x86_64-gcc8 | 1.5.1 | 1353.00 | 240.00 | 1593.00 | NOTE | |
| r-patched-linux-x86_64 | 1.5.1 | 559.95 | 89.76 | 649.71 | OK | |
| r-patched-solaris-x86 | 1.5.1 | 888.00 | OK | |||
| r-release-linux-x86_64 | 1.5.1 | 553.62 | 87.88 | 641.50 | OK | |
| r-release-windows-ix86+x86_64 | 1.5.1 | 1341.00 | 169.00 | 1510.00 | NOTE | |
| r-release-osx-x86_64 | 1.5.1 | NOTE | ||||
| r-oldrel-windows-ix86+x86_64 | 1.5.1 | 1312.00 | 174.00 | 1486.00 | ERROR | |
| r-oldrel-osx-x86_64 | 1.5.1 | NOTE |
Version: 1.5.1
Check: installed package size
Result: NOTE
installed size is 40.1Mb
sub-directories of 1Mb or more:
libs 37.8Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-windows-ix86+x86_64, r-devel-windows-ix86+x86_64-gcc8, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64
Version: 1.5.1
Check: running tests for arch ‘i386’
Result: ERROR
Running 'ClusterSimul.R' [0s]
Running 'clusterDiagGaussianLikelihood.R' [1s]
Running 'clusterGammaLikelihood.R' [1s]
Running 'simulHeterogeneous.R' [0s]
Running 'simulNonLinear.R' [0s]
Running 'testAllLearners.R' [1s]
Running 'testPoissonExample.R' [1s]
Running 'testPredict.R' [8s]
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,] 12 4 0.2
[2,] 47 4 0.2
[3,] 72 2 2.8
[4,] 47 3 1.6
[5,] 137 4 2.4
> 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 72 2 2.73799795
2 47 3 1.36603653
3 12 4 -0.07200802
4 47 4 0.27170501
5 137 4 1.75461411
> print(model)
****************************************
* model name = gaussian_p_sk
* data =
Sepal.Length Sepal.Width Petal.Length Petal.Width
[1,] 5.10000000 3.50000000 1.40000000 0.20000000
[2,] 4.90000000 3.00000000 1.40000000 0.20000000
[3,] 4.70000000 3.20000000 1.30000000 0.20000000
[4,] 4.60000000 3.10000000 1.50000000 0.20000000
[5,] 5.00000000 3.60000000 1.40000000 0.20000000
[6,] 5.40000000 3.90000000 1.70000000 0.40000000
[7,] 4.60000000 3.40000000 1.40000000 0.30000000
[8,] 5.00000000 3.40000000 1.50000000 0.20000000
[9,] 4.40000000 2.90000000 1.40000000 0.20000000
[10,] 4.90000000 3.10000000 1.50000000 0.10000000
[11,] 5.40000000 3.70000000 1.50000000 0.20000000
[12,] 4.80000000 3.40000000 1.60000000 -0.07200802
[13,] 4.80000000 3.00000000 1.40000000 0.10000000
[14,] 4.30000000 3.00000000 1.10000000 0.10000000
[15,] 5.80000000 4.00000000 1.20000000 0.20000000
[16,] 5.70000000 4.40000000 1.50000000 0.40000000
[17,] 5.40000000 3.90000000 1.30000000 0.40000000
[18,] 5.10000000 3.50000000 1.40000000 0.30000000
[19,] 5.70000000 3.80000000 1.70000000 0.30000000
[20,] 5.10000000 3.80000000 1.50000000 0.30000000
[21,] 5.40000000 3.40000000 1.70000000 0.20000000
[22,] 5.10000000 3.70000000 1.50000000 0.40000000
[23,] 4.60000000 3.60000000 1.00000000 0.20000000
[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 1.36603653 0.27170501
[48,] 4.60000000 3.20000000 1.40000000 0.20000000
[49,] 5.30000000 3.70000000 1.50000000 0.20000000
[50,] 5.00000000 3.30000000 1.40000000 0.20000000
[51,] 7.00000000 3.20000000 4.70000000 1.40000000
[52,] 6.40000000 3.20000000 4.50000000 1.50000000
[53,] 6.90000000 3.10000000 4.90000000 1.50000000
[54,] 5.50000000 2.30000000 4.00000000 1.30000000
[55,] 6.50000000 2.80000000 4.60000000 1.50000000
[56,] 5.70000000 2.80000000 4.50000000 1.30000000
[57,] 6.30000000 3.30000000 4.70000000 1.60000000
[58,] 4.90000000 2.40000000 3.30000000 1.00000000
[59,] 6.60000000 2.90000000 4.60000000 1.30000000
[60,] 5.20000000 2.70000000 3.90000000 1.40000000
[61,] 5.00000000 2.00000000 3.50000000 1.00000000
[62,] 5.90000000 3.00000000 4.20000000 1.50000000
[63,] 6.00000000 2.20000000 4.00000000 1.00000000
[64,] 6.10000000 2.90000000 4.70000000 1.40000000
[65,] 5.60000000 2.90000000 3.60000000 1.30000000
[66,] 6.70000000 3.10000000 4.40000000 1.40000000
[67,] 5.60000000 3.00000000 4.50000000 1.50000000
[68,] 5.80000000 2.70000000 4.10000000 1.00000000
[69,] 6.20000000 2.20000000 4.50000000 1.50000000
[70,] 5.60000000 2.50000000 3.90000000 1.10000000
[71,] 5.90000000 3.20000000 4.80000000 1.80000000
[72,] 6.10000000 2.73799795 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 1.75461411
[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,] 72 2
[2,] 47 3
[3,] 12 4
[4,] 47 4
[5,] 137 4
* nbSample = 150
* nbCluster = 3
* lnLikelihood = -1031.707
* nbFreeParameter= 70
* criterion name = ICL
* criterion value= 2421.424
* 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 1.4573207 0.2419939
* S.D. = 0.2760147 0.2760147 0.2760147 0.2760147
****************************************
*** Cluster: 2
* Proportion = 0.3333333
* Means = 5.93600 2.76876 4.26000 1.32600
* S.D. = 0.3912573 0.3912573 0.3912573 0.3912573
****************************************
*** Cluster: 3
* Proportion = 0.3333333
* Means = 6.588000 2.974000 5.552000 2.013092
* S.D. = 0.46613 0.46613 0.46613 0.46613
****************************************
> model <- learnDiagGaussian( data=x, labels= z,
+ , models = clusterDiagGaussianNames(prop = "equal")
+ , algo = "impute", nbIter = 2, epsilon = 1e-08)
> missingValues(model)
row col value
> print(model)
****************************************
* model name = gaussian_p_sjk
* data =
Sepal.Length Sepal.Width Petal.Length Petal.Width
[1,] 5.10000000 3.50000000 1.40000000 0.20000000
[2,] 4.90000000 3.00000000 1.40000000 0.20000000
[3,] 4.70000000 3.20000000 1.30000000 0.20000000
[4,] 4.60000000 3.10000000 1.50000000 0.20000000
[5,] 5.00000000 3.60000000 1.40000000 0.20000000
[6,] 5.40000000 3.90000000 1.70000000 0.40000000
[7,] 4.60000000 3.40000000 1.40000000 0.30000000
[8,] 5.00000000 3.40000000 1.50000000 0.20000000
[9,] 4.40000000 2.90000000 1.40000000 0.20000000
[10,] 4.90000000 3.10000000 1.50000000 0.10000000
[11,] 5.40000000 3.70000000 1.50000000 0.20000000
[12,] 4.80000000 3.40000000 1.60000000 -0.07200802
[13,] 4.80000000 3.00000000 1.40000000 0.10000000
[14,] 4.30000000 3.00000000 1.10000000 0.10000000
[15,] 5.80000000 4.00000000 1.20000000 0.20000000
[16,] 5.70000000 4.40000000 1.50000000 0.40000000
[17,] 5.40000000 3.90000000 1.30000000 0.40000000
[18,] 5.10000000 3.50000000 1.40000000 0.30000000
[19,] 5.70000000 3.80000000 1.70000000 0.30000000
[20,] 5.10000000 3.80000000 1.50000000 0.30000000
[21,] 5.40000000 3.40000000 1.70000000 0.20000000
[22,] 5.10000000 3.70000000 1.50000000 0.40000000
[23,] 4.60000000 3.60000000 1.00000000 0.20000000
[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 1.36603653 0.27170501
[48,] 4.60000000 3.20000000 1.40000000 0.20000000
[49,] 5.30000000 3.70000000 1.50000000 0.20000000
[50,] 5.00000000 3.30000000 1.40000000 0.20000000
[51,] 7.00000000 3.20000000 4.70000000 1.40000000
[52,] 6.40000000 3.20000000 4.50000000 1.50000000
[53,] 6.90000000 3.10000000 4.90000000 1.50000000
[54,] 5.50000000 2.30000000 4.00000000 1.30000000
[55,] 6.50000000 2.80000000 4.60000000 1.50000000
[56,] 5.70000000 2.80000000 4.50000000 1.30000000
[57,] 6.30000000 3.30000000 4.70000000 1.60000000
[58,] 4.90000000 2.40000000 3.30000000 1.00000000
[59,] 6.60000000 2.90000000 4.60000000 1.30000000
[60,] 5.20000000 2.70000000 3.90000000 1.40000000
[61,] 5.00000000 2.00000000 3.50000000 1.00000000
[62,] 5.90000000 3.00000000 4.20000000 1.50000000
[63,] 6.00000000 2.20000000 4.00000000 1.00000000
[64,] 6.10000000 2.90000000 4.70000000 1.40000000
[65,] 5.60000000 2.90000000 3.60000000 1.30000000
[66,] 6.70000000 3.10000000 4.40000000 1.40000000
[67,] 5.60000000 3.00000000 4.50000000 1.50000000
[68,] 5.80000000 2.70000000 4.10000000 1.00000000
[69,] 6.20000000 2.20000000 4.50000000 1.50000000
[70,] 5.60000000 2.50000000 3.90000000 1.10000000
[71,] 5.90000000 3.20000000 4.80000000 1.80000000
[72,] 6.10000000 2.73799795 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 1.75461411
[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 = -1028.104
* nbFreeParameter= 70
* criterion name = ICL
* criterion value= 2414.36
* 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 1.4573207 0.2419939
* S.D. = 0.3489470 0.3752546 0.1712817 0.1132215
****************************************
*** Cluster: 2
* Proportion = 0.3333333
* Means = 5.93600 2.76876 4.26000 1.32600
* S.D. = 0.5109834 0.3106460 0.4651881 0.1957652
****************************************
*** Cluster: 3
* Proportion = 0.3333333
* Means = 6.588000 2.974000 5.552000 2.013092
* S.D. = 0.6294887 0.3192554 0.5463479 0.2691336
****************************************
> model <- learnGamma( data=x, labels= z,
+ , models = clusterGammaNames(prop = "equal")
+ , algo = "simul", nbIter = 2, epsilon = 1e-08
+ )
Flavor: r-devel-windows-ix86+x86_64
Version: 1.5.1
Check: running tests for arch ‘i386’
Result: 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' [9s]
Running the tests in 'tests/testAllLearners.R' failed.
Complete output:
> library(MixAll)
Loading required package: rtkore
Loading required package: Rcpp
Attaching package: 'rtkore'
The following object is masked from 'package:Rcpp':
LdFlags
> ## get data and target from iris data set
> data(iris)
> x <- as.matrix(iris[,1:4]); z <- as.vector(iris[,5]); n <- nrow(x); p <- ncol(x)
> ## add missing values at random
> indexes <- matrix(c(round(runif(5,1,n)), round(runif(5,1,p))), ncol=2)
> cbind(indexes, x[indexes])
[,1] [,2] [,3]
[1,] 98 2 2.9
[2,] 126 4 1.8
[3,] 9 4 0.2
[4,] 120 4 1.5
[5,] 8 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 98 2 2.91136488
2 8 4 -0.14681092
3 9 4 -0.06330799
4 120 4 1.60998473
5 126 4 1.77714576
> print(model)
****************************************
* model name = gaussian_p_s
* data =
Sepal.Length Sepal.Width Petal.Length Petal.Width
[1,] 5.10000000 3.50000000 1.40000000 0.20000000
[2,] 4.90000000 3.00000000 1.40000000 0.20000000
[3,] 4.70000000 3.20000000 1.30000000 0.20000000
[4,] 4.60000000 3.10000000 1.50000000 0.20000000
[5,] 5.00000000 3.60000000 1.40000000 0.20000000
[6,] 5.40000000 3.90000000 1.70000000 0.40000000
[7,] 4.60000000 3.40000000 1.40000000 0.30000000
[8,] 5.00000000 3.40000000 1.50000000 -0.14681092
[9,] 4.40000000 2.90000000 1.40000000 -0.06330799
[10,] 4.90000000 3.10000000 1.50000000 0.10000000
[11,] 5.40000000 3.70000000 1.50000000 0.20000000
[12,] 4.80000000 3.40000000 1.60000000 0.20000000
[13,] 4.80000000 3.00000000 1.40000000 0.10000000
[14,] 4.30000000 3.00000000 1.10000000 0.10000000
[15,] 5.80000000 4.00000000 1.20000000 0.20000000
[16,] 5.70000000 4.40000000 1.50000000 0.40000000
[17,] 5.40000000 3.90000000 1.30000000 0.40000000
[18,] 5.10000000 3.50000000 1.40000000 0.30000000
[19,] 5.70000000 3.80000000 1.70000000 0.30000000
[20,] 5.10000000 3.80000000 1.50000000 0.30000000
[21,] 5.40000000 3.40000000 1.70000000 0.20000000
[22,] 5.10000000 3.70000000 1.50000000 0.40000000
[23,] 4.60000000 3.60000000 1.00000000 0.20000000
[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 4.60000000 1.30000000
[60,] 5.20000000 2.70000000 3.90000000 1.40000000
[61,] 5.00000000 2.00000000 3.50000000 1.00000000
[62,] 5.90000000 3.00000000 4.20000000 1.50000000
[63,] 6.00000000 2.20000000 4.00000000 1.00000000
[64,] 6.10000000 2.90000000 4.70000000 1.40000000
[65,] 5.60000000 2.90000000 3.60000000 1.30000000
[66,] 6.70000000 3.10000000 4.40000000 1.40000000
[67,] 5.60000000 3.00000000 4.50000000 1.50000000
[68,] 5.80000000 2.70000000 4.10000000 1.00000000
[69,] 6.20000000 2.20000000 4.50000000 1.50000000
[70,] 5.60000000 2.50000000 3.90000000 1.10000000
[71,] 5.90000000 3.20000000 4.80000000 1.80000000
[72,] 6.10000000 2.80000000 4.00000000 1.30000000
[73,] 6.30000000 2.50000000 4.90000000 1.50000000
[74,] 6.10000000 2.80000000 4.70000000 1.20000000
[75,] 6.40000000 2.90000000 4.30000000 1.30000000
[76,] 6.60000000 3.00000000 4.40000000 1.40000000
[77,] 6.80000000 2.80000000 4.80000000 1.40000000
[78,] 6.70000000 3.00000000 5.00000000 1.70000000
[79,] 6.00000000 2.90000000 4.50000000 1.50000000
[80,] 5.70000000 2.60000000 3.50000000 1.00000000
[81,] 5.50000000 2.40000000 3.80000000 1.10000000
[82,] 5.50000000 2.40000000 3.70000000 1.00000000
[83,] 5.80000000 2.70000000 3.90000000 1.20000000
[84,] 6.00000000 2.70000000 5.10000000 1.60000000
[85,] 5.40000000 3.00000000 4.50000000 1.50000000
[86,] 6.00000000 3.40000000 4.50000000 1.60000000
[87,] 6.70000000 3.10000000 4.70000000 1.50000000
[88,] 6.30000000 2.30000000 4.40000000 1.30000000
[89,] 5.60000000 3.00000000 4.10000000 1.30000000
[90,] 5.50000000 2.50000000 4.00000000 1.30000000
[91,] 5.50000000 2.60000000 4.40000000 1.20000000
[92,] 6.10000000 3.00000000 4.60000000 1.40000000
[93,] 5.80000000 2.60000000 4.00000000 1.20000000
[94,] 5.00000000 2.30000000 3.30000000 1.00000000
[95,] 5.60000000 2.70000000 4.20000000 1.30000000
[96,] 5.70000000 3.00000000 4.20000000 1.20000000
[97,] 5.70000000 2.90000000 4.20000000 1.30000000
[98,] 6.20000000 2.91136488 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 1.60998473
[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 1.77714576
[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,] 98 2
[2,] 8 4
[3,] 9 4
[4,] 120 4
[5,] 126 4
* nbSample = 150
* nbCluster = 3
* lnLikelihood = -1034.915
* nbFreeParameter= 70
* criterion name = ICL
* criterion value= 2427.374
* 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 0.2337976
* S.D. = 0.3861042 0.3861042 0.3861042 0.3861042
****************************************
*** Cluster: 2
* Proportion = 0.3333333
* Means = 5.936000 2.770227 4.260000 1.326000
* S.D. = 0.3861042 0.3861042 0.3861042 0.3861042
****************************************
*** Cluster: 3
* Proportion = 0.3333333
* Means = 6.588000 2.974000 5.552000 2.027743
* S.D. = 0.3861042 0.3861042 0.3861042 0.3861042
****************************************
> model <- learnDiagGaussian( data=x, labels= z,
+ , models = clusterDiagGaussianNames(prop = "equal")
+ , algo = "impute", nbIter = 2, epsilon = 1e-08)
> missingValues(model)
row col value
> print(model)
****************************************
* model name = gaussian_p_sjk
* data =
Sepal.Length Sepal.Width Petal.Length Petal.Width
[1,] 5.10000000 3.50000000 1.40000000 0.20000000
[2,] 4.90000000 3.00000000 1.40000000 0.20000000
[3,] 4.70000000 3.20000000 1.30000000 0.20000000
[4,] 4.60000000 3.10000000 1.50000000 0.20000000
[5,] 5.00000000 3.60000000 1.40000000 0.20000000
[6,] 5.40000000 3.90000000 1.70000000 0.40000000
[7,] 4.60000000 3.40000000 1.40000000 0.30000000
[8,] 5.00000000 3.40000000 1.50000000 -0.14681092
[9,] 4.40000000 2.90000000 1.40000000 -0.06330799
[10,] 4.90000000 3.10000000 1.50000000 0.10000000
[11,] 5.40000000 3.70000000 1.50000000 0.20000000
[12,] 4.80000000 3.40000000 1.60000000 0.20000000
[13,] 4.80000000 3.00000000 1.40000000 0.10000000
[14,] 4.30000000 3.00000000 1.10000000 0.10000000
[15,] 5.80000000 4.00000000 1.20000000 0.20000000
[16,] 5.70000000 4.40000000 1.50000000 0.40000000
[17,] 5.40000000 3.90000000 1.30000000 0.40000000
[18,] 5.10000000 3.50000000 1.40000000 0.30000000
[19,] 5.70000000 3.80000000 1.70000000 0.30000000
[20,] 5.10000000 3.80000000 1.50000000 0.30000000
[21,] 5.40000000 3.40000000 1.70000000 0.20000000
[22,] 5.10000000 3.70000000 1.50000000 0.40000000
[23,] 4.60000000 3.60000000 1.00000000 0.20000000
[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 4.60000000 1.30000000
[60,] 5.20000000 2.70000000 3.90000000 1.40000000
[61,] 5.00000000 2.00000000 3.50000000 1.00000000
[62,] 5.90000000 3.00000000 4.20000000 1.50000000
[63,] 6.00000000 2.20000000 4.00000000 1.00000000
[64,] 6.10000000 2.90000000 4.70000000 1.40000000
[65,] 5.60000000 2.90000000 3.60000000 1.30000000
[66,] 6.70000000 3.10000000 4.40000000 1.40000000
[67,] 5.60000000 3.00000000 4.50000000 1.50000000
[68,] 5.80000000 2.70000000 4.10000000 1.00000000
[69,] 6.20000000 2.20000000 4.50000000 1.50000000
[70,] 5.60000000 2.50000000 3.90000000 1.10000000
[71,] 5.90000000 3.20000000 4.80000000 1.80000000
[72,] 6.10000000 2.80000000 4.00000000 1.30000000
[73,] 6.30000000 2.50000000 4.90000000 1.50000000
[74,] 6.10000000 2.80000000 4.70000000 1.20000000
[75,] 6.40000000 2.90000000 4.30000000 1.30000000
[76,] 6.60000000 3.00000000 4.40000000 1.40000000
[77,] 6.80000000 2.80000000 4.80000000 1.40000000
[78,] 6.70000000 3.00000000 5.00000000 1.70000000
[79,] 6.00000000 2.90000000 4.50000000 1.50000000
[80,] 5.70000000 2.60000000 3.50000000 1.00000000
[81,] 5.50000000 2.40000000 3.80000000 1.10000000
[82,] 5.50000000 2.40000000 3.70000000 1.00000000
[83,] 5.80000000 2.70000000 3.90000000 1.20000000
[84,] 6.00000000 2.70000000 5.10000000 1.60000000
[85,] 5.40000000 3.00000000 4.50000000 1.50000000
[86,] 6.00000000 3.40000000 4.50000000 1.60000000
[87,] 6.70000000 3.10000000 4.70000000 1.50000000
[88,] 6.30000000 2.30000000 4.40000000 1.30000000
[89,] 5.60000000 3.00000000 4.10000000 1.30000000
[90,] 5.50000000 2.50000000 4.00000000 1.30000000
[91,] 5.50000000 2.60000000 4.40000000 1.20000000
[92,] 6.10000000 3.00000000 4.60000000 1.40000000
[93,] 5.80000000 2.60000000 4.00000000 1.20000000
[94,] 5.00000000 2.30000000 3.30000000 1.00000000
[95,] 5.60000000 2.70000000 4.20000000 1.30000000
[96,] 5.70000000 3.00000000 4.20000000 1.20000000
[97,] 5.70000000 2.90000000 4.20000000 1.30000000
[98,] 6.20000000 2.91136488 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 1.60998473
[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 1.77714576
[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 = -1040.166
* nbFreeParameter= 70
* criterion name = ICL
* criterion value= 2438.357
* 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 0.2337976
* S.D. = 0.3489470 0.3752546 0.1719186 0.1250996
****************************************
*** Cluster: 2
* Proportion = 0.3333333
* Means = 5.936000 2.770227 4.260000 1.326000
* S.D. = 0.5109834 0.3107437 0.4651881 0.1957652
****************************************
*** Cluster: 3
* Proportion = 0.3333333
* Means = 6.588000 2.974000 5.552000 2.027743
* S.D. = 0.6294887 0.3192554 0.5463479 0.2684509
****************************************
> model <- learnGamma( data=x, labels= z,
+ , models = clusterGammaNames(prop = "equal")
+ , algo = "simul", nbIter = 2, epsilon = 1e-08
+ )
Flavor: r-oldrel-windows-ix86+x86_64