Last updated on 2021-10-22 17:51:00 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.0 | 151.91 | 218.82 | 370.73 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.0 | 113.57 | 157.96 | 271.53 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.0.0 | 501.11 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 1.0.0 | 514.79 | NOTE | |||
| r-devel-windows-x86_64 | 1.0.0 | 202.00 | 289.00 | 491.00 | OK | |
| r-devel-windows-x86_64-gcc10-UCRT | 1.0.0 | NOTE | ||||
| r-patched-linux-x86_64 | 1.0.0 | 142.71 | 225.92 | 368.63 | OK | |
| r-patched-solaris-x86 | 1.0.0 | 379.30 | ERROR | |||
| r-release-linux-x86_64 | 1.0.0 | 138.75 | 207.36 | 346.11 | ERROR | |
| r-release-macos-arm64 | 1.0.0 | NOTE | ||||
| r-release-macos-x86_64 | 1.0.0 | NOTE | ||||
| r-release-windows-ix86+x86_64 | 1.0.0 | 277.00 | 313.00 | 590.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.0.0 | NOTE | ||||
| r-oldrel-windows-ix86+x86_64 | 1.0.0 | 395.00 | 336.00 | 731.00 | ERROR |
Version: 1.0.0
Check: examples
Result: ERROR
Running examples in ‘hdpGLM-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: plot.dpGLM
> ### Title: Default plot for class dpGLM
> ### Aliases: plot.dpGLM
>
> ### ** Examples
>
> # Note: this example is just for illustration. MCMC iterations are very reduced
> set.seed(10)
> n = 20
> data = tibble::data_frame(x1 = rnorm(n, -3),
+ x2 = rnorm(n, 3),
+ z = sample(1:3, n, replace=TRUE),
+ y =I(z==1) * (3 + 4*x1 - x2 + rnorm(n)) +
+ I(z==2) * (3 + 2*x1 + x2 + rnorm(n)) +
+ I(z==3) * (3 - 4*x1 - x2 + rnorm(n)) ,
+ )
Warning: `data_frame()` was deprecated in tibble 1.1.0.
Please use `tibble()` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
>
> ## estimation
> mcmc = list(burn.in=1, n.iter=50)
> samples = hdpGLM(y ~ x1 + x2, data=data, mcmc=mcmc, n.display=1)
Preparing for estimation ...
Warning in model.matrix.default(mt1, reg.matrix, stats::contrasts) :
non-list contrasts argument ignored
Estimation in progress ...
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 1
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 1
Current number of active clusters : 1
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000e+00
1.0000e+02
[=== ] 3 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 2
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
15.0000 20.0000 25.0000 10.0000 20.0000
[===== ] 5 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 3
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 8.0000
10.0000 15.0000 50.0000 10.0000
[====== ] 7 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 4
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 8.0000
10.0000 15.0000 50.0000 10.0000 15.0000
[======= ] 9 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 5
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000
10.0000 15.0000 50.0000 20.0000
[========= ] 11 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 6
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 8.0000
15.0000 15.0000 50.0000 15.0000
[========== ] 13 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 7
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000 8.0000
10.0000 45.0000 20.0000 20.0000
[=========== ] 15 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 8
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 4.0000 5.0000 8.0000
50.0000 10.0000 10.0000 20.0000
[============= ] 17 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 9
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 5.0000 6.0000 8.0000
45.0000 15.0000 10.0000 20.0000
[============== ] 19 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 10
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 4.0000 5.0000 8.0000
45.0000 10.0000 20.0000 25.0000
[================ ] 21 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 11
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 5.0000 8.0000
40.0000 20.0000 25.0000
[================= ] 23 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 12
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 5.0000 7.0000 8.0000
50.0000 20.0000 10.0000 10.0000
[================== ] 25 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 13
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 4.0000 5.0000 8.0000
10.0000 45.0000 15.0000 15.0000 10.0000
[==================== ] 27 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 14
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000
20.0000 45.0000 20.0000
[===================== ] 29 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 15
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000 8.0000
10.0000 50.0000 25.0000 10.0000
[====================== ] 31 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 16
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 10.0000 50.0000 10.0000
[======================== ] 33 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 17
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 10.0000 50.0000 15.0000
[========================= ] 35 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 18
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 15.0000 50.0000 10.0000
[=========================== ] 37 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 19
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000
25.0000 50.0000 15.0000
[============================ ] 39 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 20
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000
25.0000 50.0000 20.0000
[============================= ] 41 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 21
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 10.0000 50.0000 15.0000
[=============================== ] 43 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 22
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 10.0000 50.0000 15.0000
[================================ ] 45 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 23
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
20.0000 15.0000 50.0000 15.0000
[================================= ] 47 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 24
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
10.0000 15.0000 50.0000 10.0000 15.0000
[=================================== ] 49 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 25
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 50.0000 15.0000 15.0000
[==================================== ] 50 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 26
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 50.0000 20.0000 10.0000
[====================================== ] 52 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 27
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 45.0000 20.0000 15.0000
[======================================= ] 54 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 28
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
15.0000 15.0000 30.0000 20.0000 15.0000
[======================================== ] 56 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 29
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
15.0000 10.0000 35.0000 20.0000 20.0000
[========================================== ] 58 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 30
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
10.0000 45.0000 20.0000 20.0000
[=========================================== ] 60 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 31
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
15.0000 15.0000 35.0000 20.0000 15.0000
[============================================ ] 62 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 32
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000
15.0000 25.0000 35.0000 15.0000
[============================================== ] 64 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 33
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 8.0000
15.0000 15.0000 35.0000 15.0000 10.0000
[=============================================== ] 66 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 34
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
20.0000 20.0000 25.0000 20.0000 10.0000
[================================================= ] 68 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 35
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 8
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000
15.0000 20.0000 30.0000 15.0000
[================================================== ] 70 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 36
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
20.0000 15.0000 30.0000 15.0000 10.0000
[=================================================== ] 72 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 37
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
20.0000 10.0000 30.0000 20.0000 20.0000
[===================================================== ] 74 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 38
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
20.0000 15.0000 30.0000 20.0000 10.0000
[====================================================== ] 76 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 39
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 4.0000 5.0000 7.0000
20.0000 30.0000 20.0000 15.0000 10.0000
[======================================================= ] 78 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 40
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 45.0000 25.0000 10.0000
[========================================================= ] 80 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 41
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000 6.0000
10.0000 35.0000 20.0000 15.0000 15.0000
[========================================================== ] 82 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 42
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 50.0000 20.0000 10.0000
[============================================================ ] 84 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 43
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000 8.0000
10.0000 45.0000 15.0000 10.0000 10.0000
[============================================================= ] 86 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 44
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 45.0000 15.0000 10.0000
[============================================================== ] 88 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 45
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
20.0000 50.0000 25.0000
[================================================================ ] 90 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 46
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
15.0000 50.0000 20.0000
[================================================================= ] 92 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 47
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
25.0000 50.0000 20.0000
[================================================================== ] 94 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 48
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 3
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
25.0000 50.0000 25.0000
[==================================================================== ] 96 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 49
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000
10.0000 25.0000 40.0000 20.0000
[===================================================================== ] 98 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 50
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
20.0000 50.0000 20.0000
[=======================================================================] 100 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 51
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 6.0000
15.0000 50.0000 20.0000 10.0000
[=======================================================================] 100 %
>
> plot(samples)
Generating plot...
*** caught segfault ***
address 0x55ec00000004, cause 'memory not mapped'
Fatal error: *** recursive gc invocation
Fatal error: *** recursive gc invocation
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.0.0
Check: package dependencies
Result: NOTE
Imports includes 23 non-default packages.
Importing from so many packages makes the package vulnerable to any of
them becoming unavailable. Move as many as possible to Suggests and
use conditionally.
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 1.0.0
Check: installed package size
Result: NOTE
installed size is 7.4Mb
sub-directories of 1Mb or more:
libs 6.7Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-release-macos-arm64, r-release-macos-x86_64, r-oldrel-macos-x86_64
Version: 1.0.0
Check: dependencies in R code
Result: NOTE
Namespaces in Imports field not imported from:
‘MCMCpack’ ‘ggjoy’ ‘isotone’ ‘mvtnorm’ ‘questionr’ ‘rprojroot’
‘tidyverse’
All declared Imports should be used.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64-gcc10-UCRT, r-patched-solaris-x86, r-release-macos-arm64, r-release-macos-x86_64, r-oldrel-macos-x86_64
Version: 1.0.0
Check: examples
Result: ERROR
Running examples in ‘hdpGLM-Ex.R’ failed
The error most likely occurred in:
> ### Name: plot.dpGLM
> ### Title: Default plot for class dpGLM
> ### Aliases: plot.dpGLM
>
> ### ** Examples
>
> # Note: this example is just for illustration. MCMC iterations are very reduced
> set.seed(10)
> n = 20
> data = tibble::data_frame(x1 = rnorm(n, -3),
+ x2 = rnorm(n, 3),
+ z = sample(1:3, n, replace=TRUE),
+ y =I(z==1) * (3 + 4*x1 - x2 + rnorm(n)) +
+ I(z==2) * (3 + 2*x1 + x2 + rnorm(n)) +
+ I(z==3) * (3 - 4*x1 - x2 + rnorm(n)) ,
+ )
Warning: `data_frame()` was deprecated in tibble 1.1.0.
Please use `tibble()` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
>
> ## estimation
> mcmc = list(burn.in=1, n.iter=50)
> samples = hdpGLM(y ~ x1 + x2, data=data, mcmc=mcmc, n.display=1)
Preparing for estimation ...
Warning in model.matrix.default(mt1, reg.matrix, stats::contrasts) :
non-list contrasts argument ignored
Estimation in progress ...
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 1
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 1
Current number of active clusters : 1
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000e+00
1.0000e+02
[=== ] 3 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 2
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
15.0000 20.0000 25.0000 10.0000 20.0000
[===== ] 5 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 3
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 8.0000
10.0000 15.0000 50.0000 10.0000
[====== ] 7 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 4
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 8.0000
10.0000 15.0000 50.0000 10.0000 15.0000
[======= ] 9 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 5
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000
10.0000 15.0000 50.0000 20.0000
[========= ] 11 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 6
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 8.0000
15.0000 15.0000 50.0000 15.0000
[========== ] 13 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 7
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000 8.0000
10.0000 45.0000 20.0000 20.0000
[=========== ] 15 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 8
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 4.0000 5.0000 8.0000
50.0000 10.0000 10.0000 20.0000
[============= ] 17 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 9
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 5.0000 6.0000 8.0000
45.0000 15.0000 10.0000 20.0000
[============== ] 19 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 10
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 4.0000 5.0000 8.0000
45.0000 10.0000 20.0000 25.0000
[================ ] 21 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 11
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 5.0000 8.0000
40.0000 20.0000 25.0000
[================= ] 23 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 12
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 5.0000 7.0000 8.0000
50.0000 20.0000 10.0000 10.0000
[================== ] 25 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 13
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 4.0000 5.0000 8.0000
10.0000 45.0000 15.0000 15.0000 10.0000
[==================== ] 27 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 14
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000
20.0000 45.0000 20.0000
[===================== ] 29 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 15
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000 8.0000
10.0000 50.0000 25.0000 10.0000
[====================== ] 31 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 16
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 10.0000 50.0000 10.0000
[======================== ] 33 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 17
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 10.0000 50.0000 15.0000
[========================= ] 35 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 18
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 15.0000 50.0000 10.0000
[=========================== ] 37 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 19
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000
25.0000 50.0000 15.0000
[============================ ] 39 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 20
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000
25.0000 50.0000 20.0000
[============================= ] 41 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 21
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 10.0000 50.0000 15.0000
[=============================== ] 43 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 22
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 10.0000 50.0000 15.0000
[================================ ] 45 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 23
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
20.0000 15.0000 50.0000 15.0000
[================================= ] 47 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 24
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
10.0000 15.0000 50.0000 10.0000 15.0000
[=================================== ] 49 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 25
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 50.0000 15.0000 15.0000
[==================================== ] 50 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 26
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 50.0000 20.0000 10.0000
[====================================== ] 52 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 27
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 45.0000 20.0000 15.0000
[======================================= ] 54 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 28
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
15.0000 15.0000 30.0000 20.0000 15.0000
[======================================== ] 56 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 29
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
15.0000 10.0000 35.0000 20.0000 20.0000
[========================================== ] 58 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 30
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
10.0000 45.0000 20.0000 20.0000
[=========================================== ] 60 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 31
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
15.0000 15.0000 35.0000 20.0000 15.0000
[============================================ ] 62 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 32
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000
15.0000 25.0000 35.0000 15.0000
[============================================== ] 64 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 33
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 8.0000
15.0000 15.0000 35.0000 15.0000 10.0000
[=============================================== ] 66 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 34
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
20.0000 20.0000 25.0000 20.0000 10.0000
[================================================= ] 68 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 35
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 8
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000
15.0000 20.0000 30.0000 15.0000
[================================================== ] 70 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 36
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
20.0000 15.0000 30.0000 15.0000 10.0000
[=================================================== ] 72 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 37
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
20.0000 10.0000 30.0000 20.0000 20.0000
[===================================================== ] 74 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 38
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
20.0000 15.0000 30.0000 20.0000 10.0000
[====================================================== ] 76 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 39
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 4.0000 5.0000 7.0000
20.0000 30.0000 20.0000 15.0000 10.0000
[======================================================= ] 78 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 40
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 45.0000 25.0000 10.0000
[========================================================= ] 80 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 41
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000 6.0000
10.0000 35.0000 20.0000 15.0000 15.0000
[========================================================== ] 82 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 42
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 50.0000 20.0000 10.0000
[============================================================ ] 84 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 43
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000 8.0000
10.0000 45.0000 15.0000 10.0000 10.0000
[============================================================= ] 86 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 44
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 45.0000 15.0000 10.0000
[============================================================== ] 88 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 45
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
20.0000 50.0000 25.0000
[================================================================ ] 90 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 46
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
15.0000 50.0000 20.0000
[================================================================= ] 92 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 47
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
25.0000 50.0000 20.0000
[================================================================== ] 94 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 48
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 3
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
25.0000 50.0000 25.0000
[==================================================================== ] 96 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 49
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000
10.0000 25.0000 40.0000 20.0000
[===================================================================== ] 98 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 50
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
20.0000 50.0000 20.0000
[=======================================================================] 100 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 51
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 6.0000
15.0000 50.0000 20.0000 10.0000
[=======================================================================] 100 %
>
> plot(samples)
Generating plot...
*** caught segfault ***
address 0x1, cause 'memory not mapped'
*** recursive gc invocation
*** recursive gc invocation
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Traceback:
1: super()
2: fetch_ggproto(super(), name)
3: fetch_ggproto(x, name)
4: `$.ggproto`(self, "limits")
5: self$limits
6: f(..., self = self)
7: self$is_empty()
8: f(..., self = self)
9: scale$get_breaks(sort(continuous_range))
10: view_scale_primary(scale, limits, continuous_range)
11: view_scales_from_scale(scale_x, self$limits$x, self$expand)
12: f(..., self = self)
13: self$coord$setup_panel_params(scale_x, scale_y, params = self$coord_params)
14: (function (scale_x, scale_y) { self$coord$setup_panel_params(scale_x, scale_y, params = self$coord_params)})(dots[[1L]][[3L]], dots[[2L]][[3L]])
15: mapply(FUN = f, ..., SIMPLIFY = FALSE)
16: Map(setup_panel_params, scales_x, scales_y)
17: f(..., self = self)
18: layout$setup_panel_params()
19: ggplot_build.ggplot(x)
20: ggplot_build(x)
21: print.ggplot(x)
22: (function (x, ...) UseMethod("print"))(x)
An irrecoverable exception occurred. R is aborting now ...
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 1.0.0
Check: examples
Result: ERROR
Running examples in ‘hdpGLM-Ex.R’ failed
The error most likely occurred in:
> ### Name: plot.dpGLM
> ### Title: Default plot for class dpGLM
> ### Aliases: plot.dpGLM
>
> ### ** Examples
>
> # Note: this example is just for illustration. MCMC iterations are very reduced
> set.seed(10)
> n = 20
> data = tibble::data_frame(x1 = rnorm(n, -3),
+ x2 = rnorm(n, 3),
+ z = sample(1:3, n, replace=TRUE),
+ y =I(z==1) * (3 + 4*x1 - x2 + rnorm(n)) +
+ I(z==2) * (3 + 2*x1 + x2 + rnorm(n)) +
+ I(z==3) * (3 - 4*x1 - x2 + rnorm(n)) ,
+ )
Warning: `data_frame()` was deprecated in tibble 1.1.0.
Please use `tibble()` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
>
> ## estimation
> mcmc = list(burn.in=1, n.iter=50)
> samples = hdpGLM(y ~ x1 + x2, data=data, mcmc=mcmc, n.display=1)
Preparing for estimation ...
Warning in model.matrix.default(mt1, reg.matrix, stats::contrasts) :
non-list contrasts argument ignored
Estimation in progress ...
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 1
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 1
Current number of active clusters : 1
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000e+00
1.0000e+02
[=== ] 3 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 2
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
15.0000 20.0000 25.0000 10.0000 20.0000
[===== ] 5 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 3
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 8.0000
10.0000 15.0000 50.0000 10.0000
[====== ] 7 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 4
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 8.0000
10.0000 15.0000 50.0000 10.0000 15.0000
[======= ] 9 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 5
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000
10.0000 15.0000 50.0000 20.0000
[========= ] 11 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 6
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 8.0000
15.0000 15.0000 50.0000 15.0000
[========== ] 13 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 7
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000 8.0000
10.0000 45.0000 20.0000 20.0000
[=========== ] 15 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 8
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 4.0000 5.0000 8.0000
50.0000 10.0000 10.0000 20.0000
[============= ] 17 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 9
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 5.0000 6.0000 8.0000
45.0000 15.0000 10.0000 20.0000
[============== ] 19 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 10
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 4.0000 5.0000 8.0000
45.0000 10.0000 20.0000 25.0000
[================ ] 21 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 11
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 5.0000 8.0000
40.0000 20.0000 25.0000
[================= ] 23 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 12
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 5.0000 7.0000 8.0000
50.0000 20.0000 10.0000 10.0000
[================== ] 25 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 13
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 4.0000 5.0000 8.0000
10.0000 45.0000 15.0000 15.0000 10.0000
[==================== ] 27 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 14
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000
20.0000 45.0000 20.0000
[===================== ] 29 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 15
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000 8.0000
10.0000 50.0000 25.0000 10.0000
[====================== ] 31 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 16
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 10.0000 50.0000 10.0000
[======================== ] 33 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 17
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 10.0000 50.0000 15.0000
[========================= ] 35 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 18
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 15.0000 50.0000 10.0000
[=========================== ] 37 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 19
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000
25.0000 50.0000 15.0000
[============================ ] 39 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 20
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000
25.0000 50.0000 20.0000
[============================= ] 41 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 21
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 10.0000 50.0000 15.0000
[=============================== ] 43 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 22
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 10.0000 50.0000 15.0000
[================================ ] 45 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 23
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
20.0000 15.0000 50.0000 15.0000
[================================= ] 47 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 24
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
10.0000 15.0000 50.0000 10.0000 15.0000
[=================================== ] 49 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 25
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 50.0000 15.0000 15.0000
[==================================== ] 50 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 26
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 50.0000 20.0000 10.0000
[====================================== ] 52 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 27
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 45.0000 20.0000 15.0000
[======================================= ] 54 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 28
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
15.0000 15.0000 30.0000 20.0000 15.0000
[======================================== ] 56 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 29
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
15.0000 10.0000 35.0000 20.0000 20.0000
[========================================== ] 58 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 30
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
10.0000 45.0000 20.0000 20.0000
[=========================================== ] 60 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 31
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
15.0000 15.0000 35.0000 20.0000 15.0000
[============================================ ] 62 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 32
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000
15.0000 25.0000 35.0000 15.0000
[============================================== ] 64 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 33
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 8.0000
15.0000 15.0000 35.0000 15.0000 10.0000
[=============================================== ] 66 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 34
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
20.0000 20.0000 25.0000 20.0000 10.0000
[================================================= ] 68 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 35
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 8
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000
15.0000 20.0000 30.0000 15.0000
[================================================== ] 70 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 36
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
20.0000 15.0000 30.0000 15.0000 10.0000
[=================================================== ] 72 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 37
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
20.0000 10.0000 30.0000 20.0000 20.0000
[===================================================== ] 74 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 38
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
20.0000 15.0000 30.0000 20.0000 10.0000
[====================================================== ] 76 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 39
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 4.0000 5.0000 7.0000
20.0000 30.0000 20.0000 15.0000 10.0000
[======================================================= ] 78 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 40
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 45.0000 25.0000 10.0000
[========================================================= ] 80 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 41
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000 6.0000
10.0000 35.0000 20.0000 15.0000 15.0000
[========================================================== ] 82 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 42
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 50.0000 20.0000 10.0000
[============================================================ ] 84 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 43
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000 8.0000
10.0000 45.0000 15.0000 10.0000 10.0000
[============================================================= ] 86 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 44
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 45.0000 15.0000 10.0000
[============================================================== ] 88 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 45
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
20.0000 50.0000 25.0000
[================================================================ ] 90 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 46
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
15.0000 50.0000 20.0000
[================================================================= ] 92 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 47
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
25.0000 50.0000 20.0000
[================================================================== ] 94 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 48
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 3
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
25.0000 50.0000 25.0000
[==================================================================== ] 96 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 49
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000
10.0000 25.0000 40.0000 20.0000
[===================================================================== ] 98 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 50
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
20.0000 50.0000 20.0000
[=======================================================================] 100 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 51
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 6.0000
15.0000 50.0000 20.0000 10.0000
[=======================================================================] 100 %
>
> plot(samples)
Generating plot...
*** caught segfault ***
address 1, cause 'memory not mapped'
*** recursive gc invocation
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Traceback:
1: cbind(inds, 1:npar)
2: cbind(vals[cbind(inds, 1:npar)], vals[cbind(inds + gap, 1:npar)])
3: HPDinterval.mcmc(coda::as.mcmc(x))
4: coda::HPDinterval(coda::as.mcmc(x))
5: (function (x) { return(coda::HPDinterval(coda::as.mcmc(x))[, "lower"])})(sample)
6: .Call(dplyr_mask_eval_all_summarise, quo, private)
7: mask$eval_all_summarise(quo)
8: withCallingHandlers({ for (i in seq_along(dots)) { mask$across_cache_reset() context_poke("column", old_current_column) quosures <- expand_across(dots[[i]]) quosures_results <- vector(mode = "list", length = length(quosures)) for (k in seq_along(quosures)) { quo <- quosures[[k]] quo_data <- attr(quo, "dplyr:::data") if (!is.null(quo_data$column)) { context_poke("column", quo_data$column) } chunks_k <- mask$eval_all_summarise(quo) if (is.null(chunks_k)) { next } types_k <- withCallingHandlers(vec_ptype_common(!!!chunks_k), vctrs_error_incompatible_type = function(cnd) { abort(class = "dplyr:::error_summarise_incompatible_combine", parent = cnd) }) chunks_k <- vec_cast_common(!!!chunks_k, .to = types_k) quosures_results[[k]] <- list(chunks = chunks_k, types = types_k) } for (k in seq_along(quosures)) { quo <- quosures[[k]] quo_data <- attr(quo, "dplyr:::data") quo_result <- quosures_results[[k]] if (is.null(quo_result)) { next } types_k <- quo_result$types chunks_k <- quo_result$chunks if (!quo_data$is_named && is.data.frame(types_k)) { chunks_extracted <- .Call(dplyr_extract_chunks, chunks_k, types_k) walk2(chunks_extracted, names(types_k), function(chunks_k_j, nm) { mask$add_one(nm, chunks_k_j) }) chunks <- append(chunks, chunks_extracted) types <- append(types, as.list(types_k)) out_names <- c(out_names, names(types_k)) } else { name <- quo_data$name_auto mask$add_one(name, chunks_k) chunks <- append(chunks, list(chunks_k)) types <- append(types, list(types_k)) out_names <- c(out_names, name) } } } recycle_info <- .Call(dplyr_summarise_recycle_chunks, chunks, mask$get_rows(), types) chunks <- recycle_info$chunks sizes <- recycle_info$sizes for (i in seq_along(chunks)) { result <- vec_c(!!!chunks[[i]], .ptype = types[[i]]) cols[[out_names[i]]] <- result }}, error = function(e) { local_call_step(dots = dots, .index = i, .fn = "summarise", .dot_data = inherits(e, "rlang_error_data_pronoun_not_found")) call_step <- peek_call_step() error_name <- call_step$error_name show_group_details <- TRUE if (inherits(e, "dplyr:::error_summarise_incompatible_combine")) { show_group_details <- FALSE bullets <- c(x = glue("`{error_name}` must return compatible vectors across groups", .envir = peek_call_step()), i = cnd_bullet_combine_details(e$parent$x, e$parent$x_arg), i = cnd_bullet_combine_details(e$parent$y, e$parent$y_arg)) } else if (inherits(e, "dplyr:::summarise_unsupported_type")) { bullets <- c(x = glue("`{error_name}` must be a vector, not {friendly_type_of(result)}.", result = e$result), i = cnd_bullet_rowwise_unlist()) } else if (inherits(e, "dplyr:::summarise_incompatible_size")) { peek_mask()$set_current_group(e$group) bullets <- c(x = glue("`{error_name}` must be size {or_1(expected_size)}, not {size}.", expected_size = e$expected_size, size = e$size), i = glue("An earlier column had size {expected_size}.", expected_size = e$expected_size)) } else if (inherits(e, "dplyr:::summarise_mixed_null")) { show_group_details <- FALSE bullets <- c(x = glue("`{error_name}` must return compatible vectors across groups."), i = "Cannot combine NULL and non NULL results.") } else { bullets <- c(x = conditionMessage(e)) } bullets <- c(cnd_bullet_header(), i = cnd_bullet_column_info(), bullets, i = if (show_group_details) cnd_bullet_cur_group_label()) abort(bullets, class = "dplyr_error")})
9: summarise_cols(.data, ..., caller_env = caller_env())
10: summarise.grouped_df(.tbl, !!!funs)
11: summarise(.tbl, !!!funs)
12: dplyr::summarize_all(., .funs = list(Mean = "mean", Median = "median", SD = "sd", HPD.lower = "HPD.lower", HPD.upper = "HPD.upper"))
13: dplyr::ungroup(.)
14: x$samples %>% tibble::as_data_frame(.) %>% tidyr::gather(key = Parameter, value = sample, -k) %>% dplyr::group_by(k, Parameter) %>% dplyr::summarize_all(.funs = list(Mean = "mean", Median = "median", SD = "sd", HPD.lower = "HPD.lower", HPD.upper = "HPD.upper")) %>% dplyr::ungroup(.)
15: summary.dpGLM(x)
16: summary(x)
17: dplyr::select(., k, Parameter, term, Mean, dplyr::contains("HPD"))
18: summary(x) %>% dplyr::select(k, Parameter, term, Mean, dplyr::contains("HPD"))
19: is.data.frame(y)
20: same_src.data.frame(x, y)
21: same_src(x, y)
22: auto_copy(x, y, copy = copy)
23: left_join.data.frame(., summary(x) %>% dplyr::select(k, Parameter, term, Mean, dplyr::contains("HPD")), by = c("k", "Parameter"))
24: dplyr::left_join(., summary(x) %>% dplyr::select(k, Parameter, term, Mean, dplyr::contains("HPD")), by = c("k", "Parameter"))
25: dplyr::mutate(., Parameter = paste0(stringr::str_extract(Parameter, "beta"), "[", stringr::str_extract(Parameter, "[0-9]+"), "]"), k = paste0("Cluster~", k, sep = ""))
26: x$samples %>% tibble::as_data_frame(.) %>% dplyr::select(-dplyr::contains("sigma")) %>% tidyr::gather(key = Parameter, value = values, -k) %>% dplyr::left_join(., summary(x) %>% dplyr::select(k, Parameter, term, Mean, dplyr::contains("HPD")), by = c("k", "Parameter")) %>% dplyr::mutate(Parameter = paste0(stringr::str_extract(Parameter, "beta"), "[", stringr::str_extract(Parameter, "[0-9]+"), "]"), k = paste0("Cluster~", k, sep = ""))
27: plot.dpGLM(samples)
28: plot(samples)
An irrecoverable exception occurred. R is aborting now ...
Flavor: r-patched-solaris-x86
Version: 1.0.0
Check: examples
Result: ERROR
Running examples in ‘hdpGLM-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: plot.dpGLM
> ### Title: Default plot for class dpGLM
> ### Aliases: plot.dpGLM
>
> ### ** Examples
>
> # Note: this example is just for illustration. MCMC iterations are very reduced
> set.seed(10)
> n = 20
> data = tibble::data_frame(x1 = rnorm(n, -3),
+ x2 = rnorm(n, 3),
+ z = sample(1:3, n, replace=TRUE),
+ y =I(z==1) * (3 + 4*x1 - x2 + rnorm(n)) +
+ I(z==2) * (3 + 2*x1 + x2 + rnorm(n)) +
+ I(z==3) * (3 - 4*x1 - x2 + rnorm(n)) ,
+ )
Warning: `data_frame()` was deprecated in tibble 1.1.0.
Please use `tibble()` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
>
> ## estimation
> mcmc = list(burn.in=1, n.iter=50)
> samples = hdpGLM(y ~ x1 + x2, data=data, mcmc=mcmc, n.display=1)
Preparing for estimation ...
Warning in model.matrix.default(mt1, reg.matrix, stats::contrasts) :
non-list contrasts argument ignored
Estimation in progress ...
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 1
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 1
Current number of active clusters : 1
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000e+00
1.0000e+02
[=== ] 3 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 2
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
15.0000 20.0000 25.0000 10.0000 20.0000
[===== ] 5 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 3
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 8.0000
10.0000 15.0000 50.0000 10.0000
[====== ] 7 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 4
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 8.0000
10.0000 15.0000 50.0000 10.0000 15.0000
[======= ] 9 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 5
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000
10.0000 15.0000 50.0000 20.0000
[========= ] 11 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 6
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 8.0000
15.0000 15.0000 50.0000 15.0000
[========== ] 13 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 7
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000 8.0000
10.0000 45.0000 20.0000 20.0000
[=========== ] 15 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 8
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 4.0000 5.0000 8.0000
50.0000 10.0000 10.0000 20.0000
[============= ] 17 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 9
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 5.0000 6.0000 8.0000
45.0000 15.0000 10.0000 20.0000
[============== ] 19 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 10
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 4.0000 5.0000 8.0000
45.0000 10.0000 20.0000 25.0000
[================ ] 21 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 11
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 5.0000 8.0000
40.0000 20.0000 25.0000
[================= ] 23 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 12
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 5.0000 7.0000 8.0000
50.0000 20.0000 10.0000 10.0000
[================== ] 25 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 13
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 4.0000 5.0000 8.0000
10.0000 45.0000 15.0000 15.0000 10.0000
[==================== ] 27 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 14
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000
20.0000 45.0000 20.0000
[===================== ] 29 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 15
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000 8.0000
10.0000 50.0000 25.0000 10.0000
[====================== ] 31 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 16
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 10.0000 50.0000 10.0000
[======================== ] 33 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 17
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 10.0000 50.0000 15.0000
[========================= ] 35 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 18
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 15.0000 50.0000 10.0000
[=========================== ] 37 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 19
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000
25.0000 50.0000 15.0000
[============================ ] 39 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 20
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000
25.0000 50.0000 20.0000
[============================= ] 41 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 21
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 10.0000 50.0000 15.0000
[=============================== ] 43 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 22
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 10.0000 50.0000 15.0000
[================================ ] 45 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 23
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
20.0000 15.0000 50.0000 15.0000
[================================= ] 47 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 24
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
10.0000 15.0000 50.0000 10.0000 15.0000
[=================================== ] 49 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 25
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 50.0000 15.0000 15.0000
[==================================== ] 50 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 26
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 50.0000 20.0000 10.0000
[====================================== ] 52 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 27
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 45.0000 20.0000 15.0000
[======================================= ] 54 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 28
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
15.0000 15.0000 30.0000 20.0000 15.0000
[======================================== ] 56 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 29
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
15.0000 10.0000 35.0000 20.0000 20.0000
[========================================== ] 58 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 30
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
10.0000 45.0000 20.0000 20.0000
[=========================================== ] 60 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 31
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
15.0000 15.0000 35.0000 20.0000 15.0000
[============================================ ] 62 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 32
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000
15.0000 25.0000 35.0000 15.0000
[============================================== ] 64 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 33
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 8.0000
15.0000 15.0000 35.0000 15.0000 10.0000
[=============================================== ] 66 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 34
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
20.0000 20.0000 25.0000 20.0000 10.0000
[================================================= ] 68 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 35
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 8
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000
15.0000 20.0000 30.0000 15.0000
[================================================== ] 70 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 36
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
20.0000 15.0000 30.0000 15.0000 10.0000
[=================================================== ] 72 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 37
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
20.0000 10.0000 30.0000 20.0000 20.0000
[===================================================== ] 74 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 38
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
20.0000 15.0000 30.0000 20.0000 10.0000
[====================================================== ] 76 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 39
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 4.0000 5.0000 7.0000
20.0000 30.0000 20.0000 15.0000 10.0000
[======================================================= ] 78 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 40
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 45.0000 25.0000 10.0000
[========================================================= ] 80 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 41
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000 6.0000
10.0000 35.0000 20.0000 15.0000 15.0000
[========================================================== ] 82 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 42
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 50.0000 20.0000 10.0000
[============================================================ ] 84 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 43
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000 8.0000
10.0000 45.0000 15.0000 10.0000 10.0000
[============================================================= ] 86 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 44
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 45.0000 15.0000 10.0000
[============================================================== ] 88 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 45
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
20.0000 50.0000 25.0000
[================================================================ ] 90 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 46
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
15.0000 50.0000 20.0000
[================================================================= ] 92 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 47
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
25.0000 50.0000 20.0000
[================================================================== ] 94 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 48
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 3
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
25.0000 50.0000 25.0000
[==================================================================== ] 96 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 49
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000
10.0000 25.0000 40.0000 20.0000
[===================================================================== ] 98 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 50
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
20.0000 50.0000 20.0000
[=======================================================================] 100 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 51
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 6.0000
15.0000 50.0000 20.0000 10.0000
[=======================================================================] 100 %
>
> plot(samples)
Generating plot...
*** caught segfault ***
address 0x562800000004, cause 'memory not mapped'
Fatal error: *** recursive gc invocation
Fatal error: *** recursive gc invocation
Flavor: r-release-linux-x86_64
Version: 1.0.0
Check: running examples for arch ‘x64’
Result: ERROR
Running examples in 'hdpGLM-Ex.R' failed
The error most likely occurred in:
> ### Name: plot_hdpglm
> ### Title: Plot posterior distribution of tau and posterior expectation of
> ### beta
> ### Aliases: plot_hdpglm
>
> ### ** Examples
>
>
> library(magrittr)
> # Note: this example is just for illustration. MCMC iterations are very reduced
> set.seed(10)
> n = 20
> data.context1 = tibble::data_frame(x1 = rnorm(n, -3),
+ x2 = rnorm(n, 3),
+ z = sample(1:3, n, replace=TRUE),
+ y =I(z==1) * (3 + 4*x1 - x2 + rnorm(n)) +
+ I(z==2) * (3 + 2*x1 + x2 + rnorm(n)) +
+ I(z==3) * (3 - 4*x1 - x2 + rnorm(n)) ,
+ w = 20
+ )
> data.context2 = tibble::data_frame(x1 = rnorm(n, -3),
+ x2 = rnorm(n, 3),
+ z = sample(1:2, n, replace=TRUE),
+ y =I(z==1) * (1 + 3*x1 - 2*x2 + rnorm(n)) +
+ I(z==2) * (1 - 2*x1 + x2 + rnorm(n)),
+ w = 10
+ )
> data = data.context1 %>%
+ dplyr::bind_rows(data.context2)
>
> ## estimation
> mcmc = list(burn.in=1, n.iter=50)
> samples = hdpGLM(y ~ x1 + x2, y ~ w, data=data, mcmc=mcmc, n.display=1)
Preparing for estimation ...
Warning in model.matrix.default(mt1, reg.matrix, stats::contrasts) :
non-list contrasts argument ignored
Warning in model.matrix.default(mt1, reg.matrix, stats::contrasts) :
non-list contrasts argument ignored
Estimation in progress ...
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 1
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 1
Maximum Number of clusters active in the current iteration : 1
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000e+00
1.0000e+02
Clusters in context 2
1.0000
95.0000
[=== ] 3 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 2
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 2
Maximum Number of clusters active in the current iteration : 2
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000
25.0000 75.0000
Clusters in context 2
1.0000 3.0000 4.0000
10.0000 55.0000 35.0000
[===== ] 5 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 3
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 4
Maximum Number of clusters active in the current iteration : 4
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 3.0000 5.0000
15.0000 10.0000 40.0000 35.0000
Clusters in context 2
1.0000 2.0000 3.0000 4.0000 7.0000
10.0000 30.0000 30.0000 10.0000 15.0000
[====== ] 7 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 4
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 6
Maximum Number of clusters active in the current iteration : 6
(displaying only clusters with more than 5% of the data)
Clusters in context 1
2.0000 4.0000 5.0000 7.0000
10.0000 35.0000 10.0000 35.0000
Clusters in context 2
3.0000 4.0000 5.0000 7.0000
35.0000 15.0000 20.0000 15.0000
[======= ] 9 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 5
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 8
Maximum Number of clusters active in the current iteration : 8
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 3.0000 4.0000 5.0000 6.0000
10.0000 35.0000 10.0000 10.0000 20.0000
Clusters in context 2
3.0000 7.0000
50.0000 40.0000
[========= ] 11 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 6
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 9
(displaying only clusters with more than 5% of the data)
Clusters in context 1
2.0000 3.0000 5.0000 6.0000 7.0000 8.0000
15.0000 10.0000 10.0000 15.0000 10.0000 25.0000
Clusters in context 2
3.0000 7.0000
50.0000 40.0000
[========== ] 13 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 7
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 8
(displaying only clusters with more than 5% of the data)
Clusters in context 1
2.0000 3.0000 4.0000 5.0000 6.0000 11.0000
20.0000 10.0000 15.0000 15.0000 20.0000 10.0000
Clusters in context 2
3.0000 7.0000
50.0000 40.0000
[=========== ] 15 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 8
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 7
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 4.0000 5.0000 6.0000
15.0000 10.0000 15.0000 25.0000 25.0000
Clusters in context 2
3.0000 7.0000
50.0000 45.0000
[============= ] 17 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 9
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 8
(displaying only clusters with more than 5% of the data)
Clusters in context 1
2.0000 4.0000 5.0000 6.0000
10.0000 20.0000 10.0000 40.0000
Clusters in context 2
3.0000 7.0000
50.0000 45.0000
[============== ] 19 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 10
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 9
(displaying only clusters with more than 5% of the data)
Clusters in context 1
2.0000 4.0000 5.0000 6.0000
15.0000 15.0000 10.0000 35.0000
Clusters in context 2
3.0000 7.0000
50.0000 40.0000
[================ ] 21 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 11
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 8
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 4.0000 6.0000 7.0000 10.0000
15.0000 15.0000 35.0000 10.0000 10.0000
Clusters in context 2
3.0000 7.0000
50.0000 50.0000
[================= ] 23 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 12
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 8
(displaying only clusters with more than 5% of the data)
Clusters in context 1
4.0000 6.0000 7.0000 9.0000
15.0000 40.0000 15.0000 10.0000
Clusters in context 2
3.0000 7.0000
50.0000 50.0000
[================== ] 25 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 13
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 8
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 4.0000 6.0000 7.0000
10.0000 15.0000 45.0000 10.0000
Clusters in context 2
3.0000 7.0000
50.0000 50.0000
[==================== ] 27 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 14
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 8
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 3.0000 4.0000 6.0000 7.0000
10.0000 10.0000 15.0000 40.0000 10.0000
Clusters in context 2
3.0000 7.0000
45.0000 45.0000
[===================== ] 29 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 15
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 5
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 3.0000 4.0000 6.0000
20.0000 10.0000 15.0000 50.0000
Clusters in context 2
3.0000 7.0000
50.0000 45.0000
[====================== ] 31 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 16
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 6
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 4.0000 6.0000
20.0000 15.0000 50.0000
Clusters in context 2
3.0000 7.0000
50.0000 45.0000
[======================== ] 33 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 17
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 6
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 3.0000 4.0000 5.0000 6.0000
10.0000 10.0000 10.0000 15.0000 50.0000
Clusters in context 2
1.0000 3.0000 7.0000
10.0000 50.0000 40.0000
[========================= ] 35 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 18
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 6
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 3.0000 6.0000
10.0000 20.0000 10.0000 50.0000
Clusters in context 2
1.0000 3.0000 7.0000
10.0000 50.0000 40.0000
[=========================== ] 37 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 19
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 7
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 3.0000 6.0000 7.0000
10.0000 15.0000 10.0000 45.0000 10.0000
Clusters in context 2
1.0000 3.0000 6.0000 7.0000
10.0000 50.0000 10.0000 30.0000
[============================ ] 39 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 20
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 7
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 5.0000 6.0000
15.0000 10.0000 15.0000 45.0000
Clusters in context 2
1.0000 3.0000 7.0000
10.0000 50.0000 35.0000
[============================= ] 41 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 21
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 6
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 4.0000 6.0000
20.0000 10.0000 10.0000 50.0000
Clusters in context 2
1.0000 3.0000 7.0000
10.0000 45.0000 40.0000
[=============================== ] 43 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 22
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 6
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 6.0000
20.0000 15.0000 50.0000
Clusters in context 2
1.0000 3.0000 7.0000
10.0000 50.0000 40.0000
[================================ ] 45 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 23
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 5
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 3.0000 6.0000
25.0000 10.0000 10.0000 50.0000
Clusters in context 2
1.0000 3.0000 7.0000
15.0000 50.0000 35.0000
[================================= ] 47 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 24
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 5
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 3.0000 4.0000 6.0000
20.0000 10.0000 10.0000 10.0000 50.0000
Clusters in context 2
1.0000 3.0000 7.0000
15.0000 50.0000 35.0000
[=================================== ] 49 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 25
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 5
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 3.0000 6.0000
25.0000 15.0000 50.0000
Clusters in context 2
1.0000 3.0000 7.0000
25.0000 40.0000 25.0000
[==================================== ] 50 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 26
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 4
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 3.0000 6.0000
25.0000 10.0000 15.0000 50.0000
Clusters in context 2
1.0000 3.0000 7.0000
35.0000 45.0000 15.0000
[====================================== ] 52 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 27
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 4
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 3.0000 6.0000
25.0000 10.0000 15.0000 50.0000
Clusters in context 2
1.0000 3.0000
45.0000 45.0000
[======================================= ] 54 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 28
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 6
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 3.0000 4.0000 6.0000
20.0000 15.0000 10.0000 45.0000
Clusters in context 2
1.0000 3.0000
50.0000 50.0000
[======================================== ] 56 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 29
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 6
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 3.0000 6.0000
20.0000 10.0000 10.0000 50.0000
Clusters in context 2
1.0000 3.0000
45.0000 50.0000
[========================================== ] 58 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 30
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 6
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 3.0000 6.0000
15.0000 10.0000 15.0000 50.0000
Clusters in context 2
1.0000 3.0000
50.0000 50.0000
[=========================================== ] 60 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 31
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 5
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 3.0000 6.0000
25.0000 15.0000 50.0000
Clusters in context 2
1.0000 3.0000
50.0000 50.0000
[============================================ ] 62 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 32
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 5
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 3.0000 6.0000
20.0000 20.0000 50.0000
Clusters in context 2
1.0000 3.0000
50.0000 50.0000
[============================================== ] 64 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 33
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 6
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 3.0000 6.0000
15.0000 20.0000 50.0000
Clusters in context 2
1.0000 3.0000
50.0000 50.0000
[=============================================== ] 66 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 34
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 4
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 3.0000 6.0000
25.0000 20.0000 50.0000
Clusters in context 2
1.0000 3.0000
50.0000 45.0000
[================================================= ] 68 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 35
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 4
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 3.0000 6.0000
25.0000 25.0000 45.0000
Clusters in context 2
1.0000 3.0000
50.0000 50.0000
[================================================== ] 70 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 36
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 4
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 3.0000 6.0000
25.0000 25.0000 45.0000
Clusters in context 2
1.0000 3.0000
45.0000 50.0000
[=================================================== ] 72 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 37
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 5
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 3.0000 6.0000
20.0000 25.0000 45.0000
Clusters in context 2
1.0000 2.0000 3.0000
40.0000 10.0000 50.0000
[===================================================== ] 74 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 38
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 4
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 3.0000 5.0000 6.0000
20.0000 30.0000 10.0000 40.0000
Clusters in context 2
1.0000 3.0000
50.0000 50.0000
[====================================================== ] 76 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 39
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 5
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 3.0000 6.0000
20.0000 10.0000 25.0000 40.0000
Clusters in context 2
1.0000 3.0000
50.0000 50.0000
[======================================================= ] 78 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 40
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 7
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 3.0000 4.0000 6.0000
10.0000 20.0000 15.0000 15.0000 30.0000
Clusters in context 2
1.0000 3.0000
45.0000 50.0000
[========================================================= ] 80 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 41
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 5
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 3.0000 4.0000
15.0000 45.0000 20.0000 15.0000
Clusters in context 2
1.0000 3.0000
50.0000 50.0000
[========================================================== ] 82 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 42
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 6
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 3.0000 4.0000 6.0000
15.0000 35.0000 20.0000 15.0000 10.0000
Clusters in context 2
1.0000 2.0000 3.0000
45.0000 10.0000 45.0000
[============================================================ ] 84 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 43
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 6
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 3.0000 4.0000
15.0000 40.0000 20.0000 15.0000
Clusters in context 2
1.0000 3.0000
50.0000 50.0000
[============================================================= ] 86 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 44
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 6
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 3.0000 4.0000
10.0000 45.0000 20.0000 15.0000
Clusters in context 2
1.0000 3.0000
50.0000 50.0000
[============================================================== ] 88 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 45
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 4
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 3.0000 4.0000
10.0000 50.0000 20.0000 20.0000
Clusters in context 2
1.0000 3.0000
50.0000 50.0000
[================================================================ ] 90 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 46
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 4
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 3.0000 4.0000
15.0000 50.0000 20.0000 15.0000
Clusters in context 2
1.0000 3.0000
50.0000 45.0000
[================================================================= ] 92 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 47
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 4
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 3.0000 4.0000
10.0000 50.0000 20.0000 20.0000
Clusters in context 2
1.0000 3.0000
50.0000 50.0000
[================================================================== ] 94 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 48
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 4
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 3.0000 4.0000
15.0000 50.0000 25.0000 10.0000
Clusters in context 2
1.0000 3.0000
45.0000 50.0000
[==================================================================== ] 96 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 49
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 4
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 3.0000
25.0000 50.0000 20.0000
Clusters in context 2
1.0000 3.0000
50.0000 50.0000
[===================================================================== ] 98 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 50
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 4
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 3.0000
20.0000 50.0000 25.0000
Clusters in context 2
1.0000 3.0000
50.0000 50.0000
[=======================================================================] 100 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC iterations : 50
Iteration: 51
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K) : 100
Maximum Number of cluster activated among all contexts : 9
Maximum Number of clusters active in the current iteration : 4
(displaying only clusters with more than 5% of the data)
Clusters in context 1
1.0000 2.0000 3.0000
25.0000 50.0000 20.0000
Clusters in context 2
1.0000 3.0000
45.0000 50.0000
[=======================================================================] 100 %
>
> plot_hdpglm(samples)
Plot being generated ...
Generating summary for beta...
Generating summary for tau...
Generating plots ...
Generating summary for beta...
Generating summary for tau...
Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
> plot_hdpglm(samples, ncol.taus=2, ncol.betas=2, X='x1')
Plot being generated ...
Generating summary for beta...
Generating summary for tau...
Generating plots ...
Generating summary for beta...
Generating summary for tau...
Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
> plot_hdpglm(samples, ncol.taus=2, ncol.betas=2, X='x1', ncol.w=2, nrow.w=1,
+ pred.pexp.beta=TRUE,smooth.line=TRUE )
Plot being generated ...
Generating summary for beta...
Generating summary for tau...
Generating plots ...
Generating summary for beta...
Generating summary for tau...
Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
Generating summary for beta...
Generating summary for tau...
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
>
>
>
>
>
> cleanEx()
detaching 'package:magrittr'
Flavor: r-release-windows-ix86+x86_64
Version: 1.0.0
Check: running examples for arch ‘i386’
Result: ERROR
Running examples in 'hdpGLM-Ex.R' failed
The error most likely occurred in:
> ### Name: plot.dpGLM
> ### Title: Default plot for class dpGLM
> ### Aliases: plot.dpGLM
>
> ### ** Examples
>
> # Note: this example is just for illustration. MCMC iterations are very reduced
> set.seed(10)
> n = 20
> data = tibble::data_frame(x1 = rnorm(n, -3),
+ x2 = rnorm(n, 3),
+ z = sample(1:3, n, replace=TRUE),
+ y =I(z==1) * (3 + 4*x1 - x2 + rnorm(n)) +
+ I(z==2) * (3 + 2*x1 + x2 + rnorm(n)) +
+ I(z==3) * (3 - 4*x1 - x2 + rnorm(n)) ,
+ )
Warning: `data_frame()` was deprecated in tibble 1.1.0.
Please use `tibble()` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
>
> ## estimation
> mcmc = list(burn.in=1, n.iter=50)
> samples = hdpGLM(y ~ x1 + x2, data=data, mcmc=mcmc, n.display=1)
Preparing for estimation ...
Warning in model.matrix.default(mt1, reg.matrix, stats::contrasts) :
non-list contrasts argument ignored
Estimation in progress ...
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 1
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 1
Current number of active clusters : 1
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000e+00
1.0000e+02
[=== ] 3 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 2
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
15.0000 20.0000 25.0000 10.0000 20.0000
[===== ] 5 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 3
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 8.0000
10.0000 15.0000 50.0000 10.0000
[====== ] 7 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 4
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 8.0000
10.0000 15.0000 50.0000 10.0000 15.0000
[======= ] 9 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 5
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000
10.0000 15.0000 50.0000 20.0000
[========= ] 11 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 6
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 8.0000
15.0000 15.0000 50.0000 15.0000
[========== ] 13 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 7
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000 8.0000
10.0000 45.0000 20.0000 20.0000
[=========== ] 15 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 8
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 4.0000 5.0000 8.0000
50.0000 10.0000 10.0000 20.0000
[============= ] 17 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 9
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 5.0000 6.0000 8.0000
45.0000 15.0000 10.0000 20.0000
[============== ] 19 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 10
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 4.0000 5.0000 8.0000
45.0000 10.0000 20.0000 25.0000
[================ ] 21 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 11
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 5.0000 8.0000
40.0000 20.0000 25.0000
[================= ] 23 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 12
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
3.0000 5.0000 7.0000 8.0000
50.0000 20.0000 10.0000 10.0000
[================== ] 25 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 13
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 4.0000 5.0000 8.0000
10.0000 45.0000 15.0000 15.0000 10.0000
[==================== ] 27 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 14
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000
20.0000 45.0000 20.0000
[===================== ] 29 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 15
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000 8.0000
10.0000 50.0000 25.0000 10.0000
[====================== ] 31 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 16
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 10.0000 50.0000 10.0000
[======================== ] 33 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 17
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 10.0000 50.0000 15.0000
[========================= ] 35 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 18
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 15.0000 50.0000 10.0000
[=========================== ] 37 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 19
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000
25.0000 50.0000 15.0000
[============================ ] 39 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 20
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 5.0000
25.0000 50.0000 20.0000
[============================= ] 41 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 21
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 10.0000 50.0000 15.0000
[=============================== ] 43 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 22
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
25.0000 10.0000 50.0000 15.0000
[================================ ] 45 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 23
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 5.0000
20.0000 15.0000 50.0000 15.0000
[================================= ] 47 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 24
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
10.0000 15.0000 50.0000 10.0000 15.0000
[=================================== ] 49 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 25
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 50.0000 15.0000 15.0000
[==================================== ] 50 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 26
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 50.0000 20.0000 10.0000
[====================================== ] 52 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 27
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 45.0000 20.0000 15.0000
[======================================= ] 54 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 28
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
15.0000 15.0000 30.0000 20.0000 15.0000
[======================================== ] 56 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 29
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
15.0000 10.0000 35.0000 20.0000 20.0000
[========================================== ] 58 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 30
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
10.0000 45.0000 20.0000 20.0000
[=========================================== ] 60 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 31
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
15.0000 15.0000 35.0000 20.0000 15.0000
[============================================ ] 62 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 32
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000
15.0000 25.0000 35.0000 15.0000
[============================================== ] 64 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 33
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 8.0000
15.0000 15.0000 35.0000 15.0000 10.0000
[=============================================== ] 66 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 34
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 7
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
20.0000 20.0000 25.0000 20.0000 10.0000
[================================================= ] 68 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 35
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 8
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000
15.0000 20.0000 30.0000 15.0000
[================================================== ] 70 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 36
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
20.0000 15.0000 30.0000 15.0000 10.0000
[=================================================== ] 72 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 37
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
20.0000 10.0000 30.0000 20.0000 20.0000
[===================================================== ] 74 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 38
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000 5.0000
20.0000 15.0000 30.0000 20.0000 10.0000
[====================================================== ] 76 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 39
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 3.0000 4.0000 5.0000 7.0000
20.0000 30.0000 20.0000 15.0000 10.0000
[======================================================= ] 78 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 40
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 45.0000 25.0000 10.0000
[========================================================= ] 80 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 41
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000 6.0000
10.0000 35.0000 20.0000 15.0000 15.0000
[========================================================== ] 82 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 42
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 50.0000 20.0000 10.0000
[============================================================ ] 84 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 43
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000 8.0000
10.0000 45.0000 15.0000 10.0000 10.0000
[============================================================= ] 86 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 44
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 7
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 5.0000
15.0000 45.0000 15.0000 10.0000
[============================================================== ] 88 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 45
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
20.0000 50.0000 25.0000
[================================================================ ] 90 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 46
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 6
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
15.0000 50.0000 20.0000
[================================================================= ] 92 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 47
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 4
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
25.0000 50.0000 20.0000
[================================================================== ] 94 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 48
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 3
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
25.0000 50.0000 25.0000
[==================================================================== ] 96 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 49
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
1.0000 2.0000 3.0000 4.0000
10.0000 25.0000 40.0000 20.0000
[===================================================================== ] 98 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 50
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000
20.0000 50.0000 20.0000
[=======================================================================] 100 %
-----------------------------------------------------
MCMC in progress ....
Family of the distribution of the outcome variable of the mixture components: gaussian
Burn-in: 1
Number of MCMC samples: 50
Iteration: 51
Acceptance rate for beta : 1
Average acceptance rate for beta : 1
Maximum Number of cluster allowed (K): 100
Maximum Number of cluster activated : 8
Current number of active clusters : 5
Percentage of data classified in each clusters k at current iteraction (displaying only clusters with more than 5% of the data)
2.0000 3.0000 4.0000 6.0000
15.0000 50.0000 20.0000 10.0000
[=======================================================================] 100 %
>
> plot(samples)
Generating plot...
>
>
>
>
>
> cleanEx()
Flavor: r-oldrel-windows-ix86+x86_64