Last updated on 2023-09-29 19:58:38 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.1 | 473.79 | 108.94 | 582.73 | NOTE | |
| r-devel-linux-x86_64-debian-gcc | 1.5.1 | 407.35 | 81.16 | 488.51 | NOTE | |
| r-devel-linux-x86_64-fedora-clang | 1.5.1 | 957.21 | NOTE | |||
| r-devel-linux-x86_64-fedora-gcc | 1.5.1 | 1086.36 | NOTE | |||
| r-devel-windows-x86_64 | 1.5.1 | 399.00 | 192.00 | 591.00 | NOTE | |
| r-patched-linux-x86_64 | 1.5.1 | 547.57 | 107.07 | 654.64 | NOTE | |
| r-release-linux-x86_64 | 1.5.1 | 527.62 | 108.50 | 636.12 | NOTE | |
| r-release-macos-arm64 | 1.5.1 | 250.00 | NOTE | |||
| r-release-macos-x86_64 | 1.5.1 | 405.00 | NOTE | |||
| r-release-windows-x86_64 | 1.5.1 | 529.00 | 211.00 | 740.00 | NOTE | |
| r-oldrel-macos-arm64 | 1.5.1 | 243.00 | NOTE | |||
| r-oldrel-macos-x86_64 | 1.5.1 | 374.00 | NOTE | |||
| r-oldrel-windows-x86_64 | 1.5.1 | 520.00 | 188.00 | 708.00 | ERROR |
Version: 1.5.1
Check: C++ specification
Result: NOTE
Specified C++11: please drop specification unless essential
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64
Version: 1.5.1
Check: installed package size
Result: NOTE
installed size is 27.9Mb
sub-directories of 1Mb or more:
libs 25.7Mb
Flavors: r-release-macos-arm64, r-release-macos-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64
Version: 1.5.1
Check: tests
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' [2s]
Running 'testPredict.R' [12s]
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,] 126 3 6.0
[2,] 88 3 4.4
[3,] 128 1 6.1
[4,] 45 4 0.4
[5,] 101 4 2.5
> 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
1 128 1 6.1841305
2 88 3 3.9148100
3 126 3 4.6078782
4 45 4 -0.1225852
5 101 4 1.8992931
> print(model)
****************************************
* model name = gaussian_p_sk
* data =
Sepal.Length Sepal.Width Petal.Length Petal.Width
[1,] 5.1000000 3.5000000 1.4000000 0.2000000
[2,] 4.9000000 3.0000000 1.4000000 0.2000000
[3,] 4.7000000 3.2000000 1.3000000 0.2000000
[4,] 4.6000000 3.1000000 1.5000000 0.2000000
[5,] 5.0000000 3.6000000 1.4000000 0.2000000
[6,] 5.4000000 3.9000000 1.7000000 0.4000000
[7,] 4.6000000 3.4000000 1.4000000 0.3000000
[8,] 5.0000000 3.4000000 1.5000000 0.2000000
[9,] 4.4000000 2.9000000 1.4000000 0.2000000
[10,] 4.9000000 3.1000000 1.5000000 0.1000000
[11,] 5.4000000 3.7000000 1.5000000 0.2000000
[12,] 4.8000000 3.4000000 1.6000000 0.2000000
[13,] 4.8000000 3.0000000 1.4000000 0.1000000
[14,] 4.3000000 3.0000000 1.1000000 0.1000000
[15,] 5.8000000 4.0000000 1.2000000 0.2000000
[16,] 5.7000000 4.4000000 1.5000000 0.4000000
[17,] 5.4000000 3.9000000 1.3000000 0.4000000
[18,] 5.1000000 3.5000000 1.4000000 0.3000000
[19,] 5.7000000 3.8000000 1.7000000 0.3000000
[20,] 5.1000000 3.8000000 1.5000000 0.3000000
[21,] 5.4000000 3.4000000 1.7000000 0.2000000
[22,] 5.1000000 3.7000000 1.5000000 0.4000000
[23,] 4.6000000 3.6000000 1.0000000 0.2000000
[24,] 5.1000000 3.3000000 1.7000000 0.5000000
[25,] 4.8000000 3.4000000 1.9000000 0.2000000
[26,] 5.0000000 3.0000000 1.6000000 0.2000000
[27,] 5.0000000 3.4000000 1.6000000 0.4000000
[28,] 5.2000000 3.5000000 1.5000000 0.2000000
[29,] 5.2000000 3.4000000 1.4000000 0.2000000
[30,] 4.7000000 3.2000000 1.6000000 0.2000000
[31,] 4.8000000 3.1000000 1.6000000 0.2000000
[32,] 5.4000000 3.4000000 1.5000000 0.4000000
[33,] 5.2000000 4.1000000 1.5000000 0.1000000
[34,] 5.5000000 4.2000000 1.4000000 0.2000000
[35,] 4.9000000 3.1000000 1.5000000 0.2000000
[36,] 5.0000000 3.2000000 1.2000000 0.2000000
[37,] 5.5000000 3.5000000 1.3000000 0.2000000
[38,] 4.9000000 3.6000000 1.4000000 0.1000000
[39,] 4.4000000 3.0000000 1.3000000 0.2000000
[40,] 5.1000000 3.4000000 1.5000000 0.2000000
[41,] 5.0000000 3.5000000 1.3000000 0.3000000
[42,] 4.5000000 2.3000000 1.3000000 0.3000000
[43,] 4.4000000 3.2000000 1.3000000 0.2000000
[44,] 5.0000000 3.5000000 1.6000000 0.6000000
[45,] 5.1000000 3.8000000 1.9000000 -0.1225852
[46,] 4.8000000 3.0000000 1.4000000 0.3000000
[47,] 5.1000000 3.8000000 1.6000000 0.2000000
[48,] 4.6000000 3.2000000 1.4000000 0.2000000
[49,] 5.3000000 3.7000000 1.5000000 0.2000000
[50,] 5.0000000 3.3000000 1.4000000 0.2000000
[51,] 7.0000000 3.2000000 4.7000000 1.4000000
[52,] 6.4000000 3.2000000 4.5000000 1.5000000
[53,] 6.9000000 3.1000000 4.9000000 1.5000000
[54,] 5.5000000 2.3000000 4.0000000 1.3000000
[55,] 6.5000000 2.8000000 4.6000000 1.5000000
[56,] 5.7000000 2.8000000 4.5000000 1.3000000
[57,] 6.3000000 3.3000000 4.7000000 1.6000000
[58,] 4.9000000 2.4000000 3.3000000 1.0000000
[59,] 6.6000000 2.9000000 4.6000000 1.3000000
[60,] 5.2000000 2.7000000 3.9000000 1.4000000
[61,] 5.0000000 2.0000000 3.5000000 1.0000000
[62,] 5.9000000 3.0000000 4.2000000 1.5000000
[63,] 6.0000000 2.2000000 4.0000000 1.0000000
[64,] 6.1000000 2.9000000 4.7000000 1.4000000
[65,] 5.6000000 2.9000000 3.6000000 1.3000000
[66,] 6.7000000 3.1000000 4.4000000 1.4000000
[67,] 5.6000000 3.0000000 4.5000000 1.5000000
[68,] 5.8000000 2.7000000 4.1000000 1.0000000
[69,] 6.2000000 2.2000000 4.5000000 1.5000000
[70,] 5.6000000 2.5000000 3.9000000 1.1000000
[71,] 5.9000000 3.2000000 4.8000000 1.8000000
[72,] 6.1000000 2.8000000 4.0000000 1.3000000
[73,] 6.3000000 2.5000000 4.9000000 1.5000000
[74,] 6.1000000 2.8000000 4.7000000 1.2000000
[75,] 6.4000000 2.9000000 4.3000000 1.3000000
[76,] 6.6000000 3.0000000 4.4000000 1.4000000
[77,] 6.8000000 2.8000000 4.8000000 1.4000000
[78,] 6.7000000 3.0000000 5.0000000 1.7000000
[79,] 6.0000000 2.9000000 4.5000000 1.5000000
[80,] 5.7000000 2.6000000 3.5000000 1.0000000
[81,] 5.5000000 2.4000000 3.8000000 1.1000000
[82,] 5.5000000 2.4000000 3.7000000 1.0000000
[83,] 5.8000000 2.7000000 3.9000000 1.2000000
[84,] 6.0000000 2.7000000 5.1000000 1.6000000
[85,] 5.4000000 3.0000000 4.5000000 1.5000000
[86,] 6.0000000 3.4000000 4.5000000 1.6000000
[87,] 6.7000000 3.1000000 4.7000000 1.5000000
[88,] 6.3000000 2.3000000 3.9148100 1.3000000
[89,] 5.6000000 3.0000000 4.1000000 1.3000000
[90,] 5.5000000 2.5000000 4.0000000 1.3000000
[91,] 5.5000000 2.6000000 4.4000000 1.2000000
[92,] 6.1000000 3.0000000 4.6000000 1.4000000
[93,] 5.8000000 2.6000000 4.0000000 1.2000000
[94,] 5.0000000 2.3000000 3.3000000 1.0000000
[95,] 5.6000000 2.7000000 4.2000000 1.3000000
[96,] 5.7000000 3.0000000 4.2000000 1.2000000
[97,] 5.7000000 2.9000000 4.2000000 1.3000000
[98,] 6.2000000 2.9000000 4.3000000 1.3000000
[99,] 5.1000000 2.5000000 3.0000000 1.1000000
[100,] 5.7000000 2.8000000 4.1000000 1.3000000
[101,] 6.3000000 3.3000000 6.0000000 1.8992931
[102,] 5.8000000 2.7000000 5.1000000 1.9000000
[103,] 7.1000000 3.0000000 5.9000000 2.1000000
[104,] 6.3000000 2.9000000 5.6000000 1.8000000
[105,] 6.5000000 3.0000000 5.8000000 2.2000000
[106,] 7.6000000 3.0000000 6.6000000 2.1000000
[107,] 4.9000000 2.5000000 4.5000000 1.7000000
[108,] 7.3000000 2.9000000 6.3000000 1.8000000
[109,] 6.7000000 2.5000000 5.8000000 1.8000000
[110,] 7.2000000 3.6000000 6.1000000 2.5000000
[111,] 6.5000000 3.2000000 5.1000000 2.0000000
[112,] 6.4000000 2.7000000 5.3000000 1.9000000
[113,] 6.8000000 3.0000000 5.5000000 2.1000000
[114,] 5.7000000 2.5000000 5.0000000 2.0000000
[115,] 5.8000000 2.8000000 5.1000000 2.4000000
[116,] 6.4000000 3.2000000 5.3000000 2.3000000
[117,] 6.5000000 3.0000000 5.5000000 1.8000000
[118,] 7.7000000 3.8000000 6.7000000 2.2000000
[119,] 7.7000000 2.6000000 6.9000000 2.3000000
[120,] 6.0000000 2.2000000 5.0000000 1.5000000
[121,] 6.9000000 3.2000000 5.7000000 2.3000000
[122,] 5.6000000 2.8000000 4.9000000 2.0000000
[123,] 7.7000000 2.8000000 6.7000000 2.0000000
[124,] 6.3000000 2.7000000 4.9000000 1.8000000
[125,] 6.7000000 3.3000000 5.7000000 2.1000000
[126,] 7.2000000 3.2000000 4.6078782 1.8000000
[127,] 6.2000000 2.8000000 4.8000000 1.8000000
[128,] 6.1841305 3.0000000 4.9000000 1.8000000
[129,] 6.4000000 2.8000000 5.6000000 2.1000000
[130,] 7.2000000 3.0000000 5.8000000 1.6000000
[131,] 7.4000000 2.8000000 6.1000000 1.9000000
[132,] 7.9000000 3.8000000 6.4000000 2.0000000
[133,] 6.4000000 2.8000000 5.6000000 2.2000000
[134,] 6.3000000 2.8000000 5.1000000 1.5000000
[135,] 6.1000000 2.6000000 5.6000000 1.4000000
[136,] 7.7000000 3.0000000 6.1000000 2.3000000
[137,] 6.3000000 3.4000000 5.6000000 2.4000000
[138,] 6.4000000 3.1000000 5.5000000 1.8000000
[139,] 6.0000000 3.0000000 4.8000000 1.8000000
[140,] 6.9000000 3.1000000 5.4000000 2.1000000
[141,] 6.7000000 3.1000000 5.6000000 2.4000000
[142,] 6.9000000 3.1000000 5.1000000 2.3000000
[143,] 5.8000000 2.7000000 5.1000000 1.9000000
[144,] 6.8000000 3.2000000 5.9000000 2.3000000
[145,] 6.7000000 3.3000000 5.7000000 2.5000000
[146,] 6.7000000 3.0000000 5.2000000 2.3000000
[147,] 6.3000000 2.5000000 5.0000000 1.9000000
[148,] 6.5000000 3.0000000 5.2000000 2.0000000
[149,] 6.2000000 3.4000000 5.4000000 2.3000000
[150,] 5.9000000 3.0000000 5.1000000 1.8000000
* missing =
row col
[1,] 128 1
[2,] 88 3
[3,] 126 3
[4,] 45 4
[5,] 101 4
* nbSample = 150
* nbCluster = 3
* lnLikelihood = -1028.518
* nbFreeParameter= 70
* criterion name = ICL
* criterion value= 2414.764
* 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 0.2355483
* S.D. = 0.2762035 0.2762035 0.2762035 0.2762035
****************************************
*** Cluster: 2
* Proportion = 0.3333333
* Means = 5.936000 2.770000 4.250296 1.326000
* S.D. = 0.3918625 0.3918625 0.3918625 0.3918625
****************************************
*** Cluster: 3
* Proportion = 0.3333333
* Means = 6.589683 2.974000 5.524158 2.013986
* S.D. = 0.4684596 0.4684596 0.4684596 0.4684596
****************************************
> 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,] 5.1000000 3.5000000 1.4000000 0.2000000
[2,] 4.9000000 3.0000000 1.4000000 0.2000000
[3,] 4.7000000 3.2000000 1.3000000 0.2000000
[4,] 4.6000000 3.1000000 1.5000000 0.2000000
[5,] 5.0000000 3.6000000 1.4000000 0.2000000
[6,] 5.4000000 3.9000000 1.7000000 0.4000000
[7,] 4.6000000 3.4000000 1.4000000 0.3000000
[8,] 5.0000000 3.4000000 1.5000000 0.2000000
[9,] 4.4000000 2.9000000 1.4000000 0.2000000
[10,] 4.9000000 3.1000000 1.5000000 0.1000000
[11,] 5.4000000 3.7000000 1.5000000 0.2000000
[12,] 4.8000000 3.4000000 1.6000000 0.2000000
[13,] 4.8000000 3.0000000 1.4000000 0.1000000
[14,] 4.3000000 3.0000000 1.1000000 0.1000000
[15,] 5.8000000 4.0000000 1.2000000 0.2000000
[16,] 5.7000000 4.4000000 1.5000000 0.4000000
[17,] 5.4000000 3.9000000 1.3000000 0.4000000
[18,] 5.1000000 3.5000000 1.4000000 0.3000000
[19,] 5.7000000 3.8000000 1.7000000 0.3000000
[20,] 5.1000000 3.8000000 1.5000000 0.3000000
[21,] 5.4000000 3.4000000 1.7000000 0.2000000
[22,] 5.1000000 3.7000000 1.5000000 0.4000000
[23,] 4.6000000 3.6000000 1.0000000 0.2000000
[24,] 5.1000000 3.3000000 1.7000000 0.5000000
[25,] 4.8000000 3.4000000 1.9000000 0.2000000
[26,] 5.0000000 3.0000000 1.6000000 0.2000000
[27,] 5.0000000 3.4000000 1.6000000 0.4000000
[28,] 5.2000000 3.5000000 1.5000000 0.2000000
[29,] 5.2000000 3.4000000 1.4000000 0.2000000
[30,] 4.7000000 3.2000000 1.6000000 0.2000000
[31,] 4.8000000 3.1000000 1.6000000 0.2000000
[32,] 5.4000000 3.4000000 1.5000000 0.4000000
[33,] 5.2000000 4.1000000 1.5000000 0.1000000
[34,] 5.5000000 4.2000000 1.4000000 0.2000000
[35,] 4.9000000 3.1000000 1.5000000 0.2000000
[36,] 5.0000000 3.2000000 1.2000000 0.2000000
[37,] 5.5000000 3.5000000 1.3000000 0.2000000
[38,] 4.9000000 3.6000000 1.4000000 0.1000000
[39,] 4.4000000 3.0000000 1.3000000 0.2000000
[40,] 5.1000000 3.4000000 1.5000000 0.2000000
[41,] 5.0000000 3.5000000 1.3000000 0.3000000
[42,] 4.5000000 2.3000000 1.3000000 0.3000000
[43,] 4.4000000 3.2000000 1.3000000 0.2000000
[44,] 5.0000000 3.5000000 1.6000000 0.6000000
[45,] 5.1000000 3.8000000 1.9000000 -0.1225852
[46,] 4.8000000 3.0000000 1.4000000 0.3000000
[47,] 5.1000000 3.8000000 1.6000000 0.2000000
[48,] 4.6000000 3.2000000 1.4000000 0.2000000
[49,] 5.3000000 3.7000000 1.5000000 0.2000000
[50,] 5.0000000 3.3000000 1.4000000 0.2000000
[51,] 7.0000000 3.2000000 4.7000000 1.4000000
[52,] 6.4000000 3.2000000 4.5000000 1.5000000
[53,] 6.9000000 3.1000000 4.9000000 1.5000000
[54,] 5.5000000 2.3000000 4.0000000 1.3000000
[55,] 6.5000000 2.8000000 4.6000000 1.5000000
[56,] 5.7000000 2.8000000 4.5000000 1.3000000
[57,] 6.3000000 3.3000000 4.7000000 1.6000000
[58,] 4.9000000 2.4000000 3.3000000 1.0000000
[59,] 6.6000000 2.9000000 4.6000000 1.3000000
[60,] 5.2000000 2.7000000 3.9000000 1.4000000
[61,] 5.0000000 2.0000000 3.5000000 1.0000000
[62,] 5.9000000 3.0000000 4.2000000 1.5000000
[63,] 6.0000000 2.2000000 4.0000000 1.0000000
[64,] 6.1000000 2.9000000 4.7000000 1.4000000
[65,] 5.6000000 2.9000000 3.6000000 1.3000000
[66,] 6.7000000 3.1000000 4.4000000 1.4000000
[67,] 5.6000000 3.0000000 4.5000000 1.5000000
[68,] 5.8000000 2.7000000 4.1000000 1.0000000
[69,] 6.2000000 2.2000000 4.5000000 1.5000000
[70,] 5.6000000 2.5000000 3.9000000 1.1000000
[71,] 5.9000000 3.2000000 4.8000000 1.8000000
[72,] 6.1000000 2.8000000 4.0000000 1.3000000
[73,] 6.3000000 2.5000000 4.9000000 1.5000000
[74,] 6.1000000 2.8000000 4.7000000 1.2000000
[75,] 6.4000000 2.9000000 4.3000000 1.3000000
[76,] 6.6000000 3.0000000 4.4000000 1.4000000
[77,] 6.8000000 2.8000000 4.8000000 1.4000000
[78,] 6.7000000 3.0000000 5.0000000 1.7000000
[79,] 6.0000000 2.9000000 4.5000000 1.5000000
[80,] 5.7000000 2.6000000 3.5000000 1.0000000
[81,] 5.5000000 2.4000000 3.8000000 1.1000000
[82,] 5.5000000 2.4000000 3.7000000 1.0000000
[83,] 5.8000000 2.7000000 3.9000000 1.2000000
[84,] 6.0000000 2.7000000 5.1000000 1.6000000
[85,] 5.4000000 3.0000000 4.5000000 1.5000000
[86,] 6.0000000 3.4000000 4.5000000 1.6000000
[87,] 6.7000000 3.1000000 4.7000000 1.5000000
[88,] 6.3000000 2.3000000 3.9148100 1.3000000
[89,] 5.6000000 3.0000000 4.1000000 1.3000000
[90,] 5.5000000 2.5000000 4.0000000 1.3000000
[91,] 5.5000000 2.6000000 4.4000000 1.2000000
[92,] 6.1000000 3.0000000 4.6000000 1.4000000
[93,] 5.8000000 2.6000000 4.0000000 1.2000000
[94,] 5.0000000 2.3000000 3.3000000 1.0000000
[95,] 5.6000000 2.7000000 4.2000000 1.3000000
[96,] 5.7000000 3.0000000 4.2000000 1.2000000
[97,] 5.7000000 2.9000000 4.2000000 1.3000000
[98,] 6.2000000 2.9000000 4.3000000 1.3000000
[99,] 5.1000000 2.5000000 3.0000000 1.1000000
[100,] 5.7000000 2.8000000 4.1000000 1.3000000
[101,] 6.3000000 3.3000000 6.0000000 1.8992931
[102,] 5.8000000 2.7000000 5.1000000 1.9000000
[103,] 7.1000000 3.0000000 5.9000000 2.1000000
[104,] 6.3000000 2.9000000 5.6000000 1.8000000
[105,] 6.5000000 3.0000000 5.8000000 2.2000000
[106,] 7.6000000 3.0000000 6.6000000 2.1000000
[107,] 4.9000000 2.5000000 4.5000000 1.7000000
[108,] 7.3000000 2.9000000 6.3000000 1.8000000
[109,] 6.7000000 2.5000000 5.8000000 1.8000000
[110,] 7.2000000 3.6000000 6.1000000 2.5000000
[111,] 6.5000000 3.2000000 5.1000000 2.0000000
[112,] 6.4000000 2.7000000 5.3000000 1.9000000
[113,] 6.8000000 3.0000000 5.5000000 2.1000000
[114,] 5.7000000 2.5000000 5.0000000 2.0000000
[115,] 5.8000000 2.8000000 5.1000000 2.4000000
[116,] 6.4000000 3.2000000 5.3000000 2.3000000
[117,] 6.5000000 3.0000000 5.5000000 1.8000000
[118,] 7.7000000 3.8000000 6.7000000 2.2000000
[119,] 7.7000000 2.6000000 6.9000000 2.3000000
[120,] 6.0000000 2.2000000 5.0000000 1.5000000
[121,] 6.9000000 3.2000000 5.7000000 2.3000000
[122,] 5.6000000 2.8000000 4.9000000 2.0000000
[123,] 7.7000000 2.8000000 6.7000000 2.0000000
[124,] 6.3000000 2.7000000 4.9000000 1.8000000
[125,] 6.7000000 3.3000000 5.7000000 2.1000000
[126,] 7.2000000 3.2000000 4.6078782 1.8000000
[127,] 6.2000000 2.8000000 4.8000000 1.8000000
[128,] 6.1841305 3.0000000 4.9000000 1.8000000
[129,] 6.4000000 2.8000000 5.6000000 2.1000000
[130,] 7.2000000 3.0000000 5.8000000 1.6000000
[131,] 7.4000000 2.8000000 6.1000000 1.9000000
[132,] 7.9000000 3.8000000 6.4000000 2.0000000
[133,] 6.4000000 2.8000000 5.6000000 2.2000000
[134,] 6.3000000 2.8000000 5.1000000 1.5000000
[135,] 6.1000000 2.6000000 5.6000000 1.4000000
[136,] 7.7000000 3.0000000 6.1000000 2.3000000
[137,] 6.3000000 3.4000000 5.6000000 2.4000000
[138,] 6.4000000 3.1000000 5.5000000 1.8000000
[139,] 6.0000000 3.0000000 4.8000000 1.8000000
[140,] 6.9000000 3.1000000 5.4000000 2.1000000
[141,] 6.7000000 3.1000000 5.6000000 2.4000000
[142,] 6.9000000 3.1000000 5.1000000 2.3000000
[143,] 5.8000000 2.7000000 5.1000000 1.9000000
[144,] 6.8000000 3.2000000 5.9000000 2.3000000
[145,] 6.7000000 3.3000000 5.7000000 2.5000000
[146,] 6.7000000 3.0000000 5.2000000 2.3000000
[147,] 6.3000000 2.5000000 5.0000000 1.9000000
[148,] 6.5000000 3.0000000 5.2000000 2.0000000
[149,] 6.2000000 3.4000000 5.4000000 2.3000000
[150,] 5.9000000 3.0000000 5.1000000 1.8000000
* missing =
row col
* nbSample = 150
* nbCluster = 3
* lnLikelihood = -1032.873
* nbFreeParameter= 70
* criterion name = ICL
* criterion value= 2423.527
* 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 0.2355483
* S.D. = 0.3489470 0.3752546 0.1719186 0.1140944
****************************************
*** Cluster: 2
* Proportion = 0.3333333
* Means = 5.936000 2.770000 4.250296 1.326000
* S.D. = 0.5109834 0.3106445 0.4672226 0.1957652
****************************************
*** Cluster: 3
* Proportion = 0.3333333
* Means = 6.589683 2.974000 5.524158 2.013986
* S.D. = 0.6282933 0.3192554 0.5581523 0.2638318
****************************************
> model <- learnGamma( data=x, labels= z,
+ , models = clusterGammaNames(prop = "equal")
+ , algo = "simul", nbIter = 2, epsilon = 1e-08
+ )
Flavor: r-oldrel-windows-x86_64