remotes::install_github("c-taimal/BayesianMDLGA")library(BayesianMDLGA)str(sims)## Classes 'tbl_df', 'tbl' and 'data.frame': 1096 obs. of 3 variables:
## $ ts1: num 42.6 39.1 57.5 22.2 66.3 ...
## $ ts2: num 34.6 32.8 31.6 26.5 29.9 ...
## $ ts3: num 67.4 32.2 64.8 44.6 65.4 ...
str(param)## List of 33
## $ nombre_datos : chr "DataCPSimRebases"
## $ frecuencia_datos : num 7
## $ n_datos : chr "TODOS"
## $ diarios0_rebases1 : num 0
## $ valor_de_rebase : num 63.2
## $ r : num 50
## $ k : num 50
## $ penalty : chr "BMDL"
## $ max_num_cp : num 20
## $ prob_inicial : num 0.06
## $ prob_volado : num 0.5
## $ probs_muta : num [1:3] 0.3 0.4 0.3
## $ mutaciones : num [1:3] -1 0 1
## $ dist_extremos : num 10
## $ prob_para_sin_cp : num 0.5
## $ cp_real : chr "sin cp_real"
## $ quita_ini0_fin1 : num 0
## $ probs_rank0_MDL1 : num 0
## $ nombre_carpeta_pdf : chr "Figures"
## $ nombre_carpeta_RData : chr "Data"
## $ cuantos_mejores_cp_graf: num 100
## $ my_data : NULL
## $ minimo_numero_de_cp : num 1
## $ probs_nuevos_muta0N : num [1:3] 0.8 0.1 0.1
## $ rf_type : chr "W"
## $ initial_val_optim : num [1:2] 0.1 0.5
## $ mat_low_upp : num [1:2, 1:2] 1e-04 1e-08 1e+01 1e+05
## $ vec_dist_a_priori : chr [1:2] "Gamma" "Gamma"
## $ mat_phi : num [1:2, 1:2] 1 3 2 1.2
## $ ajuste_bloque : logi TRUE
## $ print_progress_bar : logi TRUE
## $ print_progress_plots : logi TRUE
## $ value_set_seed : num 123
str(pm_25)## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 1096 obs. of 1 variable:
## $ PM2.5: num 393 277 303 336 329 201 237 235 292 276 ...
## - attr(*, "spec")=List of 3
## ..$ cols :List of 1
## .. ..$ PM2.5: list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_number" "collector"
## ..$ default: list()
## .. ..- attr(*, "class")= chr [1:2] "collector_guess" "collector"
## ..$ delim : chr ","
## ..- attr(*, "class")= chr "col_spec"
## - attr(*, "problems")=<externalptr>
summary(pm_25)## PM2.5
## Min. : 13.0
## 1st Qu.:207.0
## Median :300.0
## Mean :288.3
## 3rd Qu.:377.0
## Max. :785.0
library(tidyverse)
sims |>
pivot_longer(
cols = everything(),
names_to = "set",
values_to = "measurement"
) |>
mutate(t = row_number()) |>
ggplot(aes(x = t, y = measurement)) +
geom_line() +
facet_wrap(vars(set), ncol = 1)
AG_BMDL_r_paso(DataCPSimRebases, param)## | | | 0%
## | |= | 2%
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## Se guardo la imagen:
## /tmp/RtmpK6V0do/Figures/Fig_CP_repetidos_DataCPSimRebases_rf_W_Gamma(1,2)_Gamma(3,1.2)_r50_k50_BMDL_586.pdf
## Se guardo la imagen:
## /tmp/RtmpK6V0do/Figures/Fig_4AGBMDL_DataCPSimRebases_rf_W_Gamma(1,2)_Gamma(3,1.2)_r50_k50_BMDL_586.pdf
## Se guardo el archivo:
## /tmp/RtmpK6V0do/Data/Dat_AGBMDL_DataCPSimRebases_rf_W_Gamma(1,2)_Gamma(3,1.2)_r50_k50_BMDL_586.RData
## $x
## [1] 20 51 60 221 226 233 271 308 354 415 488 548 553 556 557
## [16] 561 562 566 571 575 581 583 589 590 596 598 604 605 609 613
## [31] 614 615 618 619 621 623 628 629 633 634 642 645 646 647 649
## [46] 654 655 656 657 659 663 666 668 671 672 677 678 680 681 687
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## [91] 771 774 777 780 784 785 790 791 792 794 798 803 809 810 813
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##
## $historia_mejores
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
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##
## $lista_AG_BMDL
## $lista_AG_BMDL$mat_cp
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 3 1 583 846 943 1096 0 0 0 0 0 0 0
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## [6,] 5 1 581 843 894 943 1000 1096 0 0 0 0 0
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## [9,] 3 1 571 596 838 1096 0 0 0 0 0 0 0
## [10,] 3 1 561 598 843 1096 0 0 0 0 0 0 0
## [11,] 5 1 556 842 844 849 1063 1096 0 0 0 0 0
## [12,] 3 1 561 618 830 1096 0 0 0 0 0 0 0
## [13,] 5 1 566 621 840 852 1046 1096 0 0 0 0 0
## [14,] 2 1 488 843 1096 0 0 0 0 0 0 0 0
## [15,] 4 1 843 851 972 1016 1096 0 0 0 0 0 0
## [16,] 2 1 566 609 1096 0 0 0 0 0 0 0 0
## [17,] 17 1 598 659 672 706 792 826 856 859 931 961 963
## [18,] 9 1 556 557 598 706 826 839 848 853 856 1096 0
## [19,] 3 1 575 583 849 1096 0 0 0 0 0 0 0
## [20,] 4 1 556 575 853 944 1096 0 0 0 0 0 0
## [21,] 4 1 415 581 854 957 1096 0 0 0 0 0 0
## [22,] 6 1 556 716 719 842 879 893 1096 0 0 0 0
## [23,] 4 1 609 765 843 856 1096 0 0 0 0 0 0
## [24,] 5 1 488 562 791 828 852 1096 0 0 0 0 0
## [25,] 3 1 553 848 935 1096 0 0 0 0 0 0 0
## [26,] 4 1 553 841 856 941 1096 0 0 0 0 0 0
## [27,] 4 1 598 706 853 854 1096 0 0 0 0 0 0
## [28,] 7 1 561 831 854 930 935 964 968 1096 0 0 0
## [29,] 6 1 553 556 604 706 725 839 1096 0 0 0 0
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## [32,] 2 1 566 848 1096 0 0 0 0 0 0 0 0
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## [34,] 3 1 791 830 840 1096 0 0 0 0 0 0 0
## [35,] 6 1 571 623 841 860 870 1005 1096 0 0 0 0
## [36,] 3 1 415 561 852 1096 0 0 0 0 0 0 0
## [37,] 4 1 566 609 761 841 1096 0 0 0 0 0 0
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## [39,] 5 1 830 841 943 1001 1013 1096 0 0 0 0 0
## [40,] 4 1 553 561 848 878 1096 0 0 0 0 0 0
## [41,] 5 1 415 566 583 828 862 1096 0 0 0 0 0
## [42,] 17 1 51 598 619 649 655 846 852 891 895 904 918
## [43,] 5 1 553 566 581 843 863 1096 0 0 0 0 0
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## [50,] 3 1 561 850 945 1096 0 0 0 0 0 0 0
## [,14] [,15] [,16] [,17] [,18] [,19] [,20]
## [1,] 0 0 0 0 0 0 0
## [2,] 0 0 0 0 0 0 0
## [3,] 0 0 0 0 0 0 0
## [4,] 0 0 0 0 0 0 0
## [5,] 0 0 0 0 0 0 0
## [6,] 0 0 0 0 0 0 0
## [7,] 0 0 0 0 0 0 0
## [8,] 0 0 0 0 0 0 0
## [9,] 0 0 0 0 0 0 0
## [10,] 0 0 0 0 0 0 0
## [11,] 0 0 0 0 0 0 0
## [12,] 0 0 0 0 0 0 0
## [13,] 0 0 0 0 0 0 0
## [14,] 0 0 0 0 0 0 0
## [15,] 0 0 0 0 0 0 0
## [16,] 0 0 0 0 0 0 0
## [17,] 984 990 1001 1037 1067 1089 1096
## [18,] 0 0 0 0 0 0 0
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## [20,] 0 0 0 0 0 0 0
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## [24,] 0 0 0 0 0 0 0
## [25,] 0 0 0 0 0 0 0
## [26,] 0 0 0 0 0 0 0
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## [28,] 0 0 0 0 0 0 0
## [29,] 0 0 0 0 0 0 0
## [30,] 0 0 0 0 0 0 0
## [31,] 967 979 1022 1081 1087 1088 1096
## [32,] 0 0 0 0 0 0 0
## [33,] 0 0 0 0 0 0 0
## [34,] 0 0 0 0 0 0 0
## [35,] 0 0 0 0 0 0 0
## [36,] 0 0 0 0 0 0 0
## [37,] 0 0 0 0 0 0 0
## [38,] 0 0 0 0 0 0 0
## [39,] 0 0 0 0 0 0 0
## [40,] 0 0 0 0 0 0 0
## [41,] 0 0 0 0 0 0 0
## [42,] 979 1005 1019 1070 1080 1092 1096
## [43,] 0 0 0 0 0 0 0
## [44,] 0 0 0 0 0 0 0
## [45,] 0 0 0 0 0 0 0
## [46,] 0 0 0 0 0 0 0
## [47,] 0 0 0 0 0 0 0
## [48,] 0 0 0 0 0 0 0
## [49,] 0 0 0 0 0 0 0
## [50,] 0 0 0 0 0 0 0
##
## $lista_AG_BMDL$vec_BMDL_k_cp
## [1] 620.7221 682.3464 612.7140 734.1199 640.2629 654.7069 629.8685 686.4068
## [9] 670.7910 650.0290 638.1831 652.8310 606.0128 686.7464 687.1560 622.0669
## [17] 622.3746 687.9044 682.0507 675.1665 661.1471 624.4801 638.3243 684.0783
## [25] 622.4185 630.2067 642.9621 686.4479 672.6062 605.8640 645.4966 711.6074
## [33] 676.3595 648.2371 609.2041 642.1418 711.0928 629.4421 610.6716 626.1861
## [41] 643.3575 645.2567 732.3095 655.2430 636.4882 622.5239 644.3481 628.5716
## [49] 708.6703 667.1138
##
##
## $vec_min_BMDL
## [1] 855.8155 658.3966 606.1193 643.7164 614.9734 626.7782 627.0348 614.3733
## [9] 614.0191 612.9008 603.9442 613.3832 613.7734 589.0907 601.7690 593.6566
## [17] 599.4772 605.2639 592.9282 604.5720 613.1924 589.3444 605.5718 608.3683
## [25] 600.3402 600.0168 605.2639 596.9412 595.2160 603.0176 590.6518 597.1935
## [33] 598.9235 601.3915 613.5774 606.1035 606.0250 586.2545 602.8866 586.2545
## [41] 590.5735 592.8639 592.8639 607.4513 591.3284 603.0176 600.1979 608.0316
## [49] 599.4409 605.8640
##
## $valor_BMDL_minimo
## [1] "_BMDL_586"
##
## $cromosoma_minimo_BMDL
## [1] 5 1 557 566 609 854 1057 1096 0 0 0 0 0 0 0
## [16] 0 0 0 0 0
##
## $minimo_BMDL
## [1] 586.2545
##
## $param
## $param$nombre_datos
## [1] "DataCPSimRebases"
##
## $param$frecuencia_datos
## [1] 7
##
## $param$n_datos
## [1] "TODOS"
##
## $param$diarios0_rebases1
## [1] 0
##
## $param$valor_de_rebase
## [1] 63.16459
##
## $param$r
## [1] 50
##
## $param$k
## [1] 50
##
## $param$penalty
## [1] "BMDL"
##
## $param$max_num_cp
## [1] 20
##
## $param$prob_inicial
## [1] 0.06
##
## $param$prob_volado
## [1] 0.5
##
## $param$probs_muta
## [1] 0.3 0.4 0.3
##
## $param$mutaciones
## [1] -1 0 1
##
## $param$dist_extremos
## [1] 10
##
## $param$prob_para_sin_cp
## [1] 0.5
##
## $param$cp_real
## [1] "sin cp_real"
##
## $param$quita_ini0_fin1
## [1] 0
##
## $param$probs_rank0_MDL1
## [1] 0
##
## $param$nombre_carpeta_pdf
## [1] "Figures"
##
## $param$nombre_carpeta_RData
## [1] "Data"
##
## $param$cuantos_mejores_cp_graf
## [1] 100
##
## $param$my_data
## NULL
##
## $param$minimo_numero_de_cp
## [1] 1
##
## $param$probs_nuevos_muta0N
## [1] 0.8 0.1 0.1
##
## $param$rf_type
## [1] "W"
##
## $param$initial_val_optim
## [1] 0.1 0.5
##
## $param$mat_low_upp
## [,1] [,2]
## [1,] 1e-04 1e+01
## [2,] 1e-08 1e+05
##
## $param$vec_dist_a_priori
## [1] "Gamma" "Gamma"
##
## $param$mat_phi
## [,1] [,2]
## [1,] 1 2.0
## [2,] 3 1.2
##
## $param$ajuste_bloque
## [1] TRUE
##
## $param$print_progress_bar
## [1] TRUE
##
## $param$print_progress_plots
## [1] TRUE
##
## $param$value_set_seed
## [1] 123
citation("BayesianMDLGA")## To cite package 'BayesianMDLGA' in publications use:
##
## Taimal CA, Suárez-Sierra BM, Rivera JC (2023). "An Exploration of
## Genetic Algorithms Operators for the Detection of Multiple
## Change-Points of Exceedances Using Non-homogeneous Poisson Processes
## and Bayesian Methods." In _Colombian Conference on Computing_,
## 230-258. Springer. doi:10.1007/978-3-031-47372-2_20
## <https://doi.org/10.1007/978-3-031-47372-2_20>.
##
## A BibTeX entry for LaTeX users is
##
## @InProceedings{,
## title = {An Exploration of Genetic Algorithms Operators for the Detection of Multiple Change-Points of Exceedances Using Non-homogeneous Poisson Processes and Bayesian Methods},
## author = {Carlos A Taimal and Biviana Marcela Suárez-Sierra and Juan Carlos Rivera},
## booktitle = {Colombian Conference on Computing},
## year = {2023},
## pages = {230--258},
## organization = {Springer},
## doi = {10.1007/978-3-031-47372-2_20},
## }