Extract changepoints
changepoints.RdRetrieve the indices of the changepoints identified by an algorithm or model.
Usage
changepoints(x, ...)
# Default S3 method
changepoints(x, ...)
# S3 method for class 'mod_cpt'
changepoints(x, ...)
# S3 method for class 'seg_basket'
changepoints(x, ...)
# S3 method for class 'seg_cpt'
changepoints(x, ...)
# S3 method for class 'tidycpt'
changepoints(x, use_labels = FALSE, ...)
# S3 method for class 'ga'
changepoints(x, ...)
# S3 method for class 'cpt'
changepoints(x, ...)
# S3 method for class 'cptga'
changepoints(x, ...)
# S3 method for class 'segmented'
changepoints(x, ...)
# S3 method for class 'stepmented'
changepoints(x, ...)
# S3 method for class 'breakpointsfull'
changepoints(x, ...)
# S3 method for class 'wbs'
changepoints(x, ...)Value
a numeric vector of changepoint indices, or, if use_labels is
TRUE, a character of time labels.
Details
tidycpt objects, as well as their segmenter and model components,
implement changepoints() methods.
Note that this function is not to be confused with wbs::changepoints(),
which returns different information.
For the default method, changepoints() will attempt to return the
cpt_true attribute, which is set by test_set().
See also
Other tidycpt-generics:
as.model(),
as.segmenter(),
diagnose(),
fitness(),
model_name()
Examples
cpts <- segment(DataCPSim, method = "ga", maxiter = 5)
changepoints(cpts$segmenter)
#> x5 x7 x8 x9 x11 x22 x24 x33 x34 x37 x38 x39 x40
#> 5 7 8 9 11 22 24 33 34 37 38 39 40
#> x41 x42 x44 x45 x52 x53 x54 x55 x57 x59 x60 x61 x64
#> 41 42 44 45 52 53 54 55 57 59 60 61 64
#> x66 x67 x72 x73 x76 x77 x78 x79 x81 x83 x84 x85 x86
#> 66 67 72 73 76 77 78 79 81 83 84 85 86
#> x88 x92 x95 x96 x97 x98 x99 x100 x104 x106 x108 x109 x114
#> 88 92 95 96 97 98 99 100 104 106 108 109 114
#> x116 x117 x119 x122 x123 x124 x130 x132 x136 x137 x141 x142 x143
#> 116 117 119 122 123 124 130 132 136 137 141 142 143
#> x144 x145 x146 x148 x149 x152 x153 x154 x158 x163 x167 x168 x169
#> 144 145 146 148 149 152 153 154 158 163 167 168 169
#> x170 x174 x177 x181 x182 x184 x185 x186 x190 x191 x192 x193 x195
#> 170 174 177 181 182 184 185 186 190 191 192 193 195
#> x196 x197 x198 x200 x201 x202 x203 x205 x207 x208 x210 x213 x214
#> 196 197 198 200 201 202 203 205 207 208 210 213 214
#> x215 x217 x219 x223 x224 x225 x227 x231 x233 x234 x235 x236 x237
#> 215 217 219 223 224 225 227 231 233 234 235 236 237
#> x238 x240 x242 x245 x252 x254 x256 x259 x261 x262 x265 x269 x272
#> 238 240 242 245 252 254 256 259 261 262 265 269 272
#> x274 x275 x277 x278 x279 x285 x286 x287 x289 x290 x291 x294 x295
#> 274 275 277 278 279 285 286 287 289 290 291 294 295
#> x297 x302 x303 x305 x310 x311 x313 x317 x318 x321 x322 x323 x327
#> 297 302 303 305 310 311 313 317 318 321 322 323 327
#> x328 x329 x330 x332 x334 x337 x340 x341 x343 x344 x345 x346 x350
#> 328 329 330 332 334 337 340 341 343 344 345 346 350
#> x353 x358 x359 x362 x364 x365 x366 x368 x369 x371 x375 x376 x377
#> 353 358 359 362 364 365 366 368 369 371 375 376 377
#> x378 x384 x386 x389 x390 x392 x394 x395 x397 x398 x402 x403 x406
#> 378 384 386 389 390 392 394 395 397 398 402 403 406
#> x408 x410 x413 x416 x417 x418 x420 x421 x422 x423 x425 x429 x431
#> 408 410 413 416 417 418 420 421 422 423 425 429 431
#> x434 x436 x438 x440 x442 x443 x446 x453 x457 x461 x462 x463 x464
#> 434 436 438 440 442 443 446 453 457 461 462 463 464
#> x466 x468 x469 x472 x478 x479 x481 x482 x485 x486 x488 x494 x498
#> 466 468 469 472 478 479 481 482 485 486 488 494 498
#> x499 x500 x501 x502 x503 x506 x508 x509 x511 x512 x513 x514 x518
#> 499 500 501 502 503 506 508 509 511 512 513 514 518
#> x519 x523 x524 x526 x527 x532 x533 x534 x535 x536 x537 x539 x541
#> 519 523 524 526 527 532 533 534 535 536 537 539 541
#> x542 x543 x544 x545 x548 x552 x554 x559 x566 x570 x572 x573 x574
#> 542 543 544 545 548 552 554 559 566 570 572 573 574
#> x579 x580 x581 x582 x583 x590 x594 x595 x597 x598 x601 x603 x609
#> 579 580 581 582 583 590 594 595 597 598 601 603 609
#> x610 x611 x613 x614 x617 x618 x620 x621 x623 x626 x627 x631 x632
#> 610 611 613 614 617 618 620 621 623 626 627 631 632
#> x635 x636 x637 x638 x639 x640 x641 x642 x643 x644 x645 x647 x648
#> 635 636 637 638 639 640 641 642 643 644 645 647 648
#> x651 x654 x656 x658 x660 x661 x662 x664 x665 x666 x667 x669 x677
#> 651 654 656 658 660 661 662 664 665 666 667 669 677
#> x679 x681 x682 x683 x684 x685 x687 x690 x691 x697 x699 x704 x705
#> 679 681 682 683 684 685 687 690 691 697 699 704 705
#> x706 x707 x709 x710 x714 x721 x725 x726 x727 x730 x733 x739 x740
#> 706 707 709 710 714 721 725 726 727 730 733 739 740
#> x743 x747 x748 x749 x750 x754 x756 x760 x761 x765 x769 x773 x777
#> 743 747 748 749 750 754 756 760 761 765 769 773 777
#> x779 x780 x782 x784 x787 x788 x791 x793 x794 x796 x805 x806 x807
#> 779 780 782 784 787 788 791 793 794 796 805 806 807
#> x808 x809 x810 x811 x813 x817 x818 x822 x823 x828 x830 x831 x832
#> 808 809 810 811 813 817 818 822 823 828 830 831 832
#> x833 x835 x836 x837 x841 x844 x845 x848 x849 x850 x854 x859 x861
#> 833 835 836 837 841 844 845 848 849 850 854 859 861
#> x864 x865 x872 x873 x876 x877 x880 x881 x882 x884 x885 x888 x891
#> 864 865 872 873 876 877 880 881 882 884 885 888 891
#> x892 x893 x897 x898 x901 x904 x905 x907 x910 x911 x912 x913 x918
#> 892 893 897 898 901 904 905 907 910 911 912 913 918
#> x920 x922 x924 x925 x927 x929 x930 x931 x933 x934 x937 x938 x939
#> 920 922 924 925 927 929 930 931 933 934 937 938 939
#> x946 x951 x953 x954 x955 x956 x957 x958 x960 x961 x962 x965 x968
#> 946 951 953 954 955 956 957 958 960 961 962 965 968
#> x971 x972 x976 x977 x981 x985 x997 x1002 x1006 x1009 x1011 x1012 x1014
#> 971 972 976 977 981 985 997 1002 1006 1009 1011 1012 1014
#> x1015 x1016 x1017 x1018 x1019 x1024 x1027 x1028 x1029 x1030 x1031 x1032 x1036
#> 1015 1016 1017 1018 1019 1024 1027 1028 1029 1030 1031 1032 1036
#> x1037 x1040 x1045 x1048 x1049 x1052 x1053 x1056 x1058 x1059 x1063 x1066 x1067
#> 1037 1040 1045 1048 1049 1052 1053 1056 1058 1059 1063 1066 1067
#> x1068 x1071 x1072 x1073 x1074 x1078 x1079 x1080 x1081 x1087
#> 1068 1071 1072 1073 1074 1078 1079 1080 1081 1087
# \donttest{
# Segment a times series using a genetic algorithm
cpts <- segment(DataCPSim, method = "cptga")
changepoints(cpts$segmenter)
#> [1] 543 823 940 973
# }
cpts <- segment(DataCPSim, method = "selgmented")
#> No. of breakpoints: 2 .. 3 .. 4 .. 5 .. 6 .. 7 .. 8 .. 9 .. 10 ..
#>
#> BIC to detect no. of breakpoints:
#> 0 1 2 3 4 5 6 6
#> 10727.65 10225.17 10207.73 10221.65 10214.40 10225.80 10231.34 10204.84
#> 7 8 9
#> 10219.03 10231.46 10245.46
#>
#> No. of selected breakpoints: 4 (2 breakpoint(s) removed due to small slope diff)
changepoints(cpts$segmenter)
#> [1] 566 760 927 1064
# \donttest{
cpts <- segment(DataCPSim, method = "stelpmented")
#> No. of breakpoints: 2 .. 3 .. 4 .. 5 .. 6 .. 7 .. 8 .. 9 .. 10 ..
#>
#> BIC to detect no. of breakpoints:
#> 0 1 2 3 4 5 5 5
#> 8385.437 7430.881 7135.682 6976.159 6987.047 7000.547 7000.547 7000.547
#> 5 5 5
#> 7000.547 7000.547 7000.547
#>
#> No. of selected breakpoints: 3
changepoints(cpts$segmenter)
#> [1] 551 822 973
# }
cpts <- segment(DataCPSim, method = "strucchange")
changepoints(cpts$segmenter)
#> [1] 547 767 932
cpts <- segment(DataCPSim, method = "wbs")
changepoints(cpts$segmenter)
#> [1] 547 822 972 997 999 1031 1033 1040 1041 1063 1066