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'
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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 ...