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Permutational analysis of variance

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of the result on assumption of normality, PERMANOVA draws tests for significance by comparing the actual F test result to that gained from random permutations of the objects between the groups. Moreover, whilst PERMANOVA tests for similarity based on a chosen distance measure, ANOVA tests for
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of the groups as defined by measure space are equivalent for all groups. A rejection of the null hypothesis means that either the centroid and/or the spread of the objects is different between the groups. Hence the test is based on the prior calculation of the distance between any two objects
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Finally, the PERMANOVA procedure draws significance for the actual F statistic by performing multiple permutations of the data. In each permutation
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PERMANOVA is widely used in the field of ecology and is implemented in several software packages including the PERMANOVA software,
913: 818: 798: 774:{\displaystyle P={\frac {{\text{number of permutations with }}F^{\pi }\geq F}{\text{total number of permutations}}}} 27: 794: 63: 30: 491: 46: 55: 656:{\displaystyle F={\frac {\left({\dfrac {SS_{A}}{p-1}}\right)}{\left({\dfrac {SS_{W}}{N-p}}\right)}}} 420: 485:) can be calculated as the difference between the overall and the within groups sum-of-squares: 240: 701: 407:{\displaystyle SS_{W}={\frac {1}{n}}\sum _{i=1}^{N-1}\sum _{j=i+1}^{N}d_{ij}^{2}\delta _{ij}} 923: 830: 458: 34: 790: 681: 211: 38: 834: 907: 455:
belong to the same group, and 0 otherwise. Then, the between groups sum-of-squares (
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to compare within-group to between-group variance. However, while ANOVA bases the
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the items are shuffled between groups, and the F-ratio is calculated for it,
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objects in each group, the total sum-of-squares is determined as:
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included in the experiment. PERMANOVA shares some resemblance to
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Similarly, the within groups sum-of-squares is determined as:
37:. PERMANOVA is used to compare groups of objects and test the 734: 704: 684: 616: 576: 562: 494: 461: 423: 292: 243: 214: 96: 773: 717: 690: 655: 542: 477: 439: 406: 264: 229: 197: 20:Permutational multivariate analysis of variance 868:"lmPerm: Permutation Tests for Linear Models" 553:Finally, a pseudo F-statistic is calculated: 8: 79:In the simple case of a single factor with 58:within and between groups, and make use of 753: 744: 741: 733: 709: 703: 683: 626: 615: 586: 575: 569: 561: 534: 518: 502: 493: 469: 460: 428: 422: 395: 385: 377: 367: 350: 334: 323: 309: 300: 291: 256: 248: 242: 213: 189: 181: 171: 154: 138: 127: 113: 104: 95: 272:is the squared distance between objects 866:Wheeler, Bob; Torchiano, Marco (2016). 810: 725:. The P-value is then calculated by: 7: 850:"Permutational Analysis of Variance" 543:{\displaystyle SS_{A}=SS_{T}-SS_{W}} 237:is the total number of objects, and 835:10.1111/j.1442-9993.2001.01070.pp.x 14: 746:number of permutations with  888:"skbio.stats.distance.permanova" 919:Statistical hypothesis testing 1: 799:Python (programming language) 767:total number of permutations 440:{\displaystyle \delta _{ij}} 75:Calculation of the statistic 54:where they both measure the 848:Anderson, Marti J. (2005). 940: 265:{\displaystyle d_{ij}^{2}} 670:is the number of groups. 447:is 1 if the observations 795:R (programming language) 718:{\displaystyle F^{\pi }} 67:similarity of the group 785:Implementation and use 775: 719: 692: 657: 544: 479: 478:{\displaystyle SS_{A}} 441: 408: 372: 345: 266: 231: 199: 176: 149: 776: 720: 693: 658: 545: 480: 442: 409: 346: 319: 267: 232: 200: 150: 123: 914:Analysis of variance 732: 702: 691:{\displaystyle \pi } 682: 674:Drawing significance 560: 492: 459: 421: 290: 241: 230:{\displaystyle N=pn} 212: 94: 390: 261: 194: 819:Anderson, Marti J. 797:Vegan, lmPerm and 771: 715: 688: 653: 645: 605: 540: 475: 437: 404: 373: 262: 244: 227: 195: 177: 769: 768: 747: 651: 644: 604: 317: 121: 931: 898: 897: 895: 894: 884: 878: 877: 875: 874: 863: 857: 856: 854: 845: 839: 838: 815: 801:skbio packages. 780: 778: 777: 772: 770: 766: 765: 758: 757: 748: 745: 742: 724: 722: 721: 716: 714: 713: 697: 695: 694: 689: 662: 660: 659: 654: 652: 650: 646: 643: 632: 631: 630: 617: 610: 606: 603: 592: 591: 590: 577: 570: 549: 547: 546: 541: 539: 538: 523: 522: 507: 506: 484: 482: 481: 476: 474: 473: 446: 444: 443: 438: 436: 435: 413: 411: 410: 405: 403: 402: 389: 384: 371: 366: 344: 333: 318: 310: 305: 304: 271: 269: 268: 263: 260: 255: 236: 234: 233: 228: 204: 202: 201: 196: 193: 188: 175: 170: 148: 137: 122: 114: 109: 108: 35:permutation test 16:Statistical test 939: 938: 934: 933: 932: 930: 929: 928: 904: 903: 902: 901: 892: 890: 886: 885: 881: 872: 870: 865: 864: 860: 852: 847: 846: 842: 823:Austral Ecology 817: 816: 812: 807: 787: 749: 743: 730: 729: 705: 700: 699: 680: 679: 676: 633: 622: 618: 611: 593: 582: 578: 571: 558: 557: 530: 514: 498: 490: 489: 465: 457: 456: 424: 419: 418: 391: 296: 288: 287: 239: 238: 210: 209: 100: 92: 91: 77: 39:null hypothesis 17: 12: 11: 5: 937: 935: 927: 926: 921: 916: 906: 905: 900: 899: 879: 858: 840: 809: 808: 806: 803: 786: 783: 782: 781: 764: 761: 756: 752: 740: 737: 712: 708: 687: 675: 672: 664: 663: 649: 642: 639: 636: 629: 625: 621: 614: 609: 602: 599: 596: 589: 585: 581: 574: 568: 565: 551: 550: 537: 533: 529: 526: 521: 517: 513: 510: 505: 501: 497: 472: 468: 464: 434: 431: 427: 415: 414: 401: 398: 394: 388: 383: 380: 376: 370: 365: 362: 359: 356: 353: 349: 343: 340: 337: 332: 329: 326: 322: 316: 313: 308: 303: 299: 295: 259: 254: 251: 247: 226: 223: 220: 217: 206: 205: 192: 187: 184: 180: 174: 169: 166: 163: 160: 157: 153: 147: 144: 141: 136: 133: 130: 126: 120: 117: 112: 107: 103: 99: 76: 73: 56:sum-of-squares 28:non-parametric 15: 13: 10: 9: 6: 4: 3: 2: 936: 925: 922: 920: 917: 915: 912: 911: 909: 889: 883: 880: 869: 862: 859: 851: 844: 841: 836: 832: 828: 824: 820: 814: 811: 804: 802: 800: 796: 792: 784: 762: 759: 754: 750: 738: 735: 728: 727: 726: 710: 706: 685: 673: 671: 669: 647: 640: 637: 634: 627: 623: 619: 612: 607: 600: 597: 594: 587: 583: 579: 572: 566: 563: 556: 555: 554: 535: 531: 527: 524: 519: 515: 511: 508: 503: 499: 495: 488: 487: 486: 470: 466: 462: 454: 450: 432: 429: 425: 399: 396: 392: 386: 381: 378: 374: 368: 363: 360: 357: 354: 351: 347: 341: 338: 335: 330: 327: 324: 320: 314: 311: 306: 301: 297: 293: 286: 285: 284: 281: 279: 275: 257: 252: 249: 245: 224: 221: 218: 215: 190: 185: 182: 178: 172: 167: 164: 161: 158: 155: 151: 145: 142: 139: 134: 131: 128: 124: 118: 115: 110: 105: 101: 97: 90: 89: 88: 86: 82: 74: 72: 70: 65: 61: 57: 53: 48: 44: 40: 36: 32: 29: 25: 21: 891:. Retrieved 882: 871:. Retrieved 861: 843: 829:(1): 32–46. 826: 822: 813: 788: 677: 667: 665: 552: 452: 448: 416: 282: 277: 273: 207: 84: 80: 78: 64:significance 33:statistical 31:multivariate 23: 19: 18: 83:groups and 908:Categories 893:2024-05-18 873:2019-02-08 805:References 47:dispersion 760:≥ 755:π 711:π 686:π 638:− 598:− 525:− 426:δ 393:δ 348:∑ 339:− 321:∑ 152:∑ 143:− 125:∑ 43:centroids 41:that the 24:PERMANOVA 69:averages 26:), is a 924:Ecology 791:PRIMER 666:where 417:where 208:where 60:F test 853:(PDF) 52:ANOVA 793:and 451:and 276:and 45:and 831:doi 910:: 827:26 825:. 280:. 71:. 896:. 876:. 855:. 837:. 833:: 763:F 751:F 739:= 736:P 707:F 668:p 648:) 641:p 635:N 628:W 624:S 620:S 613:( 608:) 601:1 595:p 588:A 584:S 580:S 573:( 567:= 564:F 536:W 532:S 528:S 520:T 516:S 512:S 509:= 504:A 500:S 496:S 471:A 467:S 463:S 453:j 449:i 433:j 430:i 400:j 397:i 387:2 382:j 379:i 375:d 369:N 364:1 361:+ 358:i 355:= 352:j 342:1 336:N 331:1 328:= 325:i 315:n 312:1 307:= 302:W 298:S 294:S 278:j 274:i 258:2 253:j 250:i 246:d 225:n 222:p 219:= 216:N 191:2 186:j 183:i 179:d 173:N 168:1 165:+ 162:i 159:= 156:j 146:1 140:N 135:1 132:= 129:i 119:N 116:1 111:= 106:T 102:S 98:S 85:n 81:p 22:(

Index

non-parametric
multivariate
permutation test
null hypothesis
centroids
dispersion
ANOVA
sum-of-squares
F test
significance
averages
PRIMER
R (programming language)
Python (programming language)
Anderson, Marti J.
doi
10.1111/j.1442-9993.2001.01070.pp.x
"Permutational Analysis of Variance"
"lmPerm: Permutation Tests for Linear Models"
"skbio.stats.distance.permanova"
Categories
Analysis of variance
Statistical hypothesis testing
Ecology

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