Last updated on 2022-05-15 01:52:24 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.1 | 629.58 | 112.09 | 741.67 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 1.5.1 | 589.58 | 85.73 | 675.31 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.1 | 1224.26 | NOTE | |||
| r-devel-linux-x86_64-fedora-gcc | 1.5.1 | 1159.70 | OK | |||
| r-devel-windows-x86_64 | 1.5.1 | 688.00 | 228.00 | 916.00 | OK | |
| r-patched-linux-x86_64 | 1.5.1 | 607.00 | 109.37 | 716.37 | OK | |
| r-release-linux-x86_64 | 1.5.1 | 616.96 | 110.07 | 727.03 | OK | |
| r-release-macos-arm64 | 1.5.1 | 248.00 | NOTE | |||
| r-release-macos-x86_64 | 1.5.1 | 374.00 | NOTE | |||
| r-release-windows-x86_64 | 1.5.1 | 669.00 | 225.00 | 894.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.1 | 288.00 | NOTE | |||
| r-oldrel-macos-x86_64 | 1.5.1 | 493.00 | NOTE | |||
| r-oldrel-windows-ix86+x86_64 | 1.5.1 | 1680.00 | 231.00 | 1911.00 | NOTE |
Version: 1.5.1
Check: tests
Result: ERROR
Running 'ClusterSimul.R' [0s/0s]
Running 'clusterDiagGaussianLikelihood.R' [1s/2s]
Running 'clusterGammaLikelihood.R' [1s/2s]
Running 'simulHeterogeneous.R' [0s/0s]
Running 'simulNonLinear.R' [1s/1s]
Running 'testAllLearners.R' [1s/1s]
Running 'testPoissonExample.R' [2s/3s]
Running 'testPredict.R' [9s/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,] 74 3 4.7
[2,] 63 3 4.0
[3,] 40 1 5.1
[4,] 54 2 2.3
[5,] 39 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 40 1 5.1849450
2 54 2 2.3675745
3 63 3 3.9400247
4 74 3 4.4835565
5 39 4 -0.1556213
> print(model)
****************************************
* model name = gaussian_p_s
* 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 -0.1556213
[40,] 5.1849450 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 0.4000000
[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 2.3675745 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 3.9400247 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 4.4835565 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 4.4000000 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 2.5000000
[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 6.0000000 1.8000000
[127,] 6.2000000 2.8000000 4.8000000 1.8000000
[128,] 6.1000000 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,] 40 1
[2,] 54 2
[3,] 63 3
[4,] 74 3
[5,] 39 4
* nbSample = 150
* nbCluster = 3
* lnLikelihood = -1031.388
* nbFreeParameter= 70
* criterion name = ICL
* criterion value= 2420.798
* 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 = 5.0076989 3.4280000 1.4620000 0.2388876
* S.D. = 0.3858058 0.3858058 0.3858058 0.3858058
****************************************
*** Cluster: 2
* Proportion = 0.3333333
* Means = 5.936000 2.771351 4.254472 1.326000
* S.D. = 0.3858058 0.3858058 0.3858058 0.3858058
****************************************
*** Cluster: 3
* Proportion = 0.3333333
* Means = 6.588 2.974 5.552 2.026
* S.D. = 0.3858058 0.3858058 0.3858058 0.3858058
****************************************
> 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 -0.1556213
[40,] 5.1849450 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 0.4000000
[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 2.3675745 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 3.9400247 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 4.4835565 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 4.4000000 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 2.5000000
[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 6.0000000 1.8000000
[127,] 6.2000000 2.8000000 4.8000000 1.8000000
[128,] 6.1000000 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 = -1031.918
* nbFreeParameter= 70
* criterion name = ICL
* criterion value= 2421.954
* 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 = 5.0076989 3.4280000 1.4620000 0.2388876
* S.D. = 0.3496067 0.3752546 0.1719186 0.1183938
****************************************
*** Cluster: 2
* Proportion = 0.3333333
* Means = 5.936000 2.771351 4.254472 1.326000
* S.D. = 0.5109834 0.3087379 0.4628095 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
+ )
*** caught segfault ***
address 0x120, 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
Flavor: r-devel-linux-x86_64-debian-clang
Version: 1.5.1
Check: installed package size
Result: NOTE
installed size is 22.6Mb
sub-directories of 1Mb or more:
libs 20.3Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-release-macos-arm64, r-release-macos-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-ix86+x86_64