tests/testthat/_snaps/difNLR.md
In drabinova/difNLR: DIF and DDF Detection by Non-Linear Regression Models
difNLR - examples at help page
Code
(fit1 <- difNLR(Data, group, focal.name = 1, model = "3PLcg"))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 3PL model with fixed guessing for groups
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 82.0689 0.0000 ***
Item2 28.3232 0.0000 ***
Item3 0.6845 0.7102
Item4 3.3055 0.1915
Item5 1.1984 0.5492
Item6 0.1573 0.9244
Item7 8.3032 0.0157 *
Item8 2.8660 0.2386
Item9 0.4549 0.7966
Item10 1.3507 0.5090
Item11 1.2431 0.5371
Item12 1.0537 0.5905
Item13 4.4139 0.1100
Item14 1.4940 0.4738
Item15 1.3079 0.5200
Item16 0.1424 0.9313
Item17 3.1673 0.2052
Item18 2.0206 0.3641
Item19 6.2546 0.0438 *
Item20 3.4871 0.1749
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 5.9915 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Item7
Item19
Code
summary(fitted(fit1))
Output
Item1 Item2 Item3 Item4
Min. :0.1340 Min. :0.1040 Min. :0.2988 Min. :0.4093
1st Qu.:0.3568 1st Qu.:0.4272 1st Qu.:0.6607 1st Qu.:0.7422
Median :0.5093 Median :0.5844 Median :0.7504 Median :0.8122
Mean :0.5251 Mean :0.5702 Mean :0.7157 Mean :0.7820
3rd Qu.:0.7268 3rd Qu.:0.7315 3rd Qu.:0.8227 3rd Qu.:0.8666
Max. :0.9705 Max. :0.9679 Max. :0.9447 Max. :0.9566
Item5 Item6 Item7 Item8
Min. :0.4258 Min. :0.1755 Min. :0.1924 Min. :0.2732
1st Qu.:0.7634 1st Qu.:0.5495 1st Qu.:0.5468 1st Qu.:0.5343
Median :0.8560 Median :0.6819 Median :0.6648 Median :0.6410
Mean :0.8122 Mean :0.6386 Mean :0.6500 Mean :0.6166
3rd Qu.:0.9192 3rd Qu.:0.7920 3rd Qu.:0.7531 3rd Qu.:0.7416
Max. :0.9886 Max. :0.9564 Max. :0.9299 Max. :0.9293
Item9 Item10 Item11 Item12
Min. :0.2212 Min. :0.2253 Min. :0.1829 Min. :0.2533
1st Qu.:0.5057 1st Qu.:0.5002 1st Qu.:0.5979 1st Qu.:0.4533
Median :0.6025 Median :0.5979 Median :0.7208 Median :0.5597
Mean :0.5786 Mean :0.5750 Mean :0.6741 Mean :0.5474
3rd Qu.:0.6932 3rd Qu.:0.6904 3rd Qu.:0.8179 3rd Qu.:0.6731
Max. :0.8841 Max. :0.8860 Max. :0.9596 Max. :0.9125
Item13 Item14 Item15 Item16
Min. :0.4102 Min. :0.1199 Min. :0.1741 Min. :0.1039
1st Qu.:0.6842 1st Qu.:0.3349 1st Qu.:0.4474 1st Qu.:0.3140
Median :0.7715 Median :0.4531 Median :0.5432 Median :0.4549
Mean :0.7421 Mean :0.4432 Mean :0.5224 Mean :0.4461
3rd Qu.:0.8443 3rd Qu.:0.5834 3rd Qu.:0.6359 3rd Qu.:0.6140
Max. :0.9610 Max. :0.8798 Max. :0.8470 Max. :0.9246
Item17 Item18 Item19 Item20
Min. :0.1824 Min. :0.06246 Min. :0.09561 Min. :0.2103
1st Qu.:0.3717 1st Qu.:0.27960 1st Qu.:0.33303 1st Qu.:0.2986
Median :0.4599 Median :0.41775 Median :0.48166 Median :0.3893
Mean :0.4519 Mean :0.40978 Mean :0.48171 Mean :0.4117
3rd Qu.:0.5565 3rd Qu.:0.57444 3rd Qu.:0.63424 3rd Qu.:0.5271
Max. :0.8139 Max. :0.90358 Max. :0.93207 Max. :0.9096
Code
fitted(fit1, item = 1)
Output
1 2 3 4 5 6 7 8
0.4364813 0.7866563 0.8870283 0.2254449 0.7950139 0.2979029 0.5093386 0.4258949
9 10 11 12 13 14 15 16
0.7950139 0.7866563 0.8463985 0.7267631 0.4364813 0.5025621 0.7326843 0.3567994
17 18 19 20 21 22 23 24
0.5825445 0.7267631 0.4364813 0.3120550 0.4258949 0.8368960 0.8870283 0.8463985
25 26 27 28 29 30 31 32
0.6607947 0.5025621 0.7866563 0.5025621 0.7267631 0.2639710 0.5025621 0.6585473
33 34 35 36 37 38 39 40
0.7950139 0.3120550 0.4364813 0.5093386 0.6607947 0.8463985 0.5025621 0.5847767
41 42 43 44 45 46 47 48
0.2639710 0.5025621 0.5025621 0.7950139 0.7950139 0.4364813 0.3120550 0.5025621
49 50 51 52 53 54 55 56
0.2254449 0.5093386 0.5093386 0.2639710 0.1726988 0.5093386 0.3120550 0.5825445
57 58 59 60 61 62 63 64
0.3120550 0.5093386 0.5093386 0.5093386 0.9091805 0.6607947 0.3120550 0.2254449
65 66 67 68 69 70 71 72
0.6585473 0.7326843 0.2254449 0.7326843 0.1431489 0.9412824 0.4258949 0.2254449
73 74 75 76 77 78 79 80
0.4364813 0.5825445 0.5025621 0.2254449 0.6585473 0.2500383 0.3120550 0.5025621
81 82 83 84 85 86 87 88
0.5093386 0.5825445 0.2254449 0.3567994 0.5025621 0.6585473 0.2979029 0.3567994
89 90 91 92 93 94 95 96
0.4364813 0.7326843 0.2979029 0.7267631 0.4364813 0.7267631 0.2639710 0.2254449
97 98 99 100 101 102 103 104
0.3698991 0.8463985 0.2639710 0.8774494 0.9582384 0.5093386 0.5093386 0.6607947
105 106 107 108 109 110 111 112
0.3698991 0.2126244 0.7950139 0.7950139 0.6607947 0.1726988 0.6585473 0.4364813
113 114 115 116 117 118 119 120
0.3698991 0.8463985 0.3120550 0.5825445 0.3120550 0.8870283 0.2254449 0.4364813
121 122 123 124 125 126 127 128
0.7866563 0.4364813 0.2639710 0.8463985 0.2639710 0.5025621 0.2639710 0.2500383
129 130 131 132 133 134 135 136
0.2639710 0.2979029 0.4364813 0.4258949 0.4258949 0.8463985 0.3698991 0.3698991
137 138 139 140 141 142 143 144
0.1842596 0.5847767 0.7326843 0.8463985 0.3698991 0.1954771 0.2979029 0.7950139
145 146 147 148 149 150 151 152
0.5025621 0.6607947 0.9181070 0.7326843 0.2254449 0.3120550 0.2639710 0.2254449
153 154 155 156 157 158 159 160
0.6607947 0.4364813 0.8463985 0.3567994 0.2254449 0.9181070 0.9412824 0.3698991
161 162 163 164 165 166 167 168
0.4258949 0.5093386 0.9181070 0.1726988 0.4258949 0.4364813 0.6607947 0.9181070
169 170 171 172 173 174 175 176
0.2639710 0.7950139 0.2979029 0.6607947 0.5847767 0.5825445 0.7326843 0.4258949
177 178 179 180 181 182 183 184
0.4364813 0.7866563 0.8870283 0.1954771 0.3698991 0.8870283 0.9181070 0.8368960
185 186 187 188 189 190 191 192
0.5847767 0.5025621 0.8870283 0.3567994 0.1556872 0.7326843 0.3120550 0.5025621
193 194 195 196 197 198 199 200
0.7326843 0.1726988 0.9181070 0.5825445 0.5825445 0.6607947 0.7950139 0.4364813
201 202 203 204 205 206 207 208
0.3120550 0.5825445 0.2500383 0.7950139 0.8870283 0.7326843 0.7326843 0.4364813
209 210 211 212 213 214 215 216
0.7950139 0.3698991 0.5847767 0.3698991 0.2500383 0.5093386 0.1954771 0.2979029
217 218 219 220 221 222 223 224
0.7326843 0.7950139 0.9412824 0.7326843 0.5847767 0.3698991 0.3698991 0.7326843
225 226 227 228 229 230 231 232
0.3698991 0.4258949 0.7326843 0.5025621 0.5825445 0.5093386 0.3567994 0.9181070
233 234 235 236 237 238 239 240
0.8870283 0.3567994 0.8463985 0.7326843 0.5825445 0.9412824 0.3120550 0.9181070
241 242 243 244 245 246 247 248
0.7326843 0.6585473 0.6607947 0.4364813 0.5025621 0.3120550 0.4364813 0.5825445
249 250 251 252 253 254 255 256
0.5847767 0.3120550 0.8463985 0.5025621 0.7950139 0.6607947 0.5847767 0.5847767
257 258 259 260 261 262 263 264
0.6607947 0.4364813 0.7267631 0.7950139 0.1431489 0.6585473 0.3567994 0.2639710
265 266 267 268 269 270 271 272
0.6607947 0.5825445 0.8463985 0.4364813 0.7866563 0.1726988 0.4364813 0.6607947
273 274 275 276 277 278 279 280
0.7950139 0.3567994 0.9181070 0.3120550 0.5093386 0.1954771 0.3567994 0.5093386
281 282 283 284 285 286 287 288
0.2639710 0.7326843 0.5825445 0.4258949 0.5847767 0.7866563 0.5025621 0.7950139
289 290 291 292 293 294 295 296
0.9412824 0.4258949 0.4258949 0.2639710 0.3120550 0.5825445 0.4364813 0.8463985
297 298 299 300 301 302 303 304
0.5847767 0.5025621 0.5847767 0.8463985 0.5847767 0.3698991 0.3567994 0.2500383
305 306 307 308 309 310 311 312
0.6607947 0.9181070 0.7950139 0.9181070 0.4258949 0.3698991 0.6607947 0.2639710
313 314 315 316 317 318 319 320
0.4364813 0.5093386 0.8774494 0.7950139 0.5093386 0.6585473 0.5825445 0.3120550
321 322 323 324 325 326 327 328
0.2639710 0.5093386 0.7326843 0.1339969 0.6585473 0.7326843 0.3567994 0.7950139
329 330 331 332 333 334 335 336
0.7326843 0.1726988 0.3120550 0.3698991 0.4364813 0.2639710 0.8870283 0.5025621
337 338 339 340 341 342 343 344
0.5825445 0.4364813 0.6607947 0.3698991 0.6607947 0.5847767 0.7950139 0.7326843
345 346 347 348 349 350 351 352
0.5025621 0.5025621 0.4364813 0.4258949 0.8463985 0.2639710 0.1954771 0.8463985
353 354 355 356 357 358 359 360
0.5825445 0.6607947 0.2979029 0.8463985 0.5093386 0.2639710 0.2979029 0.4258949
361 362 363 364 365 366 367 368
0.7866563 0.2639710 0.5093386 0.2979029 0.8870283 0.4364813 0.3567994 0.3120550
369 370 371 372 373 374 375 376
0.2639710 0.5847767 0.3567994 0.5093386 0.2254449 0.4258949 0.5825445 0.3120550
377 378 379 380 381 382 383 384
0.1726988 0.7326843 0.5025621 0.7950139 0.8463985 0.5093386 0.4258949 0.5825445
385 386 387 388 389 390 391 392
0.6607947 0.9412824 0.5025621 0.9704721 0.3567994 0.1556872 0.3120550 0.3698991
393 394 395 396 397 398 399 400
0.7267631 0.5093386 0.5025621 0.3698991 0.4258949 0.8368960 0.8368960 0.8463985
401 402 403 404 405 406 407 408
0.2500383 0.3120550 0.4364813 0.8463985 0.9181070 0.3698991 0.5093386 0.5847767
409 410 411 412 413 414 415 416
0.4364813 0.8368960 0.6585473 0.4364813 0.7950139 0.4258949 0.5825445 0.1954771
417 418 419 420 421 422 423 424
0.5093386 0.2979029 0.4258949 0.1556872 0.3120550 0.6585473 0.4364813 0.4364813
425 426 427 428 429 430 431 432
0.5093386 0.3567994 0.6585473 0.6585473 0.5093386 0.5025621 0.3698991 0.4364813
433 434 435 436 437 438 439 440
0.5025621 0.8463985 0.3120550 0.7866563 0.7950139 0.2639710 0.2500383 0.5093386
441 442 443 444 445 446 447 448
0.5847767 0.5093386 0.7326843 0.2254449 0.3120550 0.7950139 0.6585473 0.6585473
449 450 451 452 453 454 455 456
0.1954771 0.3120550 0.5847767 0.1726988 0.8368960 0.5093386 0.5025621 0.7267631
457 458 459 460 461 462 463 464
0.5093386 0.5847767 0.3120550 0.7326843 0.5093386 0.5847767 0.7267631 0.7950139
465 466 467 468 469 470 471 472
0.2126244 0.2639710 0.5093386 0.7326843 0.5847767 0.6585473 0.5825445 0.6607947
473 474 475 476 477 478 479 480
0.3120550 0.5025621 0.7950139 0.7326843 0.3567994 0.5093386 0.5093386 0.7326843
481 482 483 484 485 486 487 488
0.8463985 0.5847767 0.7950139 0.7326843 0.5025621 0.3120550 0.7950139 0.6607947
489 490 491 492 493 494 495 496
0.4364813 0.6585473 0.7326843 0.2254449 0.2254449 0.3120550 0.5025621 0.1954771
497 498 499 500 501 502 503 504
0.2979029 0.5847767 0.5025621 0.7866563 0.8463985 0.2500383 0.3698991 0.3120550
505 506 507 508 509 510 511 512
0.5025621 0.4258949 0.3120550 0.4364813 0.3120550 0.3120550 0.3120550 0.1842596
513 514 515 516 517 518 519 520
0.2126244 0.6607947 0.7267631 0.2979029 0.3698991 0.4258949 0.2639710 0.5093386
521 522 523 524 525 526 527 528
0.5825445 0.5093386 0.9091805 0.7866563 0.5093386 0.6585473 0.8870283 0.5847767
529 530 531 532 533 534 535 536
0.2639710 0.8463985 0.8463985 0.5847767 0.3698991 0.4258949 0.3120550 0.6607947
537 538 539 540 541 542 543 544
0.6607947 0.7326843 0.5025621 0.5093386 0.2639710 0.3698991 0.6585473 0.3567994
545 546 547 548 549 550 551 552
0.5025621 0.7326843 0.5093386 0.4364813 0.3698991 0.2254449 0.4364813 0.2254449
553 554 555 556 557 558 559 560
0.7326843 0.4364813 0.3567994 0.4364813 0.6585473 0.2639710 0.6607947 0.5093386
561 562 563 564 565 566 567 568
0.8463985 0.2500383 0.5025621 0.7950139 0.4364813 0.5847767 0.2639710 0.7326843
569 570 571 572 573 574 575 576
0.9181070 0.4258949 0.5025621 0.7267631 0.3698991 0.4364813 0.5025621 0.5093386
577 578 579 580 581 582 583 584
0.3698991 0.7326843 0.1726988 0.3698991 0.4258949 0.1954771 0.5025621 0.5025621
585 586 587 588 589 590 591 592
0.5093386 0.2639710 0.4364813 0.1556872 0.7950139 0.4364813 0.8870283 0.2126244
593 594 595 596 597 598 599 600
0.3567994 0.5825445 0.5025621 0.5025621 0.4364813 0.2254449 0.5847767 0.6607947
601 602 603 604 605 606 607 608
0.2639710 0.2500383 0.3698991 0.2639710 0.3120550 0.7326843 0.8463985 0.7326843
609 610 611 612 613 614 615 616
0.7866563 0.5847767 0.6607947 0.5847767 0.5825445 0.7866563 0.4364813 0.1556872
617 618 619 620 621 622 623 624
0.5025621 0.1556872 0.7326843 0.2979029 0.4364813 0.1954771 0.4364813 0.4364813
625 626 627 628 629 630 631 632
0.3698991 0.1954771 0.7950139 0.4258949 0.8870283 0.7326843 0.2254449 0.6607947
633 634 635 636 637 638 639 640
0.5825445 0.7950139 0.7326843 0.8463985 0.6607947 0.7326843 0.5825445 0.8870283
641 642 643 644 645 646 647 648
0.4258949 0.5825445 0.5825445 0.5847767 0.2254449 0.3567994 0.3120550 0.3567994
649 650 651 652 653 654 655 656
0.4258949 0.5025621 0.8774494 0.4258949 0.7326843 0.7326843 0.6607947 0.4364813
657 658 659 660 661 662 663 664
0.7950139 0.7326843 0.1954771 0.7326843 0.3120550 0.7950139 0.2639710 0.3120550
665 666 667 668 669 670 671 672
0.3120550 0.9704721 0.5825445 0.6607947 0.2979029 0.2254449 0.3698991 0.5847767
673 674 675 676 677 678 679 680
0.4364813 0.3120550 0.7326843 0.5847767 0.1954771 0.4364813 0.3120550 0.4364813
681 682 683 684 685 686 687 688
0.1726988 0.4258949 0.3120550 0.1556872 0.6607947 0.8870283 0.4364813 0.8368960
689 690 691 692 693 694 695 696
0.3120550 0.3698991 0.8463985 0.5093386 0.5025621 0.5093386 0.2500383 0.7950139
697 698 699 700 701 702 703 704
0.7950139 0.5025621 0.2639710 0.7950139 0.2500383 0.2639710 0.9181070 0.2639710
705 706 707 708 709 710 711 712
0.3698991 0.2979029 0.7950139 0.4258949 0.4258949 0.3120550 0.1556872 0.8870283
713 714 715 716 717 718 719 720
0.7950139 0.5825445 0.5093386 0.5093386 0.5825445 0.1954771 0.3120550 0.5025621
721 722 723 724 725 726 727 728
0.5093386 0.4258949 0.5025621 0.5825445 0.8463985 0.7267631 0.1954771 0.3698991
729 730 731 732 733 734 735 736
0.5093386 0.6607947 0.3698991 0.7326843 0.5825445 0.3698991 0.6585473 0.3698991
737 738 739 740 741 742 743 744
0.7326843 0.2254449 0.3567994 0.8368960 0.5093386 0.5847767 0.8368960 0.7326843
745 746 747 748 749 750 751 752
0.3120550 0.5093386 0.2500383 0.8870283 0.7267631 0.2979029 0.7267631 0.5847767
753 754 755 756 757 758 759 760
0.5847767 0.6607947 0.5025621 0.7326843 0.4258949 0.3698991 0.6607947 0.9412824
761 762 763 764 765 766 767 768
0.2254449 0.3698991 0.6585473 0.1726988 0.2639710 0.3120550 0.5025621 0.5825445
769 770 771 772 773 774 775 776
0.5025621 0.3567994 0.3567994 0.8870283 0.7326843 0.5093386 0.3120550 0.3567994
777 778 779 780 781 782 783 784
0.6585473 0.7326843 0.3698991 0.8463985 0.6607947 0.3567994 0.7950139 0.3698991
785 786 787 788 789 790 791 792
0.4258949 0.3120550 0.2639710 0.5025621 0.5025621 0.5847767 0.2979029 0.8463985
793 794 795 796 797 798 799 800
0.5025621 0.7326843 0.1954771 0.5025621 0.4364813 0.5825445 0.3567994 0.2639710
801 802 803 804 805 806 807 808
0.5825445 0.5825445 0.7950139 0.8368960 0.7267631 0.7326843 0.3120550 0.4258949
809 810 811 812 813 814 815 816
0.2639710 0.8368960 0.8463985 0.5093386 0.7267631 0.7950139 0.2639710 0.4258949
817 818 819 820 821 822 823 824
0.2254449 0.5093386 0.4364813 0.1954771 0.7950139 0.7326843 0.7950139 0.8870283
825 826 827 828 829 830 831 832
0.8870283 0.3698991 0.4258949 0.1954771 0.5025621 0.4364813 0.2254449 0.2500383
833 834 835 836 837 838 839 840
0.5093386 0.5093386 0.3120550 0.2254449 0.4364813 0.3567994 0.8463985 0.5847767
841 842 843 844 845 846 847 848
0.6607947 0.4258949 0.7326843 0.2979029 0.5847767 0.3698991 0.2639710 0.2639710
849 850 851 852 853 854 855 856
0.7326843 0.1726988 0.5025621 0.4258949 0.5825445 0.2500383 0.5825445 0.3698991
857 858 859 860 861 862 863 864
0.5093386 0.5825445 0.5093386 0.7326843 0.5093386 0.7326843 0.6585473 0.7866563
865 866 867 868 869 870 871 872
0.4364813 0.5847767 0.8368960 0.5093386 0.5825445 0.2639710 0.5825445 0.8870283
873 874 875 876 877 878 879 880
0.6607947 0.3698991 0.9181070 0.7326843 0.5825445 0.3567994 0.4364813 0.6585473
881 882 883 884 885 886 887 888
0.3698991 0.1954771 0.5025621 0.8870283 0.5825445 0.4364813 0.6607947 0.5825445
889 890 891 892 893 894 895 896
0.3698991 0.7950139 0.4258949 0.1954771 0.4258949 0.6585473 0.5847767 0.4364813
897 898 899 900 901 902 903 904
0.3120550 0.8368960 0.6585473 0.6607947 0.5825445 0.3120550 0.7866563 0.3120550
905 906 907 908 909 910 911 912
0.7326843 0.2639710 0.7326843 0.5847767 0.3120550 0.7950139 0.6607947 0.3698991
913 914 915 916 917 918 919 920
0.5847767 0.5825445 0.3698991 0.7950139 0.3698991 0.4364813 0.5847767 0.8870283
921 922 923 924 925 926 927 928
0.5847767 0.3567994 0.4258949 0.5025621 0.6607947 0.1954771 0.5825445 0.5847767
929 930 931 932 933 934 935 936
0.5025621 0.5847767 0.7326843 0.7866563 0.5825445 0.4258949 0.5825445 0.3698991
937 938 939 940 941 942 943 944
0.5847767 0.7267631 0.1954771 0.7326843 0.5093386 0.4258949 0.3698991 0.8870283
945 946 947 948 949 950 951 952
0.3120550 0.5847767 0.1954771 0.2500383 0.5825445 0.5025621 0.5847767 0.5093386
953 954 955 956 957 958 959 960
0.3698991 0.8870283 0.6607947 0.1954771 0.5025621 0.5093386 0.2254449 0.8463985
961 962 963 964 965 966 967 968
0.7866563 0.2639710 0.1954771 0.6607947 0.2254449 0.2254449 0.4364813 0.6585473
969 970 971 972 973 974 975 976
0.4364813 0.2639710 0.3698991 0.1726988 0.5825445 0.7267631 0.3698991 0.6607947
977 978 979 980 981 982 983 984
0.7267631 0.7267631 0.6607947 0.7326843 0.6585473 0.9181070 0.5825445 0.7950139
985 986 987 988 989 990 991 992
0.2254449 0.6607947 0.5825445 0.2639710 0.5093386 0.5847767 0.1339969 0.5847767
993 994 995 996 997 998 999 1000
0.5093386 0.3698991 0.7326843 0.3567994 0.9181070 0.3698991 0.8870283 0.6607947
1001 1002 1003 1004 1005 1006 1007 1008
0.5825445 0.3567994 0.2254449 0.4258949 0.7326843 0.2979029 0.9582384 0.6607947
1009 1010 1011 1012 1013 1014 1015 1016
0.5847767 0.5025621 0.2254449 0.6607947 0.1632496 0.4364813 0.5825445 0.2639710
1017 1018 1019 1020 1021 1022 1023 1024
0.1726988 0.7326843 0.3567994 0.5825445 0.7326843 0.4258949 0.9181070 0.4258949
1025 1026 1027 1028 1029 1030 1031 1032
0.6585473 0.4364813 0.6607947 0.2254449 0.3120550 0.6607947 0.4258949 0.5847767
1033 1034 1035 1036 1037 1038 1039 1040
0.5093386 0.1954771 0.7267631 0.5825445 0.1556872 0.4258949 0.7326843 0.1842596
1041 1042 1043 1044 1045 1046 1047 1048
0.6607947 0.3698991 0.4258949 0.6585473 0.6585473 0.6607947 0.3120550 0.3567994
1049 1050 1051 1052 1053 1054 1055 1056
0.3698991 0.7950139 0.2639710 0.3120550 0.6607947 0.4258949 0.5093386 0.4364813
1057 1058 1059 1060 1061 1062 1063 1064
0.2500383 0.4364813 0.3698991 0.3698991 0.2979029 0.3567994 0.5847767 0.5025621
1065 1066 1067 1068 1069 1070 1071 1072
0.7326843 0.7326843 0.7950139 0.8463985 0.3567994 0.6607947 0.4364813 0.4258949
1073 1074 1075 1076 1077 1078 1079 1080
0.4364813 0.1556872 0.7950139 0.7950139 0.1431489 0.2254449 0.5025621 0.2639710
1081 1082 1083 1084 1085 1086 1087 1088
0.4258949 0.2254449 0.9412824 0.1726988 0.5025621 0.2639710 0.2500383 0.5025621
1089 1090 1091 1092 1093 1094 1095 1096
0.2126244 0.4258949 0.5847767 0.7950139 0.8870283 0.7950139 0.6607947 0.5093386
1097 1098 1099 1100 1101 1102 1103 1104
0.5847767 0.1954771 0.3120550 0.3120550 0.9412824 0.2500383 0.7326843 0.2639710
1105 1106 1107 1108 1109 1110 1111 1112
0.7267631 0.5847767 0.3120550 0.4258949 0.5093386 0.4258949 0.5847767 0.4364813
1113 1114 1115 1116 1117 1118 1119 1120
0.3698991 0.2500383 0.7326843 0.6585473 0.5025621 0.6607947 0.5093386 0.7950139
1121 1122 1123 1124 1125 1126 1127 1128
0.8870283 0.7326843 0.5025621 0.6607947 0.5825445 0.5825445 0.3567994 0.3698991
1129 1130 1131 1132 1133 1134 1135 1136
0.4258949 0.6607947 0.1726988 0.3698991 0.8368960 0.4258949 0.8870283 0.4258949
1137 1138 1139 1140 1141 1142 1143 1144
0.2979029 0.5025621 0.2500383 0.5825445 0.3698991 0.1726988 0.3698991 0.3698991
1145 1146 1147 1148 1149 1150 1151 1152
0.5847767 0.5847767 0.7950139 0.5093386 0.3698991 0.4364813 0.6607947 0.2500383
1153 1154 1155 1156 1157 1158 1159 1160
0.2500383 0.2254449 0.7950139 0.9181070 0.8870283 0.4364813 0.9181070 0.7326843
1161 1162 1163 1164 1165 1166 1167 1168
0.2126244 0.2254449 0.5025621 0.7267631 0.7326843 0.2126244 0.5025621 0.2254449
1169 1170 1171 1172 1173 1174 1175 1176
0.5025621 0.7326843 0.4258949 0.4258949 0.2979029 0.9091805 0.9412824 0.7267631
1177 1178 1179 1180 1181 1182 1183 1184
0.7950139 0.5093386 0.1556872 0.5825445 0.7950139 0.5025621 0.5025621 0.6607947
1185 1186 1187 1188 1189 1190 1191 1192
0.4364813 0.5825445 0.3120550 0.5847767 0.5093386 0.4258949 0.6607947 0.8774494
1193 1194 1195 1196 1197 1198 1199 1200
0.7866563 0.5093386 0.7950139 0.3120550 0.7950139 0.4364813 0.2500383 0.7866563
1201 1202 1203 1204 1205 1206 1207 1208
0.6585473 0.5847767 0.3567994 0.6607947 0.4258949 0.8870283 0.9181070 0.2639710
1209 1210 1211 1212 1213 1214 1215 1216
0.9181070 0.3120550 0.3120550 0.6607947 0.3698991 0.9412824 0.3698991 0.4364813
1217 1218 1219 1220 1221 1222 1223 1224
0.6585473 0.6585473 0.8368960 0.1726988 0.5093386 0.7950139 0.4364813 0.5825445
1225 1226 1227 1228 1229 1230 1231 1232
0.3120550 0.7950139 0.3698991 0.7267631 0.7326843 0.4364813 0.5093386 0.7950139
1233 1234 1235 1236 1237 1238 1239 1240
0.5093386 0.7326843 0.9181070 0.3567994 0.7950139 0.4258949 0.7326843 0.3120550
1241 1242 1243 1244 1245 1246 1247 1248
0.5825445 0.6607947 0.5825445 0.7326843 0.7267631 0.5025621 0.7267631 0.5825445
1249 1250 1251 1252 1253 1254 1255 1256
0.9412824 0.3120550 0.3120550 0.3120550 0.4364813 0.5093386 0.3698991 0.2254449
1257 1258 1259 1260 1261 1262 1263 1264
0.5093386 0.7326843 0.5025621 0.5825445 0.5093386 0.1954771 0.3120550 0.6607947
1265 1266 1267 1268 1269 1270 1271 1272
0.5825445 0.1726988 0.5025621 0.6607947 0.5025621 0.2979029 0.2979029 0.5093386
1273 1274 1275 1276 1277 1278 1279 1280
0.7267631 0.3567994 0.7866563 0.7326843 0.3120550 0.8463985 0.4364813 0.6585473
1281 1282 1283 1284 1285 1286 1287 1288
0.3120550 0.9181070 0.1726988 0.5847767 0.5847767 0.2500383 0.5025621 0.9412824
1289 1290 1291 1292 1293 1294 1295 1296
0.1726988 0.5093386 0.8463985 0.1726988 0.5847767 0.6585473 0.6607947 0.4364813
1297 1298 1299 1300 1301 1302 1303 1304
0.6585473 0.8463985 0.3698991 0.5847767 0.7950139 0.1726988 0.7950139 0.3120550
1305 1306 1307 1308 1309 1310 1311 1312
0.7866563 0.6607947 0.2639710 0.5825445 0.5025621 0.8463985 0.8463985 0.4258949
1313 1314 1315 1316 1317 1318 1319 1320
0.3698991 0.2254449 0.5093386 0.7326843 0.7950139 0.8870283 0.7267631 0.6607947
1321 1322 1323 1324 1325 1326 1327 1328
0.5847767 0.3698991 0.5025621 0.7950139 0.6585473 0.2639710 0.3120550 0.5093386
1329 1330 1331 1332 1333 1334 1335 1336
0.8463985 0.2979029 0.6585473 0.5025621 0.7326843 0.3698991 0.5025621 0.7950139
1337 1338 1339 1340 1341 1342 1343 1344
0.2639710 0.5825445 0.7950139 0.2639710 0.3120550 0.9181070 0.5825445 0.2979029
1345 1346 1347 1348 1349 1350 1351 1352
0.5825445 0.4364813 0.5025621 0.2254449 0.3120550 0.5093386 0.3698991 0.5025621
1353 1354 1355 1356 1357 1358 1359 1360
0.8870283 0.7866563 0.3567994 0.5847767 0.5093386 0.5825445 0.7866563 0.8463985
1361 1362 1363 1364 1365 1366 1367 1368
0.2639710 0.6607947 0.5825445 0.2254449 0.5093386 0.6607947 0.9181070 0.3698991
1369 1370 1371 1372 1373 1374 1375 1376
0.6607947 0.9181070 0.6585473 0.6607947 0.9091805 0.3120550 0.4364813 0.5093386
1377 1378 1379 1380 1381 1382 1383 1384
0.7950139 0.4364813 0.5825445 0.4258949 0.2254449 0.5025621 0.5825445 0.1954771
1385 1386 1387 1388 1389 1390 1391 1392
0.7950139 0.3120550 0.3698991 0.1954771 0.1556872 0.7950139 0.5093386 0.5093386
1393 1394 1395 1396 1397 1398 1399 1400
0.7326843 0.7866563 0.7866563 0.7326843 0.4258949 0.5847767 0.4364813 0.2126244
1401 1402 1403 1404 1405 1406 1407 1408
0.7950139 0.9704721 0.4258949 0.7950139 0.3698991 0.3698991 0.6607947 0.5093386
1409 1410 1411 1412 1413 1414 1415 1416
0.8774494 0.7950139 0.2500383 0.5025621 0.2639710 0.3567994 0.4258949 0.9181070
1417 1418 1419 1420 1421 1422 1423 1424
0.6585473 0.7326843 0.2126244 0.5847767 0.3120550 0.5093386 0.4364813 0.8463985
1425 1426 1427 1428 1429 1430 1431 1432
0.6607947 0.7326843 0.6607947 0.2979029 0.4258949 0.1954771 0.7267631 0.3120550
1433 1434 1435 1436 1437 1438 1439 1440
0.5825445 0.5025621 0.4258949 0.1954771 0.7326843 0.9582384 0.7326843 0.5025621
1441 1442 1443 1444 1445 1446 1447 1448
0.1954771 0.7326843 0.5847767 0.5825445 0.8463985 0.2639710 0.7866563 0.3567994
1449 1450 1451 1452 1453 1454 1455 1456
0.6607947 0.7267631 0.8870283 0.4364813 0.3698991 0.4258949 0.5847767 0.6585473
1457 1458 1459 1460 1461 1462 1463 1464
0.7326843 0.3120550 0.5093386 0.2500383 0.5025621 0.5847767 0.3120550 0.2639710
1465 1466 1467 1468 1469 1470 1471 1472
0.7950139 0.5093386 0.5093386 0.5093386 0.6607947 0.1954771 0.5025621 0.8368960
1473 1474 1475 1476 1477 1478 1479 1480
0.2126244 0.7950139 0.3698991 0.2979029 0.7326843 0.1556872 0.8463985 0.7326843
1481 1482 1483 1484 1485 1486 1487 1488
0.3698991 0.8463985 0.3698991 0.9582384 0.4364813 0.5093386 0.2979029 0.7267631
1489 1490 1491 1492 1493 1494 1495 1496
0.4364813 0.6607947 0.6607947 0.3120550 0.5093386 0.7326843 0.5825445 0.3698991
1497 1498 1499 1500 1501 1502 1503 1504
0.4364813 0.7950139 0.6607947 0.5825445 0.7326843 0.5093386 0.9181070 0.1954771
1505 1506 1507 1508 1509 1510 1511 1512
0.7950139 0.6607947 0.2254449 0.5093386 0.1954771 0.5093386 0.2639710 0.4364813
1513 1514 1515 1516 1517 1518 1519 1520
0.3698991 0.2639710 0.5825445 0.4258949 0.5825445 0.5025621 0.1726988 0.7326843
1521 1522 1523 1524 1525 1526 1527 1528
0.4364813 0.6585473 0.5025621 0.7950139 0.5093386 0.6607947 0.2500383 0.6607947
1529 1530 1531 1532 1533 1534 1535 1536
0.5825445 0.6607947 0.4258949 0.7866563 0.7326843 0.4364813 0.3120550 0.9412824
1537 1538 1539 1540 1541 1542 1543 1544
0.2639710 0.5025621 0.6607947 0.5825445 0.8463985 0.6607947 0.2500383 0.3698991
1545 1546 1547 1548 1549 1550 1551 1552
0.8463985 0.8463985 0.3120550 0.6607947 0.6607947 0.1726988 0.2639710 0.2254449
1553 1554 1555 1556 1557 1558 1559 1560
0.2979029 0.3698991 0.4364813 0.3120550 0.5847767 0.2254449 0.6585473 0.3567994
1561 1562 1563 1564 1565 1566 1567 1568
0.1842596 0.5847767 0.9412824 0.5847767 0.6585473 0.6607947 0.5025621 0.9412824
1569 1570 1571 1572 1573 1574 1575 1576
0.1726988 0.4364813 0.7950139 0.6585473 0.7326843 0.4364813 0.3698991 0.5847767
1577 1578 1579 1580 1581 1582 1583 1584
0.3120550 0.5847767 0.5847767 0.8463985 0.2500383 0.3698991 0.8368960 0.1726988
1585 1586 1587 1588 1589 1590 1591 1592
0.8870283 0.7267631 0.5847767 0.2979029 0.1954771 0.7326843 0.7326843 0.3567994
1593 1594 1595 1596 1597 1598 1599 1600
0.5847767 0.2639710 0.5847767 0.5093386 0.7950139 0.2639710 0.7950139 0.5093386
1601 1602 1603 1604 1605 1606 1607 1608
0.5847767 0.9704721 0.1632496 0.3698991 0.6607947 0.7326843 0.3698991 0.3698991
1609 1610 1611 1612 1613 1614 1615 1616
0.2979029 0.5093386 0.5825445 0.3698991 0.7326843 0.2500383 0.8870283 0.5825445
1617 1618 1619 1620 1621 1622 1623 1624
0.5093386 0.2639710 0.4364813 0.1954771 0.5847767 0.7950139 0.5825445 0.5093386
1625 1626 1627 1628 1629 1630 1631 1632
0.3567994 0.2126244 0.6607947 0.7326843 0.8870283 0.7326843 0.5825445 0.4258949
1633 1634 1635 1636 1637 1638 1639 1640
0.5093386 0.6607947 0.2639710 0.7326843 0.7326843 0.7326843 0.3120550 0.5093386
1641 1642 1643 1644 1645 1646 1647 1648
0.8870283 0.6607947 0.4258949 0.6607947 0.5093386 0.7950139 0.2979029 0.7326843
1649 1650 1651 1652 1653 1654 1655 1656
0.5025621 0.5025621 0.3120550 0.7950139 0.3120550 0.5093386 0.4364813 0.5825445
1657 1658 1659 1660 1661 1662 1663 1664
0.2639710 0.4364813 0.1954771 0.7950139 0.6585473 0.7950139 0.9412824 0.5093386
1665 1666 1667 1668 1669 1670 1671 1672
0.2979029 0.5093386 0.5847767 0.8463985 0.1431489 0.6607947 0.3120550 0.3120550
1673 1674 1675 1676 1677 1678 1679 1680
0.5025621 0.2126244 0.4258949 0.5025621 0.7326843 0.5093386 0.5093386 0.7326843
1681 1682 1683 1684 1685 1686 1687 1688
0.4258949 0.2979029 0.5825445 0.7326843 0.5025621 0.4258949 0.2639710 0.2639710
1689 1690 1691 1692 1693 1694 1695 1696
0.2254449 0.7267631 0.3698991 0.5825445 0.5025621 0.2500383 0.6585473 0.3120550
1697 1698 1699 1700 1701 1702 1703 1704
0.3698991 0.6585473 0.4364813 0.4364813 0.1556872 0.1431489 0.5825445 0.5025621
1705 1706 1707 1708 1709 1710 1711 1712
0.3698991 0.2639710 0.7267631 0.6607947 0.9181070 0.5825445 0.1954771 0.3567994
1713 1714 1715 1716 1717 1718 1719 1720
0.2639710 0.5825445 0.3120550 0.4364813 0.9412824 0.5025621 0.2254449 0.2639710
1721 1722 1723 1724 1725 1726 1727 1728
0.6607947 0.2254449 0.7326843 0.3698991 0.5093386 0.6585473 0.5847767 0.2254449
1729 1730 1731 1732 1733 1734 1735 1736
0.5847767 0.1954771 0.3698991 0.4364813 0.8463985 0.5825445 0.6607947 0.5093386
1737 1738 1739 1740 1741 1742 1743 1744
0.1726988 0.6585473 0.2639710 0.5093386 0.5847767 0.2979029 0.4364813 0.9704721
1745 1746 1747 1748 1749 1750 1751 1752
0.5093386 0.3698991 0.2639710 0.3120550 0.8463985 0.1726988 0.4258949 0.7326843
1753 1754 1755 1756 1757 1758 1759 1760
0.2639710 0.9582384 0.3120550 0.6585473 0.5025621 0.2639710 0.3120550 0.7267631
1761 1762 1763 1764 1765 1766 1767 1768
0.8463985 0.2500383 0.3120550 0.5025621 0.5847767 0.8774494 0.9582384 0.6607947
1769 1770 1771 1772 1773 1774 1775 1776
0.5093386 0.8463985 0.3567994 0.2979029 0.7950139 0.5847767 0.1842596 0.8870283
1777 1778 1779 1780 1781 1782 1783 1784
0.6607947 0.4364813 0.2254449 0.3698991 0.7866563 0.2979029 0.2639710 0.5093386
1785 1786 1787 1788 1789 1790 1791 1792
0.2126244 0.1726988 0.3698991 0.6585473 0.7950139 0.7950139 0.4258949 0.5847767
1793 1794 1795 1796 1797 1798 1799 1800
0.7950139 0.5847767 0.9412824 0.6585473 0.3698991 0.7950139 0.5847767 0.5825445
1801 1802 1803 1804 1805 1806 1807 1808
0.3120550 0.5847767 0.7866563 0.3120550 0.5825445 0.5825445 0.3567994 0.5025621
1809 1810 1811 1812 1813 1814 1815 1816
0.4364813 0.5825445 0.4258949 0.8870283 0.3120550 0.5093386 0.2126244 0.3698991
1817 1818 1819 1820 1821 1822 1823 1824
0.8463985 0.2254449 0.6607947 0.2254449 0.3698991 0.9181070 0.4364813 0.3120550
1825 1826 1827 1828 1829 1830 1831 1832
0.2254449 0.2639710 0.7326843 0.7866563 0.1726988 0.1726988 0.5825445 0.5093386
1833 1834 1835 1836 1837 1838 1839 1840
0.6607947 0.3698991 0.2254449 0.2979029 0.5093386 0.3567994 0.4364813 0.5093386
1841 1842 1843 1844 1845 1846 1847 1848
0.5025621 0.7326843 0.7866563 0.3698991 0.5025621 0.7866563 0.6607947 0.1726988
1849 1850 1851 1852 1853 1854 1855 1856
0.6607947 0.1954771 0.5025621 0.2639710 0.2639710 0.6607947 0.5847767 0.8463985
1857 1858 1859 1860 1861 1862 1863 1864
0.2254449 0.3120550 0.5825445 0.4364813 0.4364813 0.3567994 0.2126244 0.2254449
1865 1866 1867 1868 1869 1870 1871 1872
0.3120550 0.7950139 0.7950139 0.4364813 0.2639710 0.5847767 0.6585473 0.5825445
1873 1874 1875 1876 1877 1878 1879 1880
0.8463985 0.6607947 0.6585473 0.3698991 0.3567994 0.6585473 0.6607947 0.7950139
1881 1882 1883 1884 1885 1886 1887 1888
0.8870283 0.5825445 0.3698991 0.2979029 0.7267631 0.6585473 0.7326843 0.4364813
1889 1890 1891 1892 1893 1894 1895 1896
0.6585473 0.4258949 0.1726988 0.5025621 0.1954771 0.5093386 0.4258949 0.5025621
1897 1898 1899 1900 1901 1902 1903 1904
0.5825445 0.5825445 0.8463985 0.5825445 0.4364813 0.5847767 0.6607947 0.3120550
1905 1906 1907 1908 1909 1910 1911 1912
0.7326843 0.7267631 0.2979029 0.7326843 0.7326843 0.3120550 0.7326843 0.6607947
1913 1914 1915 1916 1917 1918 1919 1920
0.3120550 0.8463985 0.3698991 0.5025621 0.7267631 0.7950139 0.3120550 0.7326843
1921 1922 1923 1924 1925 1926 1927 1928
0.3698991 0.3698991 0.1954771 0.1726988 0.9181070 0.2639710 0.5825445 0.3567994
1929 1930 1931 1932 1933 1934 1935 1936
0.6585473 0.4258949 0.2500383 0.5093386 0.6607947 0.2639710 0.5025621 0.5093386
1937 1938 1939 1940 1941 1942 1943 1944
0.8463985 0.2639710 0.2500383 0.5825445 0.4364813 0.5093386 0.6607947 0.2254449
1945 1946 1947 1948 1949 1950 1951 1952
0.4364813 0.8870283 0.5847767 0.7326843 0.7326843 0.3120550 0.6585473 0.9091805
1953 1954 1955 1956 1957 1958 1959 1960
0.2979029 0.7267631 0.5025621 0.4258949 0.6607947 0.3120550 0.7326843 0.7326843
1961 1962 1963 1964 1965 1966 1967 1968
0.3567994 0.8463985 0.5093386 0.5847767 0.5825445 0.3567994 0.6585473 0.7267631
1969 1970 1971 1972 1973 1974 1975 1976
0.4364813 0.8463985 0.5847767 0.5825445 0.5825445 0.1954771 0.5825445 0.7326843
1977 1978 1979 1980 1981 1982 1983 1984
0.5025621 0.4258949 0.3120550 0.1556872 0.3567994 0.6607947 0.5093386 0.3567994
1985 1986 1987 1988 1989 1990 1991 1992
0.2639710 0.3567994 0.4364813 0.7326843 0.2979029 0.8368960 0.3120550 0.6607947
1993 1994 1995 1996 1997 1998 1999 2000
0.2979029 0.7326843 0.8368960 0.5093386 0.4364813 0.7326843 0.7326843 0.7267631
Code
summary(residuals(fit1))
Output
Item1 Item2 Item3
Min. :-0.9412824 Min. :-0.9314870 Min. :-0.932174
1st Qu.:-0.3698991 1st Qu.:-0.4272403 1st Qu.:-0.503827
Median : 0.0818930 Median : 0.1573856 Median : 0.177257
Mean :-0.0001467 Mean :-0.0006539 Mean :-0.002202
3rd Qu.: 0.4152233 3rd Qu.: 0.3637518 3rd Qu.: 0.292406
Max. : 0.8443128 Max. : 0.8829579 Max. : 0.653850
Item4 Item5 Item6
Min. :-0.936174 Min. :-0.968908 Min. :-0.9010826
1st Qu.: 0.077120 1st Qu.: 0.043100 1st Qu.:-0.4807352
Median : 0.158674 Median : 0.108763 Median : 0.1642555
Mean : 0.000033 Mean : 0.002275 Mean : 0.0004391
3rd Qu.: 0.220781 3rd Qu.: 0.186779 3rd Qu.: 0.3180502
Max. : 0.590711 Max. : 0.574219 Max. : 0.8244711
Item7 Item8 Item9
Min. :-0.8851430 Min. :-0.9101997 Min. :-0.8614888
1st Qu.:-0.4890386 1st Qu.:-0.4831021 1st Qu.:-0.5056860
Median : 0.2111020 Median : 0.2142025 Median : 0.2282535
Mean : 0.0005126 Mean : 0.0009005 Mean : 0.0003707
3rd Qu.: 0.3426624 3rd Qu.: 0.3589697 3rd Qu.: 0.3974888
Max. : 0.7690036 Max. : 0.7267992 Max. : 0.7788047
Item10 Item11 Item12
Min. :-0.8362669 Min. :-0.9316052 Min. :-0.8873538
1st Qu.:-0.5002406 1st Qu.:-0.4619166 1st Qu.:-0.4532789
Median : 0.2289536 Median : 0.1821138 Median : 0.1806710
Mean : 0.0004637 Mean : 0.0003882 Mean : 0.0006462
3rd Qu.: 0.4020989 3rd Qu.: 0.3379863 3rd Qu.: 0.4403448
Max. : 0.7747230 Max. : 0.8171048 Max. : 0.7330316
Item13 Item14 Item15
Min. :-0.9364053 Min. :-0.846923 Min. :-0.8203574
1st Qu.:-0.4702768 1st Qu.:-0.391395 1st Qu.:-0.4952739
Median : 0.1899360 Median :-0.206135 Median : 0.2097287
Mean :-0.0001134 Mean : 0.000785 Mean : 0.0006185
3rd Qu.: 0.2708100 3rd Qu.: 0.482109 3rd Qu.: 0.4567820
Max. : 0.5898026 Max. : 0.865499 Max. : 0.7635355
Item16 Item17 Item18
Min. :-0.896687 Min. :-0.7793370 Min. :-0.8303705
1st Qu.:-0.380265 1st Qu.:-0.4142573 1st Qu.:-0.3447702
Median :-0.176930 Median :-0.2407092 Median :-0.1413968
Mean : 0.001382 Mean : 0.0005929 Mean : 0.0007223
3rd Qu.: 0.465682 3rd Qu.: 0.4923666 3rd Qu.: 0.4255570
Max. : 0.870413 Max. : 0.8175671 Max. : 0.9089239
Item19 Item20
Min. :-0.9068390 Min. :-0.8715910
1st Qu.:-0.4038863 1st Qu.:-0.3379118
Median :-0.1494888 Median :-0.2490140
Mean : 0.0007949 Mean : 0.0007879
3rd Qu.: 0.4376773 3rd Qu.: 0.4729031
Max. : 0.8915756 Max. : 0.7866203
Code
residuals(fit1, item = 1)
Output
1 2 3 4 5 6
-0.43648127 0.21334366 0.11297173 -0.22544489 0.20498605 -0.29790295
7 8 9 10 11 12
0.49066136 0.57410507 0.20498605 0.21334366 0.15360152 0.27323691
13 14 15 16 17 18
-0.43648127 -0.50256206 -0.73268430 -0.35679939 0.41745549 0.27323691
19 20 21 22 23 24
-0.43648127 -0.31205497 0.57410507 0.16310397 0.11297173 0.15360152
25 26 27 28 29 30
0.33920533 0.49743794 0.21334366 -0.50256206 0.27323691 -0.26397099
31 32 33 34 35 36
-0.50256206 -0.65854732 -0.79501395 -0.31205497 0.56351873 -0.50933864
37 38 39 40 41 42
-0.66079467 0.15360152 0.49743794 -0.58477674 -0.26397099 0.49743794
43 44 45 46 47 48
-0.50256206 0.20498605 0.20498605 -0.43648127 -0.31205497 0.49743794
49 50 51 52 53 54
0.77455511 -0.50933864 0.49066136 -0.26397099 0.82730124 -0.50933864
55 56 57 58 59 60
0.68794503 0.41745549 -0.31205497 0.49066136 -0.50933864 0.49066136
61 62 63 64 65 66
0.09081953 -0.66079467 0.68794503 -0.22544489 0.34145268 -0.73268430
67 68 69 70 71 72
-0.22544489 0.26731570 -0.14314887 0.05871759 -0.42589493 -0.22544489
73 74 75 76 77 78
0.56351873 0.41745549 -0.50256206 0.77455511 0.34145268 -0.25003829
79 80 81 82 83 84
-0.31205497 0.49743794 0.49066136 -0.58254451 -0.22544489 -0.35679939
85 86 87 88 89 90
-0.50256206 -0.65854732 0.70209705 -0.35679939 -0.43648127 -0.73268430
91 92 93 94 95 96
-0.29790295 0.27323691 -0.43648127 0.27323691 0.73602901 -0.22544489
97 98 99 100 101 102
0.63010089 0.15360152 -0.26397099 0.12255063 0.04176155 0.49066136
103 104 105 106 107 108
0.49066136 0.33920533 -0.36989911 -0.21262439 0.20498605 -0.79501395
109 110 111 112 113 114
0.33920533 -0.17269876 0.34145268 -0.43648127 0.63010089 0.15360152
115 116 117 118 119 120
-0.31205497 -0.58254451 0.68794503 0.11297173 -0.22544489 0.56351873
121 122 123 124 125 126
0.21334366 -0.43648127 -0.26397099 0.15360152 -0.26397099 0.49743794
127 128 129 130 131 132
0.73602901 -0.25003829 -0.26397099 -0.29790295 0.56351873 -0.42589493
133 134 135 136 137 138
-0.42589493 0.15360152 -0.36989911 -0.36989911 -0.18425962 0.41522326
139 140 141 142 143 144
0.26731570 0.15360152 -0.36989911 0.80452289 -0.29790295 0.20498605
145 146 147 148 149 150
0.49743794 -0.66079467 0.08189303 0.26731570 -0.22544489 0.68794503
151 152 153 154 155 156
-0.26397099 -0.22544489 0.33920533 -0.43648127 0.15360152 -0.35679939
157 158 159 160 161 162
-0.22544489 0.08189303 0.05871759 0.63010089 0.57410507 0.49066136
163 164 165 166 167 168
-0.91810697 -0.17269876 0.57410507 -0.43648127 0.33920533 -0.91810697
169 170 171 172 173 174
-0.26397099 0.20498605 -0.29790295 0.33920533 -0.58477674 0.41745549
175 176 177 178 179 180
-0.73268430 0.57410507 -0.43648127 0.21334366 0.11297173 -0.19547711
181 182 183 184 185 186
0.63010089 0.11297173 0.08189303 0.16310397 -0.58477674 -0.50256206
187 188 189 190 191 192
0.11297173 -0.35679939 -0.15568724 0.26731570 -0.31205497 0.49743794
193 194 195 196 197 198
0.26731570 -0.17269876 0.08189303 0.41745549 -0.58254451 -0.66079467
199 200 201 202 203 204
0.20498605 0.56351873 -0.31205497 0.41745549 -0.25003829 0.20498605
205 206 207 208 209 210
0.11297173 -0.73268430 0.26731570 -0.43648127 0.20498605 -0.36989911
211 212 213 214 215 216
-0.58477674 0.63010089 0.74996171 -0.50933864 -0.19547711 -0.29790295
217 218 219 220 221 222
0.26731570 0.20498605 0.05871759 0.26731570 0.41522326 -0.36989911
223 224 225 226 227 228
-0.36989911 -0.73268430 -0.36989911 0.57410507 0.26731570 0.49743794
229 230 231 232 233 234
0.41745549 -0.50933864 0.64320061 0.08189303 0.11297173 0.64320061
235 236 237 238 239 240
0.15360152 -0.73268430 0.41745549 -0.94128241 -0.31205497 0.08189303
241 242 243 244 245 246
0.26731570 -0.65854732 0.33920533 0.56351873 0.49743794 -0.31205497
247 248 249 250 251 252
-0.43648127 -0.58254451 0.41522326 -0.31205497 0.15360152 0.49743794
253 254 255 256 257 258
0.20498605 -0.66079467 -0.58477674 -0.58477674 0.33920533 0.56351873
259 260 261 262 263 264
0.27323691 0.20498605 -0.14314887 0.34145268 -0.35679939 -0.26397099
265 266 267 268 269 270
0.33920533 -0.58254451 0.15360152 -0.43648127 0.21334366 -0.17269876
271 272 273 274 275 276
-0.43648127 -0.66079467 0.20498605 -0.35679939 0.08189303 -0.31205497
277 278 279 280 281 282
-0.50933864 -0.19547711 0.64320061 -0.50933864 -0.26397099 0.26731570
283 284 285 286 287 288
0.41745549 -0.42589493 -0.58477674 -0.78665634 0.49743794 -0.79501395
289 290 291 292 293 294
0.05871759 -0.42589493 -0.42589493 -0.26397099 0.68794503 -0.58254451
295 296 297 298 299 300
0.56351873 0.15360152 0.41522326 -0.50256206 0.41522326 0.15360152
301 302 303 304 305 306
0.41522326 -0.36989911 -0.35679939 -0.25003829 -0.66079467 0.08189303
307 308 309 310 311 312
0.20498605 0.08189303 0.57410507 -0.36989911 -0.66079467 -0.26397099
313 314 315 316 317 318
-0.43648127 -0.50933864 0.12255063 0.20498605 -0.50933864 -0.65854732
319 320 321 322 323 324
0.41745549 -0.31205497 0.73602901 0.49066136 0.26731570 -0.13399692
325 326 327 328 329 330
0.34145268 0.26731570 -0.35679939 0.20498605 0.26731570 -0.17269876
331 332 333 334 335 336
-0.31205497 -0.36989911 -0.43648127 0.73602901 0.11297173 0.49743794
337 338 339 340 341 342
-0.58254451 0.56351873 0.33920533 -0.36989911 -0.66079467 0.41522326
343 344 345 346 347 348
0.20498605 -0.73268430 0.49743794 0.49743794 0.56351873 0.57410507
349 350 351 352 353 354
0.15360152 -0.26397099 -0.19547711 0.15360152 -0.58254451 0.33920533
355 356 357 358 359 360
-0.29790295 0.15360152 -0.50933864 -0.26397099 -0.29790295 0.57410507
361 362 363 364 365 366
0.21334366 0.73602901 -0.50933864 -0.29790295 0.11297173 -0.43648127
367 368 369 370 371 372
-0.35679939 -0.31205497 -0.26397099 -0.58477674 -0.35679939 -0.50933864
373 374 375 376 377 378
0.77455511 -0.42589493 0.41745549 0.68794503 -0.17269876 -0.73268430
379 380 381 382 383 384
0.49743794 0.20498605 0.15360152 -0.50933864 0.57410507 -0.58254451
385 386 387 388 389 390
-0.66079467 0.05871759 -0.50256206 0.02952791 -0.35679939 -0.15568724
391 392 393 394 395 396
-0.31205497 -0.36989911 0.27323691 -0.50933864 -0.50256206 0.63010089
397 398 399 400 401 402
-0.42589493 0.16310397 -0.83689603 0.15360152 -0.25003829 -0.31205497
403 404 405 406 407 408
-0.43648127 -0.84639848 0.08189303 -0.36989911 0.49066136 0.41522326
409 410 411 412 413 414
0.56351873 0.16310397 0.34145268 0.56351873 0.20498605 -0.42589493
415 416 417 418 419 420
-0.58254451 -0.19547711 0.49066136 -0.29790295 0.57410507 0.84431276
421 422 423 424 425 426
-0.31205497 0.34145268 -0.43648127 -0.43648127 0.49066136 -0.35679939
427 428 429 430 431 432
-0.65854732 0.34145268 -0.50933864 0.49743794 -0.36989911 0.56351873
433 434 435 436 437 438
0.49743794 0.15360152 -0.31205497 0.21334366 0.20498605 0.73602901
439 440 441 442 443 444
0.74996171 -0.50933864 0.41522326 0.49066136 0.26731570 -0.22544489
445 446 447 448 449 450
-0.31205497 0.20498605 -0.65854732 -0.65854732 -0.19547711 -0.31205497
451 452 453 454 455 456
0.41522326 -0.17269876 0.16310397 0.49066136 0.49743794 0.27323691
457 458 459 460 461 462
0.49066136 0.41522326 0.68794503 0.26731570 0.49066136 0.41522326
463 464 465 466 467 468
-0.72676309 -0.79501395 0.78737561 0.73602901 -0.50933864 0.26731570
469 470 471 472 473 474
-0.58477674 0.34145268 -0.58254451 0.33920533 -0.31205497 -0.50256206
475 476 477 478 479 480
0.20498605 0.26731570 -0.35679939 -0.50933864 -0.50933864 0.26731570
481 482 483 484 485 486
0.15360152 -0.58477674 0.20498605 0.26731570 0.49743794 -0.31205497
487 488 489 490 491 492
0.20498605 -0.66079467 -0.43648127 0.34145268 -0.73268430 -0.22544489
493 494 495 496 497 498
0.77455511 -0.31205497 -0.50256206 0.80452289 0.70209705 0.41522326
499 500 501 502 503 504
-0.50256206 0.21334366 -0.84639848 -0.25003829 -0.36989911 0.68794503
505 506 507 508 509 510
-0.50256206 0.57410507 0.68794503 -0.43648127 0.68794503 0.68794503
511 512 513 514 515 516
-0.31205497 -0.18425962 -0.21262439 0.33920533 -0.72676309 -0.29790295
517 518 519 520 521 522
-0.36989911 0.57410507 -0.26397099 0.49066136 0.41745549 0.49066136
523 524 525 526 527 528
0.09081953 -0.78665634 0.49066136 0.34145268 0.11297173 -0.58477674
529 530 531 532 533 534
-0.26397099 -0.84639848 0.15360152 -0.58477674 -0.36989911 0.57410507
535 536 537 538 539 540
0.68794503 0.33920533 0.33920533 0.26731570 0.49743794 0.49066136
541 542 543 544 545 546
-0.26397099 -0.36989911 0.34145268 -0.35679939 0.49743794 0.26731570
547 548 549 550 551 552
-0.50933864 -0.43648127 -0.36989911 0.77455511 -0.43648127 -0.22544489
553 554 555 556 557 558
0.26731570 0.56351873 0.64320061 -0.43648127 0.34145268 -0.26397099
559 560 561 562 563 564
0.33920533 0.49066136 0.15360152 -0.25003829 0.49743794 0.20498605
565 566 567 568 569 570
0.56351873 0.41522326 -0.26397099 -0.73268430 0.08189303 0.57410507
571 572 573 574 575 576
-0.50256206 -0.72676309 -0.36989911 0.56351873 0.49743794 -0.50933864
577 578 579 580 581 582
-0.36989911 0.26731570 0.82730124 -0.36989911 0.57410507 -0.19547711
583 584 585 586 587 588
0.49743794 -0.50256206 0.49066136 -0.26397099 -0.43648127 -0.15568724
589 590 591 592 593 594
-0.79501395 0.56351873 0.11297173 -0.21262439 0.64320061 0.41745549
595 596 597 598 599 600
-0.50256206 0.49743794 0.56351873 -0.22544489 0.41522326 -0.66079467
601 602 603 604 605 606
0.73602901 -0.25003829 0.63010089 0.73602901 -0.31205497 0.26731570
607 608 609 610 611 612
-0.84639848 -0.73268430 0.21334366 0.41522326 0.33920533 -0.58477674
613 614 615 616 617 618
-0.58254451 0.21334366 0.56351873 -0.15568724 -0.50256206 -0.15568724
619 620 621 622 623 624
-0.73268430 -0.29790295 -0.43648127 0.80452289 -0.43648127 -0.43648127
625 626 627 628 629 630
-0.36989911 -0.19547711 -0.79501395 0.57410507 0.11297173 0.26731570
631 632 633 634 635 636
-0.22544489 -0.66079467 0.41745549 0.20498605 0.26731570 0.15360152
637 638 639 640 641 642
0.33920533 0.26731570 0.41745549 0.11297173 -0.42589493 -0.58254451
643 644 645 646 647 648
0.41745549 -0.58477674 0.77455511 -0.35679939 0.68794503 0.64320061
649 650 651 652 653 654
-0.42589493 0.49743794 -0.87744937 -0.42589493 0.26731570 0.26731570
655 656 657 658 659 660
-0.66079467 -0.43648127 -0.79501395 0.26731570 -0.19547711 0.26731570
661 662 663 664 665 666
-0.31205497 0.20498605 -0.26397099 -0.31205497 0.68794503 0.02952791
667 668 669 670 671 672
-0.58254451 0.33920533 0.70209705 -0.22544489 -0.36989911 0.41522326
673 674 675 676 677 678
-0.43648127 -0.31205497 0.26731570 -0.58477674 -0.19547711 -0.43648127
679 680 681 682 683 684
-0.31205497 0.56351873 -0.17269876 -0.42589493 -0.31205497 -0.15568724
685 686 687 688 689 690
0.33920533 -0.88702827 -0.43648127 0.16310397 -0.31205497 0.63010089
691 692 693 694 695 696
0.15360152 -0.50933864 0.49743794 -0.50933864 -0.25003829 0.20498605
697 698 699 700 701 702
0.20498605 0.49743794 0.73602901 0.20498605 0.74996171 0.73602901
703 704 705 706 707 708
0.08189303 -0.26397099 0.63010089 0.70209705 -0.79501395 -0.42589493
709 710 711 712 713 714
0.57410507 0.68794503 0.84431276 0.11297173 0.20498605 0.41745549
715 716 717 718 719 720
-0.50933864 -0.50933864 0.41745549 -0.19547711 -0.31205497 -0.50256206
721 722 723 724 725 726
0.49066136 0.57410507 -0.50256206 -0.58254451 -0.84639848 -0.72676309
727 728 729 730 731 732
-0.19547711 -0.36989911 -0.50933864 0.33920533 -0.36989911 -0.73268430
733 734 735 736 737 738
-0.58254451 0.63010089 -0.65854732 0.63010089 0.26731570 -0.22544489
739 740 741 742 743 744
-0.35679939 0.16310397 -0.50933864 -0.58477674 0.16310397 0.26731570
745 746 747 748 749 750
-0.31205497 0.49066136 -0.25003829 -0.88702827 0.27323691 -0.29790295
751 752 753 754 755 756
0.27323691 -0.58477674 -0.58477674 0.33920533 -0.50256206 0.26731570
757 758 759 760 761 762
-0.42589493 0.63010089 -0.66079467 0.05871759 -0.22544489 0.63010089
763 764 765 766 767 768
0.34145268 -0.17269876 0.73602901 -0.31205497 -0.50256206 -0.58254451
769 770 771 772 773 774
-0.50256206 -0.35679939 -0.35679939 0.11297173 0.26731570 -0.50933864
775 776 777 778 779 780
-0.31205497 -0.35679939 0.34145268 0.26731570 -0.36989911 0.15360152
781 782 783 784 785 786
0.33920533 0.64320061 0.20498605 -0.36989911 -0.42589493 0.68794503
787 788 789 790 791 792
-0.26397099 0.49743794 0.49743794 0.41522326 0.70209705 0.15360152
793 794 795 796 797 798
-0.50256206 0.26731570 -0.19547711 0.49743794 0.56351873 -0.58254451
799 800 801 802 803 804
-0.35679939 -0.26397099 0.41745549 -0.58254451 0.20498605 0.16310397
805 806 807 808 809 810
0.27323691 -0.73268430 -0.31205497 -0.42589493 -0.26397099 0.16310397
811 812 813 814 815 816
0.15360152 -0.50933864 -0.72676309 0.20498605 -0.26397099 -0.42589493
817 818 819 820 821 822
-0.22544489 -0.50933864 -0.43648127 -0.19547711 -0.79501395 0.26731570
823 824 825 826 827 828
0.20498605 0.11297173 0.11297173 0.63010089 0.57410507 -0.19547711
829 830 831 832 833 834
-0.50256206 -0.43648127 -0.22544489 -0.25003829 -0.50933864 0.49066136
835 836 837 838 839 840
-0.31205497 -0.22544489 0.56351873 -0.35679939 -0.84639848 0.41522326
841 842 843 844 845 846
0.33920533 0.57410507 -0.73268430 -0.29790295 -0.58477674 -0.36989911
847 848 849 850 851 852
-0.26397099 -0.26397099 0.26731570 -0.17269876 -0.50256206 0.57410507
853 854 855 856 857 858
-0.58254451 -0.25003829 0.41745549 -0.36989911 -0.50933864 0.41745549
859 860 861 862 863 864
0.49066136 -0.73268430 0.49066136 0.26731570 -0.65854732 0.21334366
865 866 867 868 869 870
0.56351873 -0.58477674 0.16310397 0.49066136 0.41745549 -0.26397099
871 872 873 874 875 876
0.41745549 0.11297173 0.33920533 0.63010089 0.08189303 0.26731570
877 878 879 880 881 882
0.41745549 0.64320061 0.56351873 0.34145268 -0.36989911 -0.19547711
883 884 885 886 887 888
0.49743794 0.11297173 0.41745549 0.56351873 0.33920533 0.41745549
889 890 891 892 893 894
-0.36989911 0.20498605 0.57410507 -0.19547711 0.57410507 0.34145268
895 896 897 898 899 900
0.41522326 0.56351873 -0.31205497 0.16310397 -0.65854732 0.33920533
901 902 903 904 905 906
-0.58254451 -0.31205497 0.21334366 0.68794503 0.26731570 -0.26397099
907 908 909 910 911 912
0.26731570 0.41522326 0.68794503 0.20498605 0.33920533 -0.36989911
913 914 915 916 917 918
-0.58477674 0.41745549 -0.36989911 0.20498605 0.63010089 0.56351873
919 920 921 922 923 924
0.41522326 0.11297173 -0.58477674 0.64320061 -0.42589493 -0.50256206
925 926 927 928 929 930
0.33920533 -0.19547711 0.41745549 0.41522326 0.49743794 -0.58477674
931 932 933 934 935 936
0.26731570 0.21334366 0.41745549 -0.42589493 -0.58254451 -0.36989911
937 938 939 940 941 942
-0.58477674 -0.72676309 -0.19547711 -0.73268430 -0.50933864 -0.42589493
943 944 945 946 947 948
-0.36989911 0.11297173 -0.31205497 -0.58477674 -0.19547711 -0.25003829
949 950 951 952 953 954
0.41745549 0.49743794 0.41522326 0.49066136 -0.36989911 0.11297173
955 956 957 958 959 960
0.33920533 0.80452289 -0.50256206 -0.50933864 0.77455511 0.15360152
961 962 963 964 965 966
0.21334366 -0.26397099 0.80452289 -0.66079467 -0.22544489 -0.22544489
967 968 969 970 971 972
-0.43648127 0.34145268 0.56351873 0.73602901 0.63010089 -0.17269876
973 974 975 976 977 978
-0.58254451 -0.72676309 -0.36989911 0.33920533 -0.72676309 0.27323691
979 980 981 982 983 984
0.33920533 -0.73268430 0.34145268 0.08189303 -0.58254451 -0.79501395
985 986 987 988 989 990
0.77455511 -0.66079467 -0.58254451 -0.26397099 0.49066136 0.41522326
991 992 993 994 995 996
-0.13399692 0.41522326 0.49066136 -0.36989911 0.26731570 -0.35679939
997 998 999 1000 1001 1002
-0.91810697 -0.36989911 0.11297173 0.33920533 0.41745549 0.64320061
1003 1004 1005 1006 1007 1008
-0.22544489 0.57410507 0.26731570 -0.29790295 0.04176155 -0.66079467
1009 1010 1011 1012 1013 1014
0.41522326 0.49743794 -0.22544489 -0.66079467 -0.16324961 0.56351873
1015 1016 1017 1018 1019 1020
0.41745549 0.73602901 0.82730124 0.26731570 -0.35679939 0.41745549
1021 1022 1023 1024 1025 1026
0.26731570 -0.42589493 0.08189303 0.57410507 0.34145268 0.56351873
1027 1028 1029 1030 1031 1032
0.33920533 0.77455511 -0.31205497 0.33920533 -0.42589493 0.41522326
1033 1034 1035 1036 1037 1038
0.49066136 -0.19547711 0.27323691 -0.58254451 -0.15568724 -0.42589493
1039 1040 1041 1042 1043 1044
-0.73268430 -0.18425962 0.33920533 0.63010089 -0.42589493 -0.65854732
1045 1046 1047 1048 1049 1050
0.34145268 -0.66079467 -0.31205497 0.64320061 -0.36989911 0.20498605
1051 1052 1053 1054 1055 1056
-0.26397099 0.68794503 -0.66079467 -0.42589493 0.49066136 -0.43648127
1057 1058 1059 1060 1061 1062
0.74996171 0.56351873 -0.36989911 0.63010089 -0.29790295 0.64320061
1063 1064 1065 1066 1067 1068
0.41522326 0.49743794 0.26731570 0.26731570 0.20498605 0.15360152
1069 1070 1071 1072 1073 1074
-0.35679939 -0.66079467 -0.43648127 -0.42589493 -0.43648127 -0.15568724
1075 1076 1077 1078 1079 1080
0.20498605 0.20498605 -0.14314887 0.77455511 -0.50256206 -0.26397099
1081 1082 1083 1084 1085 1086
0.57410507 0.77455511 0.05871759 -0.17269876 0.49743794 0.73602901
1087 1088 1089 1090 1091 1092
-0.25003829 0.49743794 -0.21262439 0.57410507 0.41522326 0.20498605
1093 1094 1095 1096 1097 1098
-0.88702827 0.20498605 -0.66079467 -0.50933864 -0.58477674 0.80452289
1099 1100 1101 1102 1103 1104
0.68794503 0.68794503 0.05871759 -0.25003829 0.26731570 0.73602901
1105 1106 1107 1108 1109 1110
-0.72676309 -0.58477674 -0.31205497 0.57410507 0.49066136 0.57410507
1111 1112 1113 1114 1115 1116
0.41522326 -0.43648127 -0.36989911 -0.25003829 -0.73268430 0.34145268
1117 1118 1119 1120 1121 1122
0.49743794 0.33920533 0.49066136 0.20498605 0.11297173 0.26731570
1123 1124 1125 1126 1127 1128
0.49743794 0.33920533 0.41745549 0.41745549 -0.35679939 0.63010089
1129 1130 1131 1132 1133 1134
0.57410507 0.33920533 -0.17269876 -0.36989911 0.16310397 -0.42589493
1135 1136 1137 1138 1139 1140
0.11297173 0.57410507 -0.29790295 -0.50256206 0.74996171 0.41745549
1141 1142 1143 1144 1145 1146
-0.36989911 -0.17269876 -0.36989911 0.63010089 0.41522326 -0.58477674
1147 1148 1149 1150 1151 1152
0.20498605 -0.50933864 -0.36989911 -0.43648127 0.33920533 -0.25003829
1153 1154 1155 1156 1157 1158
-0.25003829 -0.22544489 -0.79501395 0.08189303 0.11297173 -0.43648127
1159 1160 1161 1162 1163 1164
0.08189303 -0.73268430 -0.21262439 -0.22544489 0.49743794 0.27323691
1165 1166 1167 1168 1169 1170
0.26731570 -0.21262439 -0.50256206 0.77455511 0.49743794 -0.73268430
1171 1172 1173 1174 1175 1176
0.57410507 -0.42589493 0.70209705 0.09081953 0.05871759 -0.72676309
1177 1178 1179 1180 1181 1182
-0.79501395 0.49066136 -0.15568724 0.41745549 0.20498605 -0.50256206
1183 1184 1185 1186 1187 1188
0.49743794 -0.66079467 -0.43648127 -0.58254451 -0.31205497 0.41522326
1189 1190 1191 1192 1193 1194
-0.50933864 0.57410507 0.33920533 0.12255063 -0.78665634 0.49066136
1195 1196 1197 1198 1199 1200
0.20498605 0.68794503 -0.79501395 0.56351873 0.74996171 0.21334366
1201 1202 1203 1204 1205 1206
0.34145268 0.41522326 -0.35679939 0.33920533 -0.42589493 0.11297173
1207 1208 1209 1210 1211 1212
0.08189303 -0.26397099 0.08189303 0.68794503 0.68794503 -0.66079467
1213 1214 1215 1216 1217 1218
-0.36989911 0.05871759 0.63010089 0.56351873 -0.65854732 -0.65854732
1219 1220 1221 1222 1223 1224
-0.83689603 -0.17269876 -0.50933864 0.20498605 0.56351873 -0.58254451
1225 1226 1227 1228 1229 1230
-0.31205497 0.20498605 0.63010089 -0.72676309 0.26731570 0.56351873
1231 1232 1233 1234 1235 1236
0.49066136 -0.79501395 -0.50933864 -0.73268430 0.08189303 -0.35679939
1237 1238 1239 1240 1241 1242
0.20498605 -0.42589493 -0.73268430 -0.31205497 0.41745549 0.33920533
1243 1244 1245 1246 1247 1248
0.41745549 0.26731570 -0.72676309 -0.50256206 0.27323691 -0.58254451
1249 1250 1251 1252 1253 1254
0.05871759 -0.31205497 0.68794503 -0.31205497 0.56351873 -0.50933864
1255 1256 1257 1258 1259 1260
0.63010089 -0.22544489 -0.50933864 -0.73268430 0.49743794 -0.58254451
1261 1262 1263 1264 1265 1266
0.49066136 -0.19547711 -0.31205497 0.33920533 -0.58254451 -0.17269876
1267 1268 1269 1270 1271 1272
-0.50256206 0.33920533 0.49743794 -0.29790295 0.70209705 -0.50933864
1273 1274 1275 1276 1277 1278
0.27323691 -0.35679939 0.21334366 0.26731570 0.68794503 0.15360152
1279 1280 1281 1282 1283 1284
-0.43648127 -0.65854732 -0.31205497 0.08189303 -0.17269876 0.41522326
1285 1286 1287 1288 1289 1290
0.41522326 -0.25003829 0.49743794 0.05871759 -0.17269876 -0.50933864
1291 1292 1293 1294 1295 1296
-0.84639848 -0.17269876 0.41522326 0.34145268 -0.66079467 -0.43648127
1297 1298 1299 1300 1301 1302
0.34145268 0.15360152 0.63010089 -0.58477674 0.20498605 -0.17269876
1303 1304 1305 1306 1307 1308
0.20498605 -0.31205497 0.21334366 -0.66079467 -0.26397099 -0.58254451
1309 1310 1311 1312 1313 1314
-0.50256206 0.15360152 0.15360152 0.57410507 -0.36989911 0.77455511
1315 1316 1317 1318 1319 1320
-0.50933864 -0.73268430 0.20498605 0.11297173 0.27323691 0.33920533
1321 1322 1323 1324 1325 1326
0.41522326 0.63010089 -0.50256206 0.20498605 -0.65854732 -0.26397099
1327 1328 1329 1330 1331 1332
0.68794503 0.49066136 0.15360152 0.70209705 0.34145268 -0.50256206
1333 1334 1335 1336 1337 1338
-0.73268430 0.63010089 -0.50256206 0.20498605 0.73602901 0.41745549
1339 1340 1341 1342 1343 1344
0.20498605 0.73602901 0.68794503 0.08189303 0.41745549 -0.29790295
1345 1346 1347 1348 1349 1350
0.41745549 -0.43648127 -0.50256206 0.77455511 -0.31205497 0.49066136
1351 1352 1353 1354 1355 1356
0.63010089 -0.50256206 0.11297173 0.21334366 -0.35679939 0.41522326
1357 1358 1359 1360 1361 1362
-0.50933864 0.41745549 0.21334366 0.15360152 -0.26397099 0.33920533
1363 1364 1365 1366 1367 1368
0.41745549 0.77455511 0.49066136 0.33920533 0.08189303 0.63010089
1369 1370 1371 1372 1373 1374
0.33920533 0.08189303 0.34145268 0.33920533 0.09081953 -0.31205497
1375 1376 1377 1378 1379 1380
0.56351873 0.49066136 0.20498605 -0.43648127 -0.58254451 -0.42589493
1381 1382 1383 1384 1385 1386
-0.22544489 -0.50256206 -0.58254451 -0.19547711 0.20498605 -0.31205497
1387 1388 1389 1390 1391 1392
-0.36989911 -0.19547711 -0.15568724 0.20498605 0.49066136 -0.50933864
1393 1394 1395 1396 1397 1398
0.26731570 0.21334366 0.21334366 0.26731570 -0.42589493 0.41522326
1399 1400 1401 1402 1403 1404
-0.43648127 -0.21262439 0.20498605 0.02952791 -0.42589493 0.20498605
1405 1406 1407 1408 1409 1410
-0.36989911 0.63010089 -0.66079467 -0.50933864 0.12255063 0.20498605
1411 1412 1413 1414 1415 1416
-0.25003829 0.49743794 -0.26397099 -0.35679939 -0.42589493 -0.91810697
1417 1418 1419 1420 1421 1422
0.34145268 -0.73268430 -0.21262439 -0.58477674 -0.31205497 0.49066136
1423 1424 1425 1426 1427 1428
0.56351873 0.15360152 0.33920533 0.26731570 0.33920533 -0.29790295
1429 1430 1431 1432 1433 1434
-0.42589493 -0.19547711 -0.72676309 -0.31205497 0.41745549 0.49743794
1435 1436 1437 1438 1439 1440
-0.42589493 0.80452289 -0.73268430 0.04176155 0.26731570 0.49743794
1441 1442 1443 1444 1445 1446
0.80452289 -0.73268430 -0.58477674 0.41745549 0.15360152 -0.26397099
1447 1448 1449 1450 1451 1452
0.21334366 0.64320061 0.33920533 0.27323691 -0.88702827 -0.43648127
1453 1454 1455 1456 1457 1458
-0.36989911 0.57410507 0.41522326 0.34145268 -0.73268430 -0.31205497
1459 1460 1461 1462 1463 1464
0.49066136 -0.25003829 0.49743794 -0.58477674 0.68794503 0.73602901
1465 1466 1467 1468 1469 1470
0.20498605 0.49066136 0.49066136 -0.50933864 -0.66079467 0.80452289
1471 1472 1473 1474 1475 1476
-0.50256206 0.16310397 -0.21262439 0.20498605 -0.36989911 -0.29790295
1477 1478 1479 1480 1481 1482
-0.73268430 -0.15568724 0.15360152 0.26731570 0.63010089 -0.84639848
1483 1484 1485 1486 1487 1488
0.63010089 0.04176155 0.56351873 -0.50933864 -0.29790295 0.27323691
1489 1490 1491 1492 1493 1494
0.56351873 -0.66079467 0.33920533 -0.31205497 -0.50933864 0.26731570
1495 1496 1497 1498 1499 1500
-0.58254451 -0.36989911 -0.43648127 0.20498605 0.33920533 -0.58254451
1501 1502 1503 1504 1505 1506
0.26731570 -0.50933864 0.08189303 0.80452289 0.20498605 -0.66079467
1507 1508 1509 1510 1511 1512
-0.22544489 -0.50933864 -0.19547711 -0.50933864 -0.26397099 -0.43648127
1513 1514 1515 1516 1517 1518
0.63010089 -0.26397099 0.41745549 0.57410507 -0.58254451 0.49743794
1519 1520 1521 1522 1523 1524
-0.17269876 0.26731570 -0.43648127 0.34145268 0.49743794 0.20498605
1525 1526 1527 1528 1529 1530
-0.50933864 0.33920533 -0.25003829 0.33920533 -0.58254451 0.33920533
1531 1532 1533 1534 1535 1536
0.57410507 0.21334366 0.26731570 -0.43648127 -0.31205497 0.05871759
1537 1538 1539 1540 1541 1542
-0.26397099 0.49743794 0.33920533 0.41745549 0.15360152 -0.66079467
1543 1544 1545 1546 1547 1548
-0.25003829 -0.36989911 0.15360152 0.15360152 -0.31205497 0.33920533
1549 1550 1551 1552 1553 1554
-0.66079467 -0.17269876 0.73602901 -0.22544489 -0.29790295 0.63010089
1555 1556 1557 1558 1559 1560
-0.43648127 -0.31205497 0.41522326 0.77455511 0.34145268 0.64320061
1561 1562 1563 1564 1565 1566
-0.18425962 0.41522326 -0.94128241 0.41522326 0.34145268 0.33920533
1567 1568 1569 1570 1571 1572
0.49743794 -0.94128241 0.82730124 -0.43648127 0.20498605 0.34145268
1573 1574 1575 1576 1577 1578
0.26731570 -0.43648127 -0.36989911 0.41522326 -0.31205497 -0.58477674
1579 1580 1581 1582 1583 1584
0.41522326 -0.84639848 -0.25003829 -0.36989911 0.16310397 0.82730124
1585 1586 1587 1588 1589 1590
0.11297173 0.27323691 -0.58477674 -0.29790295 -0.19547711 0.26731570
1591 1592 1593 1594 1595 1596
0.26731570 -0.35679939 0.41522326 -0.26397099 -0.58477674 -0.50933864
1597 1598 1599 1600 1601 1602
-0.79501395 -0.26397099 0.20498605 -0.50933864 0.41522326 0.02952791
1603 1604 1605 1606 1607 1608
-0.16324961 -0.36989911 0.33920533 0.26731570 0.63010089 -0.36989911
1609 1610 1611 1612 1613 1614
-0.29790295 0.49066136 0.41745549 -0.36989911 0.26731570 0.74996171
1615 1616 1617 1618 1619 1620
0.11297173 -0.58254451 0.49066136 -0.26397099 -0.43648127 -0.19547711
1621 1622 1623 1624 1625 1626
-0.58477674 0.20498605 0.41745549 0.49066136 -0.35679939 0.78737561
1627 1628 1629 1630 1631 1632
0.33920533 0.26731570 0.11297173 -0.73268430 0.41745549 -0.42589493
1633 1634 1635 1636 1637 1638
0.49066136 -0.66079467 -0.26397099 -0.73268430 0.26731570 -0.73268430
1639 1640 1641 1642 1643 1644
-0.31205497 0.49066136 0.11297173 -0.66079467 0.57410507 0.33920533
1645 1646 1647 1648 1649 1650
-0.50933864 0.20498605 0.70209705 -0.73268430 -0.50256206 0.49743794
1651 1652 1653 1654 1655 1656
0.68794503 0.20498605 -0.31205497 -0.50933864 0.56351873 0.41745549
1657 1658 1659 1660 1661 1662
-0.26397099 0.56351873 -0.19547711 -0.79501395 -0.65854732 0.20498605
1663 1664 1665 1666 1667 1668
0.05871759 -0.50933864 0.70209705 0.49066136 0.41522326 0.15360152
1669 1670 1671 1672 1673 1674
-0.14314887 0.33920533 -0.31205497 0.68794503 -0.50256206 0.78737561
1675 1676 1677 1678 1679 1680
-0.42589493 -0.50256206 -0.73268430 -0.50933864 0.49066136 0.26731570
1681 1682 1683 1684 1685 1686
-0.42589493 -0.29790295 -0.58254451 0.26731570 0.49743794 -0.42589493
1687 1688 1689 1690 1691 1692
0.73602901 -0.26397099 0.77455511 0.27323691 -0.36989911 0.41745549
1693 1694 1695 1696 1697 1698
0.49743794 -0.25003829 0.34145268 -0.31205497 0.63010089 0.34145268
1699 1700 1701 1702 1703 1704
-0.43648127 0.56351873 -0.15568724 -0.14314887 0.41745549 -0.50256206
1705 1706 1707 1708 1709 1710
-0.36989911 -0.26397099 0.27323691 0.33920533 -0.91810697 -0.58254451
1711 1712 1713 1714 1715 1716
-0.19547711 0.64320061 -0.26397099 -0.58254451 -0.31205497 -0.43648127
1717 1718 1719 1720 1721 1722
0.05871759 -0.50256206 -0.22544489 -0.26397099 0.33920533 -0.22544489
1723 1724 1725 1726 1727 1728
-0.73268430 -0.36989911 0.49066136 0.34145268 0.41522326 -0.22544489
1729 1730 1731 1732 1733 1734
0.41522326 -0.19547711 -0.36989911 0.56351873 0.15360152 0.41745549
1735 1736 1737 1738 1739 1740
0.33920533 -0.50933864 -0.17269876 -0.65854732 -0.26397099 0.49066136
1741 1742 1743 1744 1745 1746
-0.58477674 -0.29790295 -0.43648127 0.02952791 0.49066136 -0.36989911
1747 1748 1749 1750 1751 1752
-0.26397099 -0.31205497 0.15360152 -0.17269876 -0.42589493 0.26731570
1753 1754 1755 1756 1757 1758
-0.26397099 0.04176155 0.68794503 0.34145268 -0.50256206 -0.26397099
1759 1760 1761 1762 1763 1764
-0.31205497 0.27323691 0.15360152 -0.25003829 -0.31205497 0.49743794
1765 1766 1767 1768 1769 1770
0.41522326 0.12255063 0.04176155 0.33920533 -0.50933864 0.15360152
1771 1772 1773 1774 1775 1776
-0.35679939 -0.29790295 0.20498605 0.41522326 -0.18425962 0.11297173
1777 1778 1779 1780 1781 1782
-0.66079467 0.56351873 -0.22544489 0.63010089 0.21334366 -0.29790295
1783 1784 1785 1786 1787 1788
0.73602901 0.49066136 -0.21262439 -0.17269876 -0.36989911 0.34145268
1789 1790 1791 1792 1793 1794
0.20498605 0.20498605 -0.42589493 0.41522326 -0.79501395 0.41522326
1795 1796 1797 1798 1799 1800
0.05871759 0.34145268 0.63010089 0.20498605 0.41522326 0.41745549
1801 1802 1803 1804 1805 1806
-0.31205497 -0.58477674 0.21334366 -0.31205497 -0.58254451 -0.58254451
1807 1808 1809 1810 1811 1812
0.64320061 0.49743794 -0.43648127 -0.58254451 -0.42589493 0.11297173
1813 1814 1815 1816 1817 1818
-0.31205497 0.49066136 -0.21262439 -0.36989911 0.15360152 -0.22544489
1819 1820 1821 1822 1823 1824
0.33920533 0.77455511 0.63010089 0.08189303 0.56351873 0.68794503
1825 1826 1827 1828 1829 1830
-0.22544489 -0.26397099 0.26731570 -0.78665634 0.82730124 -0.17269876
1831 1832 1833 1834 1835 1836
-0.58254451 0.49066136 0.33920533 -0.36989911 -0.22544489 -0.29790295
1837 1838 1839 1840 1841 1842
0.49066136 -0.35679939 -0.43648127 -0.50933864 -0.50256206 0.26731570
1843 1844 1845 1846 1847 1848
0.21334366 -0.36989911 0.49743794 0.21334366 0.33920533 -0.17269876
1849 1850 1851 1852 1853 1854
-0.66079467 -0.19547711 -0.50256206 -0.26397099 -0.26397099 0.33920533
1855 1856 1857 1858 1859 1860
-0.58477674 0.15360152 -0.22544489 -0.31205497 -0.58254451 0.56351873
1861 1862 1863 1864 1865 1866
-0.43648127 -0.35679939 -0.21262439 -0.22544489 0.68794503 0.20498605
1867 1868 1869 1870 1871 1872
0.20498605 0.56351873 -0.26397099 -0.58477674 -0.65854732 -0.58254451
1873 1874 1875 1876 1877 1878
0.15360152 -0.66079467 0.34145268 0.63010089 0.64320061 0.34145268
1879 1880 1881 1882 1883 1884
0.33920533 0.20498605 0.11297173 -0.58254451 -0.36989911 -0.29790295
1885 1886 1887 1888 1889 1890
0.27323691 -0.65854732 0.26731570 0.56351873 -0.65854732 0.57410507
1891 1892 1893 1894 1895 1896
-0.17269876 0.49743794 -0.19547711 0.49066136 0.57410507 -0.50256206
1897 1898 1899 1900 1901 1902
0.41745549 -0.58254451 0.15360152 -0.58254451 -0.43648127 -0.58477674
1903 1904 1905 1906 1907 1908
0.33920533 -0.31205497 0.26731570 -0.72676309 -0.29790295 0.26731570
1909 1910 1911 1912 1913 1914
0.26731570 -0.31205497 0.26731570 0.33920533 -0.31205497 0.15360152
1915 1916 1917 1918 1919 1920
0.63010089 -0.50256206 0.27323691 0.20498605 -0.31205497 0.26731570
1921 1922 1923 1924 1925 1926
-0.36989911 0.63010089 -0.19547711 -0.17269876 0.08189303 -0.26397099
1927 1928 1929 1930 1931 1932
0.41745549 0.64320061 0.34145268 0.57410507 -0.25003829 0.49066136
1933 1934 1935 1936 1937 1938
0.33920533 -0.26397099 -0.50256206 -0.50933864 0.15360152 0.73602901
1939 1940 1941 1942 1943 1944
-0.25003829 0.41745549 0.56351873 0.49066136 0.33920533 -0.22544489
1945 1946 1947 1948 1949 1950
-0.43648127 0.11297173 -0.58477674 0.26731570 0.26731570 -0.31205497
1951 1952 1953 1954 1955 1956
0.34145268 0.09081953 0.70209705 0.27323691 -0.50256206 -0.42589493
1957 1958 1959 1960 1961 1962
0.33920533 0.68794503 0.26731570 0.26731570 -0.35679939 0.15360152
1963 1964 1965 1966 1967 1968
-0.50933864 0.41522326 -0.58254451 0.64320061 0.34145268 0.27323691
1969 1970 1971 1972 1973 1974
-0.43648127 0.15360152 0.41522326 -0.58254451 0.41745549 -0.19547711
1975 1976 1977 1978 1979 1980
-0.58254451 0.26731570 -0.50256206 -0.42589493 -0.31205497 -0.15568724
1981 1982 1983 1984 1985 1986
-0.35679939 0.33920533 -0.50933864 0.64320061 -0.26397099 0.64320061
1987 1988 1989 1990 1991 1992
0.56351873 0.26731570 0.70209705 0.16310397 0.68794503 0.33920533
1993 1994 1995 1996 1997 1998
-0.29790295 0.26731570 0.16310397 -0.50933864 -0.43648127 0.26731570
1999 2000
0.26731570 0.27323691
Code
summary(predict(fit1))
Output
item match group prob
Item1 : 2000 Min. :-2.7699 0:20000 Min. :0.06246
Item2 : 2000 1st Qu.:-0.5161 1:20000 1st Qu.:0.43153
Item3 : 2000 Median : 0.1278 Median :0.59037
Item4 : 2000 Mean : 0.0000 Mean :0.57972
Item5 : 2000 3rd Qu.: 0.7718 3rd Qu.:0.74058
Item6 : 2000 Max. : 2.7036 Max. :0.98863
(Other):28000
Code
predict(fit1, item = 1)
Output
item match group prob
1 Item1 0.1278239 1 0.4364813
2 Item1 1.7376967 1 0.7866563
3 Item1 1.4157221 0 0.8870283
4 Item1 -1.1600743 1 0.2254449
5 Item1 0.7717730 0 0.7950139
6 Item1 -1.4820489 0 0.2979029
7 Item1 0.4497985 1 0.5093386
8 Item1 -0.8380998 0 0.4258949
9 Item1 0.7717730 0 0.7950139
10 Item1 1.7376967 1 0.7866563
11 Item1 1.0937476 0 0.8463985
12 Item1 1.4157221 1 0.7267631
13 Item1 0.1278239 1 0.4364813
14 Item1 -0.5161252 0 0.5025621
15 Item1 0.4497985 0 0.7326843
16 Item1 -1.1600743 0 0.3567994
17 Item1 -0.1941507 0 0.5825445
18 Item1 1.4157221 1 0.7267631
19 Item1 0.1278239 1 0.4364813
20 Item1 -0.5161252 1 0.3120550
21 Item1 -0.8380998 0 0.4258949
22 Item1 2.0596712 1 0.8368960
23 Item1 1.4157221 0 0.8870283
24 Item1 1.0937476 0 0.8463985
25 Item1 0.1278239 0 0.6607947
26 Item1 -0.5161252 0 0.5025621
27 Item1 1.7376967 1 0.7866563
28 Item1 -0.5161252 0 0.5025621
29 Item1 1.4157221 1 0.7267631
30 Item1 -0.8380998 1 0.2639710
31 Item1 -0.5161252 0 0.5025621
32 Item1 1.0937476 1 0.6585473
33 Item1 0.7717730 0 0.7950139
34 Item1 -0.5161252 1 0.3120550
35 Item1 0.1278239 1 0.4364813
36 Item1 0.4497985 1 0.5093386
37 Item1 0.1278239 0 0.6607947
38 Item1 1.0937476 0 0.8463985
39 Item1 -0.5161252 0 0.5025621
40 Item1 0.7717730 1 0.5847767
41 Item1 -0.8380998 1 0.2639710
42 Item1 -0.5161252 0 0.5025621
43 Item1 -0.5161252 0 0.5025621
44 Item1 0.7717730 0 0.7950139
45 Item1 0.7717730 0 0.7950139
46 Item1 0.1278239 1 0.4364813
47 Item1 -0.5161252 1 0.3120550
48 Item1 -0.5161252 0 0.5025621
49 Item1 -1.1600743 1 0.2254449
50 Item1 0.4497985 1 0.5093386
51 Item1 0.4497985 1 0.5093386
52 Item1 -0.8380998 1 0.2639710
53 Item1 -1.8040234 1 0.1726988
54 Item1 0.4497985 1 0.5093386
55 Item1 -0.5161252 1 0.3120550
56 Item1 -0.1941507 0 0.5825445
57 Item1 -0.5161252 1 0.3120550
58 Item1 0.4497985 1 0.5093386
59 Item1 0.4497985 1 0.5093386
60 Item1 0.4497985 1 0.5093386
61 Item1 2.7036203 1 0.9091805
62 Item1 0.1278239 0 0.6607947
63 Item1 -0.5161252 1 0.3120550
64 Item1 -1.1600743 1 0.2254449
65 Item1 1.0937476 1 0.6585473
66 Item1 0.4497985 0 0.7326843
67 Item1 -1.1600743 1 0.2254449
68 Item1 0.4497985 0 0.7326843
69 Item1 -2.4479725 1 0.1431489
70 Item1 2.0596712 0 0.9412824
71 Item1 -0.8380998 0 0.4258949
72 Item1 -1.1600743 1 0.2254449
73 Item1 0.1278239 1 0.4364813
74 Item1 -0.1941507 0 0.5825445
75 Item1 -0.5161252 0 0.5025621
76 Item1 -1.1600743 1 0.2254449
77 Item1 1.0937476 1 0.6585473
78 Item1 -1.8040234 0 0.2500383
79 Item1 -0.5161252 1 0.3120550
80 Item1 -0.5161252 0 0.5025621
81 Item1 0.4497985 1 0.5093386
82 Item1 -0.1941507 0 0.5825445
83 Item1 -1.1600743 1 0.2254449
84 Item1 -1.1600743 0 0.3567994
85 Item1 -0.5161252 0 0.5025621
86 Item1 1.0937476 1 0.6585473
87 Item1 -1.4820489 0 0.2979029
88 Item1 -1.1600743 0 0.3567994
89 Item1 0.1278239 1 0.4364813
90 Item1 0.4497985 0 0.7326843
91 Item1 -1.4820489 0 0.2979029
92 Item1 1.4157221 1 0.7267631
93 Item1 0.1278239 1 0.4364813
94 Item1 1.4157221 1 0.7267631
95 Item1 -0.8380998 1 0.2639710
96 Item1 -1.1600743 1 0.2254449
97 Item1 -0.1941507 1 0.3698991
98 Item1 1.0937476 0 0.8463985
99 Item1 -0.8380998 1 0.2639710
100 Item1 2.3816458 1 0.8774494
101 Item1 2.3816458 0 0.9582384
102 Item1 0.4497985 1 0.5093386
103 Item1 0.4497985 1 0.5093386
104 Item1 0.1278239 0 0.6607947
105 Item1 -0.1941507 1 0.3698991
106 Item1 -2.1259980 0 0.2126244
107 Item1 0.7717730 0 0.7950139
108 Item1 0.7717730 0 0.7950139
109 Item1 0.1278239 0 0.6607947
110 Item1 -1.8040234 1 0.1726988
111 Item1 1.0937476 1 0.6585473
112 Item1 0.1278239 1 0.4364813
113 Item1 -0.1941507 1 0.3698991
114 Item1 1.0937476 0 0.8463985
115 Item1 -0.5161252 1 0.3120550
116 Item1 -0.1941507 0 0.5825445
117 Item1 -0.5161252 1 0.3120550
118 Item1 1.4157221 0 0.8870283
119 Item1 -1.1600743 1 0.2254449
120 Item1 0.1278239 1 0.4364813
121 Item1 1.7376967 1 0.7866563
122 Item1 0.1278239 1 0.4364813
123 Item1 -0.8380998 1 0.2639710
124 Item1 1.0937476 0 0.8463985
125 Item1 -0.8380998 1 0.2639710
126 Item1 -0.5161252 0 0.5025621
127 Item1 -0.8380998 1 0.2639710
128 Item1 -1.8040234 0 0.2500383
129 Item1 -0.8380998 1 0.2639710
130 Item1 -1.4820489 0 0.2979029
131 Item1 0.1278239 1 0.4364813
132 Item1 -0.8380998 0 0.4258949
133 Item1 -0.8380998 0 0.4258949
134 Item1 1.0937476 0 0.8463985
135 Item1 -0.1941507 1 0.3698991
136 Item1 -0.1941507 1 0.3698991
137 Item1 -2.4479725 0 0.1842596
138 Item1 0.7717730 1 0.5847767
139 Item1 0.4497985 0 0.7326843
140 Item1 1.0937476 0 0.8463985
141 Item1 -0.1941507 1 0.3698991
142 Item1 -1.4820489 1 0.1954771
143 Item1 -1.4820489 0 0.2979029
144 Item1 0.7717730 0 0.7950139
145 Item1 -0.5161252 0 0.5025621
146 Item1 0.1278239 0 0.6607947
147 Item1 1.7376967 0 0.9181070
148 Item1 0.4497985 0 0.7326843
149 Item1 -1.1600743 1 0.2254449
150 Item1 -0.5161252 1 0.3120550
151 Item1 -0.8380998 1 0.2639710
152 Item1 -1.1600743 1 0.2254449
153 Item1 0.1278239 0 0.6607947
154 Item1 0.1278239 1 0.4364813
155 Item1 1.0937476 0 0.8463985
156 Item1 -1.1600743 0 0.3567994
157 Item1 -1.1600743 1 0.2254449
158 Item1 1.7376967 0 0.9181070
159 Item1 2.0596712 0 0.9412824
160 Item1 -0.1941507 1 0.3698991
161 Item1 -0.8380998 0 0.4258949
162 Item1 0.4497985 1 0.5093386
163 Item1 1.7376967 0 0.9181070
164 Item1 -1.8040234 1 0.1726988
165 Item1 -0.8380998 0 0.4258949
166 Item1 0.1278239 1 0.4364813
167 Item1 0.1278239 0 0.6607947
168 Item1 1.7376967 0 0.9181070
169 Item1 -0.8380998 1 0.2639710
170 Item1 0.7717730 0 0.7950139
171 Item1 -1.4820489 0 0.2979029
172 Item1 0.1278239 0 0.6607947
173 Item1 0.7717730 1 0.5847767
174 Item1 -0.1941507 0 0.5825445
175 Item1 0.4497985 0 0.7326843
176 Item1 -0.8380998 0 0.4258949
177 Item1 0.1278239 1 0.4364813
178 Item1 1.7376967 1 0.7866563
179 Item1 1.4157221 0 0.8870283
180 Item1 -1.4820489 1 0.1954771
181 Item1 -0.1941507 1 0.3698991
182 Item1 1.4157221 0 0.8870283
183 Item1 1.7376967 0 0.9181070
184 Item1 2.0596712 1 0.8368960
185 Item1 0.7717730 1 0.5847767
186 Item1 -0.5161252 0 0.5025621
187 Item1 1.4157221 0 0.8870283
188 Item1 -1.1600743 0 0.3567994
189 Item1 -2.1259980 1 0.1556872
190 Item1 0.4497985 0 0.7326843
191 Item1 -0.5161252 1 0.3120550
192 Item1 -0.5161252 0 0.5025621
193 Item1 0.4497985 0 0.7326843
194 Item1 -1.8040234 1 0.1726988
195 Item1 1.7376967 0 0.9181070
196 Item1 -0.1941507 0 0.5825445
197 Item1 -0.1941507 0 0.5825445
198 Item1 0.1278239 0 0.6607947
199 Item1 0.7717730 0 0.7950139
200 Item1 0.1278239 1 0.4364813
201 Item1 -0.5161252 1 0.3120550
202 Item1 -0.1941507 0 0.5825445
203 Item1 -1.8040234 0 0.2500383
204 Item1 0.7717730 0 0.7950139
205 Item1 1.4157221 0 0.8870283
206 Item1 0.4497985 0 0.7326843
207 Item1 0.4497985 0 0.7326843
208 Item1 0.1278239 1 0.4364813
209 Item1 0.7717730 0 0.7950139
210 Item1 -0.1941507 1 0.3698991
211 Item1 0.7717730 1 0.5847767
212 Item1 -0.1941507 1 0.3698991
213 Item1 -1.8040234 0 0.2500383
214 Item1 0.4497985 1 0.5093386
215 Item1 -1.4820489 1 0.1954771
216 Item1 -1.4820489 0 0.2979029
217 Item1 0.4497985 0 0.7326843
218 Item1 0.7717730 0 0.7950139
219 Item1 2.0596712 0 0.9412824
220 Item1 0.4497985 0 0.7326843
221 Item1 0.7717730 1 0.5847767
222 Item1 -0.1941507 1 0.3698991
223 Item1 -0.1941507 1 0.3698991
224 Item1 0.4497985 0 0.7326843
225 Item1 -0.1941507 1 0.3698991
226 Item1 -0.8380998 0 0.4258949
227 Item1 0.4497985 0 0.7326843
228 Item1 -0.5161252 0 0.5025621
229 Item1 -0.1941507 0 0.5825445
230 Item1 0.4497985 1 0.5093386
231 Item1 -1.1600743 0 0.3567994
232 Item1 1.7376967 0 0.9181070
233 Item1 1.4157221 0 0.8870283
234 Item1 -1.1600743 0 0.3567994
235 Item1 1.0937476 0 0.8463985
236 Item1 0.4497985 0 0.7326843
237 Item1 -0.1941507 0 0.5825445
238 Item1 2.0596712 0 0.9412824
239 Item1 -0.5161252 1 0.3120550
240 Item1 1.7376967 0 0.9181070
241 Item1 0.4497985 0 0.7326843
242 Item1 1.0937476 1 0.6585473
243 Item1 0.1278239 0 0.6607947
244 Item1 0.1278239 1 0.4364813
245 Item1 -0.5161252 0 0.5025621
246 Item1 -0.5161252 1 0.3120550
247 Item1 0.1278239 1 0.4364813
248 Item1 -0.1941507 0 0.5825445
249 Item1 0.7717730 1 0.5847767
250 Item1 -0.5161252 1 0.3120550
251 Item1 1.0937476 0 0.8463985
252 Item1 -0.5161252 0 0.5025621
253 Item1 0.7717730 0 0.7950139
254 Item1 0.1278239 0 0.6607947
255 Item1 0.7717730 1 0.5847767
256 Item1 0.7717730 1 0.5847767
257 Item1 0.1278239 0 0.6607947
258 Item1 0.1278239 1 0.4364813
259 Item1 1.4157221 1 0.7267631
260 Item1 0.7717730 0 0.7950139
261 Item1 -2.4479725 1 0.1431489
262 Item1 1.0937476 1 0.6585473
263 Item1 -1.1600743 0 0.3567994
264 Item1 -0.8380998 1 0.2639710
265 Item1 0.1278239 0 0.6607947
266 Item1 -0.1941507 0 0.5825445
267 Item1 1.0937476 0 0.8463985
268 Item1 0.1278239 1 0.4364813
269 Item1 1.7376967 1 0.7866563
270 Item1 -1.8040234 1 0.1726988
271 Item1 0.1278239 1 0.4364813
272 Item1 0.1278239 0 0.6607947
273 Item1 0.7717730 0 0.7950139
274 Item1 -1.1600743 0 0.3567994
275 Item1 1.7376967 0 0.9181070
276 Item1 -0.5161252 1 0.3120550
277 Item1 0.4497985 1 0.5093386
278 Item1 -1.4820489 1 0.1954771
279 Item1 -1.1600743 0 0.3567994
280 Item1 0.4497985 1 0.5093386
281 Item1 -0.8380998 1 0.2639710
282 Item1 0.4497985 0 0.7326843
283 Item1 -0.1941507 0 0.5825445
284 Item1 -0.8380998 0 0.4258949
285 Item1 0.7717730 1 0.5847767
286 Item1 1.7376967 1 0.7866563
287 Item1 -0.5161252 0 0.5025621
288 Item1 0.7717730 0 0.7950139
289 Item1 2.0596712 0 0.9412824
290 Item1 -0.8380998 0 0.4258949
291 Item1 -0.8380998 0 0.4258949
292 Item1 -0.8380998 1 0.2639710
293 Item1 -0.5161252 1 0.3120550
294 Item1 -0.1941507 0 0.5825445
295 Item1 0.1278239 1 0.4364813
296 Item1 1.0937476 0 0.8463985
297 Item1 0.7717730 1 0.5847767
298 Item1 -0.5161252 0 0.5025621
299 Item1 0.7717730 1 0.5847767
300 Item1 1.0937476 0 0.8463985
301 Item1 0.7717730 1 0.5847767
302 Item1 -0.1941507 1 0.3698991
303 Item1 -1.1600743 0 0.3567994
304 Item1 -1.8040234 0 0.2500383
305 Item1 0.1278239 0 0.6607947
306 Item1 1.7376967 0 0.9181070
307 Item1 0.7717730 0 0.7950139
308 Item1 1.7376967 0 0.9181070
309 Item1 -0.8380998 0 0.4258949
310 Item1 -0.1941507 1 0.3698991
311 Item1 0.1278239 0 0.6607947
312 Item1 -0.8380998 1 0.2639710
313 Item1 0.1278239 1 0.4364813
314 Item1 0.4497985 1 0.5093386
315 Item1 2.3816458 1 0.8774494
316 Item1 0.7717730 0 0.7950139
317 Item1 0.4497985 1 0.5093386
318 Item1 1.0937476 1 0.6585473
319 Item1 -0.1941507 0 0.5825445
320 Item1 -0.5161252 1 0.3120550
321 Item1 -0.8380998 1 0.2639710
322 Item1 0.4497985 1 0.5093386
323 Item1 0.4497985 0 0.7326843
324 Item1 -2.7699471 1 0.1339969
325 Item1 1.0937476 1 0.6585473
326 Item1 0.4497985 0 0.7326843
327 Item1 -1.1600743 0 0.3567994
328 Item1 0.7717730 0 0.7950139
329 Item1 0.4497985 0 0.7326843
330 Item1 -1.8040234 1 0.1726988
331 Item1 -0.5161252 1 0.3120550
332 Item1 -0.1941507 1 0.3698991
333 Item1 0.1278239 1 0.4364813
334 Item1 -0.8380998 1 0.2639710
335 Item1 1.4157221 0 0.8870283
336 Item1 -0.5161252 0 0.5025621
337 Item1 -0.1941507 0 0.5825445
338 Item1 0.1278239 1 0.4364813
339 Item1 0.1278239 0 0.6607947
340 Item1 -0.1941507 1 0.3698991
341 Item1 0.1278239 0 0.6607947
342 Item1 0.7717730 1 0.5847767
343 Item1 0.7717730 0 0.7950139
344 Item1 0.4497985 0 0.7326843
345 Item1 -0.5161252 0 0.5025621
346 Item1 -0.5161252 0 0.5025621
347 Item1 0.1278239 1 0.4364813
348 Item1 -0.8380998 0 0.4258949
349 Item1 1.0937476 0 0.8463985
350 Item1 -0.8380998 1 0.2639710
351 Item1 -1.4820489 1 0.1954771
352 Item1 1.0937476 0 0.8463985
353 Item1 -0.1941507 0 0.5825445
354 Item1 0.1278239 0 0.6607947
355 Item1 -1.4820489 0 0.2979029
356 Item1 1.0937476 0 0.8463985
357 Item1 0.4497985 1 0.5093386
358 Item1 -0.8380998 1 0.2639710
359 Item1 -1.4820489 0 0.2979029
360 Item1 -0.8380998 0 0.4258949
361 Item1 1.7376967 1 0.7866563
362 Item1 -0.8380998 1 0.2639710
363 Item1 0.4497985 1 0.5093386
364 Item1 -1.4820489 0 0.2979029
365 Item1 1.4157221 0 0.8870283
366 Item1 0.1278239 1 0.4364813
367 Item1 -1.1600743 0 0.3567994
368 Item1 -0.5161252 1 0.3120550
369 Item1 -0.8380998 1 0.2639710
370 Item1 0.7717730 1 0.5847767
371 Item1 -1.1600743 0 0.3567994
372 Item1 0.4497985 1 0.5093386
373 Item1 -1.1600743 1 0.2254449
374 Item1 -0.8380998 0 0.4258949
375 Item1 -0.1941507 0 0.5825445
376 Item1 -0.5161252 1 0.3120550
377 Item1 -1.8040234 1 0.1726988
378 Item1 0.4497985 0 0.7326843
379 Item1 -0.5161252 0 0.5025621
380 Item1 0.7717730 0 0.7950139
381 Item1 1.0937476 0 0.8463985
382 Item1 0.4497985 1 0.5093386
383 Item1 -0.8380998 0 0.4258949
384 Item1 -0.1941507 0 0.5825445
385 Item1 0.1278239 0 0.6607947
386 Item1 2.0596712 0 0.9412824
387 Item1 -0.5161252 0 0.5025621
388 Item1 2.7036203 0 0.9704721
389 Item1 -1.1600743 0 0.3567994
390 Item1 -2.1259980 1 0.1556872
391 Item1 -0.5161252 1 0.3120550
392 Item1 -0.1941507 1 0.3698991
393 Item1 1.4157221 1 0.7267631
394 Item1 0.4497985 1 0.5093386
395 Item1 -0.5161252 0 0.5025621
396 Item1 -0.1941507 1 0.3698991
397 Item1 -0.8380998 0 0.4258949
398 Item1 2.0596712 1 0.8368960
399 Item1 2.0596712 1 0.8368960
400 Item1 1.0937476 0 0.8463985
401 Item1 -1.8040234 0 0.2500383
402 Item1 -0.5161252 1 0.3120550
403 Item1 0.1278239 1 0.4364813
404 Item1 1.0937476 0 0.8463985
405 Item1 1.7376967 0 0.9181070
406 Item1 -0.1941507 1 0.3698991
407 Item1 0.4497985 1 0.5093386
408 Item1 0.7717730 1 0.5847767
409 Item1 0.1278239 1 0.4364813
410 Item1 2.0596712 1 0.8368960
411 Item1 1.0937476 1 0.6585473
412 Item1 0.1278239 1 0.4364813
413 Item1 0.7717730 0 0.7950139
414 Item1 -0.8380998 0 0.4258949
415 Item1 -0.1941507 0 0.5825445
416 Item1 -1.4820489 1 0.1954771
417 Item1 0.4497985 1 0.5093386
418 Item1 -1.4820489 0 0.2979029
419 Item1 -0.8380998 0 0.4258949
420 Item1 -2.1259980 1 0.1556872
421 Item1 -0.5161252 1 0.3120550
422 Item1 1.0937476 1 0.6585473
423 Item1 0.1278239 1 0.4364813
424 Item1 0.1278239 1 0.4364813
425 Item1 0.4497985 1 0.5093386
426 Item1 -1.1600743 0 0.3567994
427 Item1 1.0937476 1 0.6585473
428 Item1 1.0937476 1 0.6585473
429 Item1 0.4497985 1 0.5093386
430 Item1 -0.5161252 0 0.5025621
431 Item1 -0.1941507 1 0.3698991
432 Item1 0.1278239 1 0.4364813
433 Item1 -0.5161252 0 0.5025621
434 Item1 1.0937476 0 0.8463985
435 Item1 -0.5161252 1 0.3120550
436 Item1 1.7376967 1 0.7866563
437 Item1 0.7717730 0 0.7950139
438 Item1 -0.8380998 1 0.2639710
439 Item1 -1.8040234 0 0.2500383
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1858 Item1 -0.5161252 1 0.3120550
1859 Item1 -0.1941507 0 0.5825445
1860 Item1 0.1278239 1 0.4364813
1861 Item1 0.1278239 1 0.4364813
1862 Item1 -1.1600743 0 0.3567994
1863 Item1 -2.1259980 0 0.2126244
1864 Item1 -1.1600743 1 0.2254449
1865 Item1 -0.5161252 1 0.3120550
1866 Item1 0.7717730 0 0.7950139
1867 Item1 0.7717730 0 0.7950139
1868 Item1 0.1278239 1 0.4364813
1869 Item1 -0.8380998 1 0.2639710
1870 Item1 0.7717730 1 0.5847767
1871 Item1 1.0937476 1 0.6585473
1872 Item1 -0.1941507 0 0.5825445
1873 Item1 1.0937476 0 0.8463985
1874 Item1 0.1278239 0 0.6607947
1875 Item1 1.0937476 1 0.6585473
1876 Item1 -0.1941507 1 0.3698991
1877 Item1 -1.1600743 0 0.3567994
1878 Item1 1.0937476 1 0.6585473
1879 Item1 0.1278239 0 0.6607947
1880 Item1 0.7717730 0 0.7950139
1881 Item1 1.4157221 0 0.8870283
1882 Item1 -0.1941507 0 0.5825445
1883 Item1 -0.1941507 1 0.3698991
1884 Item1 -1.4820489 0 0.2979029
1885 Item1 1.4157221 1 0.7267631
1886 Item1 1.0937476 1 0.6585473
1887 Item1 0.4497985 0 0.7326843
1888 Item1 0.1278239 1 0.4364813
1889 Item1 1.0937476 1 0.6585473
1890 Item1 -0.8380998 0 0.4258949
1891 Item1 -1.8040234 1 0.1726988
1892 Item1 -0.5161252 0 0.5025621
1893 Item1 -1.4820489 1 0.1954771
1894 Item1 0.4497985 1 0.5093386
1895 Item1 -0.8380998 0 0.4258949
1896 Item1 -0.5161252 0 0.5025621
1897 Item1 -0.1941507 0 0.5825445
1898 Item1 -0.1941507 0 0.5825445
1899 Item1 1.0937476 0 0.8463985
1900 Item1 -0.1941507 0 0.5825445
1901 Item1 0.1278239 1 0.4364813
1902 Item1 0.7717730 1 0.5847767
1903 Item1 0.1278239 0 0.6607947
1904 Item1 -0.5161252 1 0.3120550
1905 Item1 0.4497985 0 0.7326843
1906 Item1 1.4157221 1 0.7267631
1907 Item1 -1.4820489 0 0.2979029
1908 Item1 0.4497985 0 0.7326843
1909 Item1 0.4497985 0 0.7326843
1910 Item1 -0.5161252 1 0.3120550
1911 Item1 0.4497985 0 0.7326843
1912 Item1 0.1278239 0 0.6607947
1913 Item1 -0.5161252 1 0.3120550
1914 Item1 1.0937476 0 0.8463985
1915 Item1 -0.1941507 1 0.3698991
1916 Item1 -0.5161252 0 0.5025621
1917 Item1 1.4157221 1 0.7267631
1918 Item1 0.7717730 0 0.7950139
1919 Item1 -0.5161252 1 0.3120550
1920 Item1 0.4497985 0 0.7326843
1921 Item1 -0.1941507 1 0.3698991
1922 Item1 -0.1941507 1 0.3698991
1923 Item1 -1.4820489 1 0.1954771
1924 Item1 -1.8040234 1 0.1726988
1925 Item1 1.7376967 0 0.9181070
1926 Item1 -0.8380998 1 0.2639710
1927 Item1 -0.1941507 0 0.5825445
1928 Item1 -1.1600743 0 0.3567994
1929 Item1 1.0937476 1 0.6585473
1930 Item1 -0.8380998 0 0.4258949
1931 Item1 -1.8040234 0 0.2500383
1932 Item1 0.4497985 1 0.5093386
1933 Item1 0.1278239 0 0.6607947
1934 Item1 -0.8380998 1 0.2639710
1935 Item1 -0.5161252 0 0.5025621
1936 Item1 0.4497985 1 0.5093386
1937 Item1 1.0937476 0 0.8463985
1938 Item1 -0.8380998 1 0.2639710
1939 Item1 -1.8040234 0 0.2500383
1940 Item1 -0.1941507 0 0.5825445
1941 Item1 0.1278239 1 0.4364813
1942 Item1 0.4497985 1 0.5093386
1943 Item1 0.1278239 0 0.6607947
1944 Item1 -1.1600743 1 0.2254449
1945 Item1 0.1278239 1 0.4364813
1946 Item1 1.4157221 0 0.8870283
1947 Item1 0.7717730 1 0.5847767
1948 Item1 0.4497985 0 0.7326843
1949 Item1 0.4497985 0 0.7326843
1950 Item1 -0.5161252 1 0.3120550
1951 Item1 1.0937476 1 0.6585473
1952 Item1 2.7036203 1 0.9091805
1953 Item1 -1.4820489 0 0.2979029
1954 Item1 1.4157221 1 0.7267631
1955 Item1 -0.5161252 0 0.5025621
1956 Item1 -0.8380998 0 0.4258949
1957 Item1 0.1278239 0 0.6607947
1958 Item1 -0.5161252 1 0.3120550
1959 Item1 0.4497985 0 0.7326843
1960 Item1 0.4497985 0 0.7326843
1961 Item1 -1.1600743 0 0.3567994
1962 Item1 1.0937476 0 0.8463985
1963 Item1 0.4497985 1 0.5093386
1964 Item1 0.7717730 1 0.5847767
1965 Item1 -0.1941507 0 0.5825445
1966 Item1 -1.1600743 0 0.3567994
1967 Item1 1.0937476 1 0.6585473
1968 Item1 1.4157221 1 0.7267631
1969 Item1 0.1278239 1 0.4364813
1970 Item1 1.0937476 0 0.8463985
1971 Item1 0.7717730 1 0.5847767
1972 Item1 -0.1941507 0 0.5825445
1973 Item1 -0.1941507 0 0.5825445
1974 Item1 -1.4820489 1 0.1954771
1975 Item1 -0.1941507 0 0.5825445
1976 Item1 0.4497985 0 0.7326843
1977 Item1 -0.5161252 0 0.5025621
1978 Item1 -0.8380998 0 0.4258949
1979 Item1 -0.5161252 1 0.3120550
1980 Item1 -2.1259980 1 0.1556872
1981 Item1 -1.1600743 0 0.3567994
1982 Item1 0.1278239 0 0.6607947
1983 Item1 0.4497985 1 0.5093386
1984 Item1 -1.1600743 0 0.3567994
1985 Item1 -0.8380998 1 0.2639710
1986 Item1 -1.1600743 0 0.3567994
1987 Item1 0.1278239 1 0.4364813
1988 Item1 0.4497985 0 0.7326843
1989 Item1 -1.4820489 0 0.2979029
1990 Item1 2.0596712 1 0.8368960
1991 Item1 -0.5161252 1 0.3120550
1992 Item1 0.1278239 0 0.6607947
1993 Item1 -1.4820489 0 0.2979029
1994 Item1 0.4497985 0 0.7326843
1995 Item1 2.0596712 1 0.8368960
1996 Item1 0.4497985 1 0.5093386
1997 Item1 0.1278239 1 0.4364813
1998 Item1 0.4497985 0 0.7326843
1999 Item1 0.4497985 0 0.7326843
2000 Item1 1.4157221 1 0.7267631
Code
predict(fit1, item = 1, match = 0, group = c(0, 1))
Output
item match group prob
1 Item1 0 0 0.6302562
2 Item1 0 1 0.4091277
Code
AIC(fit1)
Output
[1] 2515.030 2549.043 2342.008 2013.198 1670.650 2455.279 2518.547 2598.912
[9] 2693.296 2695.274 2371.996 2694.215 2221.522 2624.144 2737.446 2527.643
[17] 2738.945 2484.224 2607.867 2613.890
Code
BIC(fit1)
Output
[1] 2543.035 2577.048 2358.811 2030.000 1687.453 2472.082 2546.552 2615.714
[9] 2710.099 2712.076 2388.799 2711.018 2238.325 2640.947 2754.248 2544.446
[17] 2755.748 2501.027 2635.871 2630.693
Code
logLik(fit1)
Output
[1] -1252.515 -1269.522 -1168.004 -1003.599 -832.325 -1224.639 -1254.274
[8] -1296.456 -1343.648 -1344.637 -1182.998 -1344.108 -1107.761 -1309.072
[15] -1365.723 -1260.821 -1366.473 -1239.112 -1298.933 -1303.945
Code
AIC(fit1, item = 1)
Output
[1] 2515.03
Code
BIC(fit1, item = 1)
Output
[1] 2543.035
Code
logLik(fit1, item = 1)
Output
'log Lik.' -1252.515 (df=5)
Code
(fit2 <- difNLR(Data, group, focal.name = 1, model = "3PLcg", test = "W"))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression Wald test statistics
based on 3PL model with fixed guessing for groups
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 41.9145 0.0000 ***
Item2 14.7397 0.0006 ***
Item3 0.6903 0.7081
Item4 2.8622 0.2390
Item5 1.0890 0.5801
Item6 0.1547 0.9256
Item7 5.6898 0.0581 .
Item8 2.4127 0.2993
Item9 0.4219 0.8098
Item10 1.1699 0.5571
Item11 1.2022 0.5482
Item12 1.0143 0.6022
Item13 3.8549 0.1455
Item14 1.4149 0.4929
Item15 1.1084 0.5745
Item16 0.1394 0.9327
Item17 2.5649 0.2774
Item18 1.9413 0.3788
Item19 4.8067 0.0904 .
Item20 3.1155 0.2106
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 5.9915 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Code
(fit3 <- difNLR(Data, group, focal.name = 1, model = "3PLcg", test = "F"))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression F-test statistics
based on 3PL model with fixed guessing for groups
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
F-value P-value
Item1 41.7833 0.0000 ***
Item2 14.2267 0.0000 ***
Item3 0.3414 0.7108
Item4 1.6500 0.1923
Item5 0.5979 0.5501
Item6 0.0784 0.9246
Item7 4.1498 0.0159 *
Item8 1.4304 0.2395
Item9 0.2269 0.7970
Item10 0.6739 0.5098
Item11 0.6202 0.5379
Item12 0.5257 0.5912
Item13 2.2039 0.1106
Item14 0.7454 0.4747
Item15 0.6525 0.5208
Item16 0.0710 0.9314
Item17 1.5809 0.2060
Item18 1.0083 0.3650
Item19 3.1244 0.0442 *
Item20 1.7407 0.1757
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 3.0002 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Item7
Item19
Code
(fit4 <- difNLR(Data, group, focal.name = 1, model = "3PLcg", sandwich = TRUE))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 3PL model with fixed guessing for groups
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 82.0689 0.0000 ***
Item2 28.3232 0.0000 ***
Item3 0.6845 0.7102
Item4 3.3055 0.1915
Item5 1.1984 0.5492
Item6 0.1573 0.9244
Item7 8.3032 0.0157 *
Item8 2.8660 0.2386
Item9 0.4549 0.7966
Item10 1.3507 0.5090
Item11 1.2431 0.5371
Item12 1.0537 0.5905
Item13 4.4139 0.1100
Item14 1.4940 0.4738
Item15 1.3079 0.5200
Item16 0.1424 0.9313
Item17 3.1673 0.2052
Item18 2.0206 0.3641
Item19 6.2546 0.0438 *
Item20 3.4871 0.1749
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 5.9915 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Item7
Item19
Code
(fit5 <- difNLR(Data, group, focal.name = 1, model = "3PLcg", p.adjust.method = "BH")
)
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 3PL model with fixed guessing for groups
Parameters were estimated using non-linear least squares
Item purification was not applied
Multiple comparisons made with Benjamini-Hochberg adjustment of p-values
Chisq-value P-value Adj. P-value
Item1 82.0689 0.0000 0.0000 ***
Item2 28.3232 0.0000 0.0000 ***
Item3 0.6845 0.7102 0.8355
Item4 3.3055 0.1915 0.5131
Item5 1.1984 0.5492 0.7323
Item6 0.1573 0.9244 0.9313
Item7 8.3032 0.0157 0.1049
Item8 2.8660 0.2386 0.5302
Item9 0.4549 0.7966 0.8851
Item10 1.3507 0.5090 0.7323
Item11 1.2431 0.5371 0.7323
Item12 1.0537 0.5905 0.7381
Item13 4.4139 0.1100 0.4401
Item14 1.4940 0.4738 0.7323
Item15 1.3079 0.5200 0.7323
Item16 0.1424 0.9313 0.9313
Item17 3.1673 0.2052 0.5131
Item18 2.0206 0.3641 0.7282
Item19 6.2546 0.0438 0.2192
Item20 3.4871 0.1749 0.5131
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 5.9915 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Code
(fit6 <- difNLR(Data, group, focal.name = 1, model = "3PLcg", purify = TRUE))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 3PL model with fixed guessing for groups
Parameters were estimated using non-linear least squares
Item purification was applied with 2 iterations.
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 78.1279 0.0000 ***
Item2 27.7354 0.0000 ***
Item3 0.0027 0.9986
Item4 2.0028 0.3674
Item5 1.8701 0.3926
Item6 1.1317 0.5679
Item7 4.2269 0.1208
Item8 3.8019 0.1494
Item9 0.2162 0.8975
Item10 0.3917 0.8221
Item11 3.1631 0.2057
Item12 0.2259 0.8932
Item13 2.4394 0.2953
Item14 0.4483 0.7992
Item15 0.4197 0.8107
Item16 1.0382 0.5950
Item17 1.4373 0.4874
Item18 0.1553 0.9253
Item19 3.1764 0.2043
Item20 5.3659 0.0684 .
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 5.9915 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Code
(fit7 <- difNLR(Data, group, focal.name = 1, model = "3PLcg", match = "score"))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 3PL model with fixed guessing for groups
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 82.0689 0.0000 ***
Item2 28.3232 0.0000 ***
Item3 0.6845 0.7102
Item4 3.3055 0.1915
Item5 1.1984 0.5492
Item6 0.1573 0.9244
Item7 8.3032 0.0157 *
Item8 2.8660 0.2386
Item9 0.4549 0.7966
Item10 1.3507 0.5090
Item11 1.2431 0.5371
Item12 1.0537 0.5905
Item13 4.4139 0.1100
Item14 1.4940 0.4738
Item15 1.3079 0.5200
Item16 0.1424 0.9313
Item17 3.1673 0.2052
Item18 2.0206 0.3641
Item19 6.2546 0.0438 *
Item20 3.4871 0.1749
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 5.9915 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Item7
Item19
Code
(fit8 <- difNLR(Data, group, focal.name = 1, model = "4PLcgdg", type = "udif"))
Output
Detection of uniform differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 4PL model with fixed guessing and inattention parameter for groups
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 81.9549 0.0000 ***
Item2 14.5542 0.0001 ***
Item3 0.3153 0.5745
Item4 2.4851 0.1149
Item5 0.0007 0.9793
Item6 0.0716 0.7890
Item7 7.9741 0.0047 **
Item8 0.1516 0.6970
Item9 0.0948 0.7582
Item10 0.9293 0.3350
Item11 0.3832 0.5359
Item12 0.5839 0.4448
Item13 2.0124 0.1560
Item14 1.1316 0.2874
Item15 0.8492 0.3568
Item16 0.0204 0.8864
Item17 2.8934 0.0889 .
Item18 1.7608 0.1845
Item19 2.1632 0.1414
Item20 1.6239 0.2025
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 3.8415 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Item7
Code
(fit9 <- difNLR(Data, group, focal.name = 1, model = "2PL", type = "nudif"))
Output
Detection of non-uniform differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 2PL model
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 1.4694 0.2254
Item2 12.8761 0.0003 ***
Item3 0.2241 0.6359
Item4 0.8464 0.3576
Item5 1.7056 0.1916
Item6 0.0902 0.7639
Item7 0.3291 0.5662
Item8 2.7878 0.0950 .
Item9 0.3482 0.5551
Item10 0.4159 0.5190
Item11 0.8829 0.3474
Item12 1.1279 0.2882
Item13 4.2949 0.0382 *
Item14 0.4103 0.5218
Item15 0.4587 0.4982
Item16 0.2215 0.6379
Item17 0.1550 0.6938
Item18 0.1909 0.6622
Item19 3.6780 0.0551 .
Item20 1.0065 0.3157
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 3.8415 (significance level: 0.05)
Items detected as DIF items:
Item2
Item13
Code
(fit10 <- difNLR(Data, group, focal.name = 1, model = "4PL", constraints = "ac",
type = "b"))
Output
Detection of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 4PL model with constraints on parameters a, c
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 17.3612 0.0000 ***
Item2 17.5307 0.0000 ***
Item3 0.0337 0.8543
Item4 0.0239 0.8771
Item5 0.0007 0.9793
Item6 0.0507 0.8218
Item7 3.3095 0.0689 .
Item8 1.3556 0.2443
Item9 0.1936 0.6599
Item10 0.4760 0.4902
Item11 0.0073 0.9319
Item12 0.2910 0.5896
Item13 0.1490 0.6995
Item14 0.4523 0.5012
Item15 0.5203 0.4707
Item16 0.0204 0.8864
Item17 1.0202 0.3125
Item18 1.3394 0.2471
Item19 4.8954 0.0269 *
Item20 1.0550 0.3044
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 3.8415 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Item19
Code
(fit11 <- difNLR(Data, group, focal.name = 1, model = "3PLcg", method = "mle"))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 3PL model with fixed guessing for groups
Parameters were estimated using maximum likelihood method
with the L-BFGS-B algorithm
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 74.3235 0.0000 ***
Item2 27.1120 0.0000 ***
Item3 0.7401 0.6907
Item4 3.4097 0.1818
Item5 0.1188 0.9423
Item6 0.0989 0.9517
Item7 7.2097 0.0272 *
Item8 5.5471 0.0624 .
Item9 0.4107 0.8144
Item10 1.2497 0.5353
Item11 0.9490 0.6222
Item12 0.6327 0.7288
Item13 2.5885 0.2741
Item14 1.8876 0.3891
Item15 1.4200 0.4916
Item16 0.5303 0.7671
Item17 3.1490 0.2071
Item18 1.3752 0.5028
Item19 5.6279 0.0600 .
Item20 3.1076 0.2114
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 5.9915 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Item7
Code
(fit13 <- difNLR(Data, group, focal.name = 1, model = "3PLcg", method = "plf"))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 3PL model with fixed guessing for groups
Parameters were estimated using maximum likelihood method
with the PLF algorithm
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 73.5354 0.0000 ***
Item2 27.6368 0.0000 ***
Item3 0.7401 0.6907
Item4 3.4110 0.1817
Item5 0.6804 0.7116
Item6 0.1073 0.9478
Item7 7.2092 0.0272 *
Item8 4.9733 0.0832 .
Item9 0.4276 0.8075
Item10 1.3559 0.5076
Item11 1.0097 0.6036
Item12 1.0773 0.5835
Item13 3.2177 0.2001
Item14 2.3594 0.3074
Item15 1.4200 0.4916
Item16 0.8646 0.6490
Item17 3.1339 0.2087
Item18 1.3752 0.5028
Item19 5.0306 0.0808 .
Item20 3.7682 0.1520
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 5.9915 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Item7
Code
(fit14 <- difNLR(Data, group, focal.name = 1, model = "2PL", method = "irls"))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 2PL model
Parameters were estimated using maximum likelihood method
with the iteratively reweighted least squares algorithm
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 73.5146 0.0000 ***
Item2 27.5609 0.0000 ***
Item3 0.7401 0.6907
Item4 3.4110 0.1817
Item5 0.6860 0.7096
Item6 0.1072 0.9478
Item7 7.2092 0.0272 *
Item8 4.9704 0.0833 .
Item9 0.4278 0.8074
Item10 1.3567 0.5075
Item11 1.0111 0.6032
Item12 1.0835 0.5817
Item13 3.2220 0.1997
Item14 2.3760 0.3048
Item15 1.4200 0.4916
Item16 0.8831 0.6430
Item17 3.1335 0.2087
Item18 1.3752 0.5028
Item19 5.0045 0.0819 .
Item20 3.7580 0.1527
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 5.9915 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Item7
testing paper code - R Journal 2020 - generated data
Code
head(df[, c(1:5, 16)])
Output
Item1 Item2 Item3 Item4 Item5 group
1 0 1 1 1 1 0
2 0 1 1 0 1 0
3 0 1 0 0 1 0
4 1 1 1 0 1 0
5 1 1 0 1 1 0
6 0 1 0 0 1 0
Code
(fit1 <- difNLR(DataDIF, groupDIF, focal.name = 1, model = "4PL", type = "all"))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 4PL model
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 6.2044 0.1844
Item2 0.2802 0.9911
Item3 2.7038 0.6086
Item4 5.8271 0.2124
Item5 48.0052 0.0000 ***
Item6 7.2060 0.1254
Item7 3.2390 0.5187
Item8 16.8991 0.0020 **
Item9 2.1595 0.7064
Item10 4.6866 0.3210
Item11 69.5328 0.0000 ***
Item12 8.1931 0.0848 .
Item13 2.5850 0.6295
Item14 2.9478 0.5666
Item15 20.6589 0.0004 ***
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 9.4877 (significance level: 0.05)
Items detected as DIF items:
Item5
Item8
Item11
Item15
Code
round(coef(fit1, simplify = TRUE), 3)
Output
a b c d aDif bDif cDif dDif
Item1 estimate 1.484 1.294 0.049 1.000 0.000 0.000 0.000 0.000
Item1 CI2.5 0.730 0.726 -0.022 0.633 0.000 0.000 0.000 0.000
Item1 CI97.5 2.237 1.861 0.119 1.367 0.000 0.000 0.000 0.000
Item2 estimate 1.176 0.153 0.000 1.000 0.000 0.000 0.000 0.000
Item2 CI2.5 0.417 -0.279 -0.242 0.751 0.000 0.000 0.000 0.000
Item2 CI97.5 1.936 0.584 0.242 1.249 0.000 0.000 0.000 0.000
Item3 estimate 1.281 1.766 0.001 1.000 0.000 0.000 0.000 0.000
Item3 CI2.5 0.548 0.773 -0.063 0.381 0.000 0.000 0.000 0.000
Item3 CI97.5 2.014 2.758 0.065 1.619 0.000 0.000 0.000 0.000
Item4 estimate 1.450 0.421 0.000 1.000 0.000 0.000 0.000 0.000
Item4 CI2.5 0.785 0.122 -0.128 0.802 0.000 0.000 0.000 0.000
Item4 CI97.5 2.115 0.719 0.128 1.198 0.000 0.000 0.000 0.000
Item5 estimate 1.965 -1.147 0.000 0.868 -0.408 0.769 0.023 -0.006
Item5 CI2.5 0.310 -1.939 -0.602 0.780 -2.455 -0.177 -0.654 -0.188
Item5 CI97.5 3.619 -0.356 0.602 0.955 1.640 1.715 0.699 0.177
Item6 estimate 1.458 -0.527 0.000 0.954 0.000 0.000 0.000 0.000
Item6 CI2.5 0.670 -0.953 -0.288 0.837 0.000 0.000 0.000 0.000
Item6 CI97.5 2.246 -0.101 0.288 1.071 0.000 0.000 0.000 0.000
Item7 estimate 0.888 1.392 0.000 1.000 0.000 0.000 0.000 0.000
Item7 CI2.5 -0.076 -0.526 -0.209 0.059 0.000 0.000 0.000 0.000
Item7 CI97.5 1.852 3.311 0.209 1.941 0.000 0.000 0.000 0.000
Item8 estimate 1.162 1.407 0.000 0.866 -0.117 0.974 0.007 0.134
Item8 CI2.5 -0.004 -0.078 -0.139 0.109 -1.975 -3.428 -0.173 -2.270
Item8 CI97.5 2.329 2.892 0.139 1.622 1.741 5.375 0.186 2.539
Item9 estimate 1.482 -1.337 0.000 0.928 0.000 0.000 0.000 0.000
Item9 CI2.5 0.426 -2.385 -0.705 0.850 0.000 0.000 0.000 0.000
Item9 CI97.5 2.538 -0.290 0.705 1.005 0.000 0.000 0.000 0.000
Item10 estimate 1.375 -0.570 0.007 0.967 0.000 0.000 0.000 0.000
Item10 CI2.5 0.572 -1.070 -0.323 0.841 0.000 0.000 0.000 0.000
Item10 CI97.5 2.178 -0.069 0.338 1.093 0.000 0.000 0.000 0.000
Item11 estimate 1.071 -1.027 0.000 0.969 1.173 -0.499 0.000 0.011
Item11 CI2.5 -0.199 -2.862 -1.022 0.763 -0.948 -2.500 -1.293 -0.204
Item11 CI97.5 2.341 0.808 1.022 1.175 3.294 1.502 1.293 0.225
Item12 estimate 1.051 1.560 0.080 1.000 0.000 0.000 0.000 0.000
Item12 CI2.5 0.035 -0.162 -0.056 0.141 0.000 0.000 0.000 0.000
Item12 CI97.5 2.066 3.283 0.215 1.859 0.000 0.000 0.000 0.000
Item13 estimate 1.009 1.348 0.084 1.000 0.000 0.000 0.000 0.000
Item13 CI2.5 -0.013 -0.253 -0.084 0.217 0.000 0.000 0.000 0.000
Item13 CI97.5 2.030 2.949 0.253 1.783 0.000 0.000 0.000 0.000
Item14 estimate 1.093 1.659 0.141 1.000 0.000 0.000 0.000 0.000
Item14 CI2.5 -0.065 -0.300 0.016 0.064 0.000 0.000 0.000 0.000
Item14 CI97.5 2.252 3.618 0.266 1.936 0.000 0.000 0.000 0.000
Item15 estimate 0.875 -0.565 0.000 0.945 0.205 0.348 0.000 -0.142
Item15 CI2.5 -0.789 -2.871 -1.192 0.460 -2.042 -2.203 -1.312 -0.739
Item15 CI97.5 2.539 1.740 1.192 1.429 2.452 2.900 1.312 0.454
Code
round(coef(fit1, SE = TRUE)[[5]], 3)
Output
a b aDif bDif c cDif d dDif
estimate 1.965 -1.147 -0.408 0.769 0.000 0.023 0.868 -0.006
SE 0.844 0.404 1.045 0.483 0.307 0.345 0.044 0.093
CI2.5 0.310 -1.939 -2.455 -0.177 -0.602 -0.654 0.780 -0.188
CI97.5 3.619 -0.356 1.640 1.715 0.602 0.699 0.955 0.177
Code
(fit2 <- difNLR(DataDIF, groupDIF, focal.name = 1, model = model, type = "all"))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 3.7724 0.0521 .
Item2 0.2487 0.8831
Item3 2.0373 0.3611
Item4 5.8151 0.1210
Item5 46.2121 0.0000 ***
Item6 6.9990 0.0719 .
Item7 3.2390 0.3562
Item8 16.8991 0.0020 **
Item9 2.1595 0.7064
Item10 4.6866 0.3210
Item11 69.5328 0.0000 ***
Item12 8.1931 0.0848 .
Item13 2.5850 0.6295
Item14 2.9478 0.5666
Item15 20.6589 0.0004 ***
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DIF items:
Item5
Item8
Item11
Item15
Code
(fit3 <- difNLR(DataDIF, groupDIF, focal.name = 1, model = model, type = type))
Output
Detection of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 3.7724 0.0521 .
Item2 0.2487 0.8831
Item3 2.0373 0.3611
Item4 5.8151 0.1210
Item5 10.0923 0.0015 **
Item6 6.9990 0.0719 .
Item7 3.2390 0.3562
Item8 0.0681 0.7941
Item9 2.1595 0.7064
Item10 4.6866 0.3210
Item11 0.0000 1.0000
Item12 8.1931 0.0848 .
Item13 2.5850 0.6295
Item14 2.9478 0.5666
Item15 0.3552 0.5512
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DIF items:
Item5
Code
(fit4 <- difNLR(DataDIF, groupDIF, focal.name = 1, model = model, constraints = constraints,
type = type))
Output
Detection of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 3.7724 0.0521 .
Item2 0.2487 0.8831
Item3 2.0373 0.3611
Item4 5.8151 0.1210
Item5 46.1905 0.0000 ***
Item6 6.9990 0.0719 .
Item7 3.2390 0.3562
Item8 11.6006 0.0007 ***
Item9 2.1595 0.7064
Item10 4.6866 0.3210
Item11 35.2213 0.0000 ***
Item12 8.1931 0.0848 .
Item13 2.5850 0.6295
Item14 2.9478 0.5666
Item15 17.7716 0.0000 ***
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DIF items:
Item5
Item8
Item11
Item15
Code
(df <- data.frame(AIC = c(AIC(fit2), AIC(fit3), AIC(fit4)), BIC = c(BIC(fit2),
BIC(fit3), BIC(fit4)), Fit = paste("fit", rep(2:4, each = 15), sep = ""), Item = as.factor(
rep(1:15, 3))))
Output
AIC BIC Fit Item
1 903.5972 913.4127 fit2 1
2 1183.4245 1193.2400 fit2 2
3 557.5339 567.3495 fit2 3
4 1054.5443 1069.2676 fit2 4
5 1169.9188 1199.3653 fit2 5
6 1138.6881 1153.4114 fit2 6
7 1035.9053 1050.6286 fit2 7
8 641.3773 680.6393 fit2 8
9 1004.7217 1024.3527 fit2 9
10 1143.9437 1163.5747 fit2 10
11 860.0053 899.2674 fit2 11
12 1096.9640 1116.5950 fit2 12
13 1172.5120 1192.1430 fit2 13
14 1190.0824 1209.7134 fit2 14
15 1311.7767 1351.0387 fit2 15
16 903.5972 913.4127 fit3 1
17 1183.4245 1193.2400 fit3 2
18 557.5339 567.3495 fit3 3
19 1054.5443 1069.2676 fit3 4
20 1169.9188 1199.3653 fit3 5
21 1138.6881 1153.4114 fit3 6
22 1035.9053 1050.6286 fit3 7
23 639.4454 673.7997 fit3 8
24 1004.7217 1024.3527 fit3 9
25 1143.9437 1163.5747 fit3 10
26 858.0053 892.3596 fit3 11
27 1096.9640 1116.5950 fit3 12
28 1172.5120 1192.1430 fit3 13
29 1190.0824 1209.7134 fit3 14
30 1310.1319 1344.4861 fit3 15
31 903.5972 913.4127 fit4 1
32 1183.4245 1193.2400 fit4 2
33 557.5339 567.3495 fit4 3
34 1054.5443 1069.2676 fit4 4
35 1165.9404 1185.5715 fit4 5
36 1138.6881 1153.4114 fit4 6
37 1035.9053 1050.6286 fit4 7
38 640.6757 665.2145 fit4 8
39 1004.7217 1024.3527 fit4 9
40 1143.9437 1163.5747 fit4 10
41 888.3168 912.8556 fit4 11
42 1096.9640 1116.5950 fit4 12
43 1172.5120 1192.1430 fit4 13
44 1190.0824 1209.7134 fit4 14
45 1308.6640 1333.2028 fit4 15
Code
logLik(fit3, item = 8)
Output
'log Lik.' -312.7227 (df=7)
Code
logLik(fit4, item = 8)
Output
'log Lik.' -315.3379 (df=5)
Code
predict(fit1, item = 5, group = c(0, 1), match = 0)
Output
item match group prob
1 Item5 0 0 0.7851726
2 Item5 0 1 0.5624891
Code
fit9$difPur
Output
Item1 Item2 Item3 Item4 Item5 Item6
Step0 0 0 0 0 1 1
Step1 0 0 0 0 1 0
Step2 0 0 0 0 1 0
Code
fit14$difPur
Output
Item1 Item2 Item3 Item4 Item5 Item6 Item7 Item8 Item9 Item10 Item11
Step0 0 0 0 0 1 0 0 1 0 0 1
Step1 1 0 0 0 1 0 0 1 0 0 1
Step2 0 0 0 0 1 0 0 1 0 0 1
Step3 1 0 0 0 1 0 0 1 0 0 1
Step4 0 0 0 0 1 0 0 1 0 0 1
Step5 1 0 0 0 1 0 0 1 0 0 1
Step6 0 0 0 0 1 0 0 1 0 0 1
Step7 1 0 0 0 1 0 0 1 0 0 1
Step8 0 0 0 0 1 0 0 1 0 0 1
Step9 1 0 0 0 1 0 0 1 0 0 1
Step10 0 0 0 0 1 0 0 1 0 0 1
Item12
Step0 0
Step1 0
Step2 0
Step3 0
Step4 0
Step5 0
Step6 0
Step7 0
Step8 0
Step9 0
Step10 0
Code
predict(fitex3, match = rep(c(-1, 0, 1), 2), group = rep(c(0, 1), each = 3),
item = 1, interval = "confidence")
Output
item match group prob lwr.conf upr.conf
1 Item6A_9 -1 0 0.6785773 0.6188773 0.7382773
2 Item6A_9 0 0 0.7773050 0.7269066 0.8277034
3 Item6A_9 1 0 0.8781427 0.8114574 0.9448281
4 Item6A_9 -1 1 0.7802954 0.7186997 0.8418912
5 Item6A_9 0 1 0.8431037 0.7869870 0.8992204
6 Item6A_9 1 1 0.9290799 0.8549497 1.0032100
testing paper code - R Journal 2020 - special cases (not included)
Code
head(df[, c(1:5, 16)])
Output
Item1 Item2 Item3 Item4 Item5 group
1 0 1 1 1 1 0
2 0 1 1 0 1 0
3 0 1 0 0 1 0
4 1 1 1 0 1 0
5 1 1 0 1 1 0
6 0 1 0 0 1 0
Code
coef(fit12b, item = 14)
Output
$Item14
a b c d
estimate 0.9294854 1.329411 0.06799893 1
CI2.5 NA NA NA NA
CI97.5 NA NA NA NA
Code
fit12c
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression Wald test statistics
based on 4PL model
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 43.3878 0.0000 ***
Item2 155.8403 0.0000 ***
Item3 153.4009 0.0000 ***
Item4 111.2974 0.0000 ***
Item5 533.3646 0.0000 ***
Item6 107.9470 0.0000 ***
Item7 11.5948 0.0206 *
Item8 53.3736 0.0000 ***
Item9 453.2297 0.0000 ***
Item10 145.8386 0.0000 ***
Item11 910.1895 0.0000 ***
Item12 68.7672 0.0000 ***
Item13 28.3833 0.0000 ***
Item14 NA NA
Item15 96.6385 0.0000 ***
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 9.4877 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Item3
Item4
Item5
Item6
Item7
Item8
Item9
Item10
Item11
Item12
Item13
Item15
Code
fit12d
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 4PL model
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 2.0935 0.7186
Item2 0.7896 0.9398
Item3 17.6515 0.0014 **
Item4 4.8403 0.3041
Item5 13.1573 0.0105 *
Item6 3.8714 0.4237
Item7 1.5778 0.8128
Item8 10.9093 0.0276 *
Item9 0.6097 0.9620
Item10 2.4853 0.6473
Item11 26.6071 0.0000 ***
Item12 5.2838 0.2594
Item13 2.8016 0.5916
Item14 NA NA
Item15 12.8104 0.0122 *
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 9.4877 (significance level: 0.05)
Items detected as DIF items:
Item3
Item5
Item8
Item11
Item15
Code
(fit15a <- difNLR(DataDIF[, -c(5, 8, 11, 15)], groupDIF, focal.name = 1, model = "4PL",
type = "all"))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 4PL model
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 9.1344 0.0578 .
Item2 1.1944 0.8790
Item3 4.5018 0.3423
Item4 2.9407 0.5678
Item6 6.2007 0.1847
Item7 0.8918 0.9257
Item9 1.2215 0.8746
Item10 1.6226 0.8047
Item12 7.2585 0.1228
Item13 5.6248 0.2290
Item14 1.5104 0.8248
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 9.4877 (significance level: 0.05)
None of items is detected as DIF
Code
(fit15b <- difNLR(DataDIF[, -c(5, 8, 11, 15)], groupDIF, focal.name = 1, model = "4PL",
type = "all", purify = TRUE))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 4PL model
Parameters were estimated using non-linear least squares
Item purification was applied with 0 iteration.
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 9.1344 0.0578 .
Item2 1.1944 0.8790
Item3 4.5018 0.3423
Item4 2.9407 0.5678
Item6 6.2007 0.1847
Item7 0.8918 0.9257
Item9 1.2215 0.8746
Item10 1.6226 0.8047
Item12 7.2585 0.1228
Item13 5.6248 0.2290
Item14 1.5104 0.8248
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 9.4877 (significance level: 0.05)
None of items is detected as DIF
drabinova/difNLR documentation built on June 12, 2025, 4:47 a.m.
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difNLR - examples at help page
Code
(fit1 <- difNLR(Data, group, focal.name = 1, model = "3PLcg"))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 3PL model with fixed guessing for groups
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 82.0689 0.0000 ***
Item2 28.3232 0.0000 ***
Item3 0.6845 0.7102
Item4 3.3055 0.1915
Item5 1.1984 0.5492
Item6 0.1573 0.9244
Item7 8.3032 0.0157 *
Item8 2.8660 0.2386
Item9 0.4549 0.7966
Item10 1.3507 0.5090
Item11 1.2431 0.5371
Item12 1.0537 0.5905
Item13 4.4139 0.1100
Item14 1.4940 0.4738
Item15 1.3079 0.5200
Item16 0.1424 0.9313
Item17 3.1673 0.2052
Item18 2.0206 0.3641
Item19 6.2546 0.0438 *
Item20 3.4871 0.1749
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 5.9915 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Item7
Item19
Code
summary(fitted(fit1))
Output
Item1 Item2 Item3 Item4
Min. :0.1340 Min. :0.1040 Min. :0.2988 Min. :0.4093
1st Qu.:0.3568 1st Qu.:0.4272 1st Qu.:0.6607 1st Qu.:0.7422
Median :0.5093 Median :0.5844 Median :0.7504 Median :0.8122
Mean :0.5251 Mean :0.5702 Mean :0.7157 Mean :0.7820
3rd Qu.:0.7268 3rd Qu.:0.7315 3rd Qu.:0.8227 3rd Qu.:0.8666
Max. :0.9705 Max. :0.9679 Max. :0.9447 Max. :0.9566
Item5 Item6 Item7 Item8
Min. :0.4258 Min. :0.1755 Min. :0.1924 Min. :0.2732
1st Qu.:0.7634 1st Qu.:0.5495 1st Qu.:0.5468 1st Qu.:0.5343
Median :0.8560 Median :0.6819 Median :0.6648 Median :0.6410
Mean :0.8122 Mean :0.6386 Mean :0.6500 Mean :0.6166
3rd Qu.:0.9192 3rd Qu.:0.7920 3rd Qu.:0.7531 3rd Qu.:0.7416
Max. :0.9886 Max. :0.9564 Max. :0.9299 Max. :0.9293
Item9 Item10 Item11 Item12
Min. :0.2212 Min. :0.2253 Min. :0.1829 Min. :0.2533
1st Qu.:0.5057 1st Qu.:0.5002 1st Qu.:0.5979 1st Qu.:0.4533
Median :0.6025 Median :0.5979 Median :0.7208 Median :0.5597
Mean :0.5786 Mean :0.5750 Mean :0.6741 Mean :0.5474
3rd Qu.:0.6932 3rd Qu.:0.6904 3rd Qu.:0.8179 3rd Qu.:0.6731
Max. :0.8841 Max. :0.8860 Max. :0.9596 Max. :0.9125
Item13 Item14 Item15 Item16
Min. :0.4102 Min. :0.1199 Min. :0.1741 Min. :0.1039
1st Qu.:0.6842 1st Qu.:0.3349 1st Qu.:0.4474 1st Qu.:0.3140
Median :0.7715 Median :0.4531 Median :0.5432 Median :0.4549
Mean :0.7421 Mean :0.4432 Mean :0.5224 Mean :0.4461
3rd Qu.:0.8443 3rd Qu.:0.5834 3rd Qu.:0.6359 3rd Qu.:0.6140
Max. :0.9610 Max. :0.8798 Max. :0.8470 Max. :0.9246
Item17 Item18 Item19 Item20
Min. :0.1824 Min. :0.06246 Min. :0.09561 Min. :0.2103
1st Qu.:0.3717 1st Qu.:0.27960 1st Qu.:0.33303 1st Qu.:0.2986
Median :0.4599 Median :0.41775 Median :0.48166 Median :0.3893
Mean :0.4519 Mean :0.40978 Mean :0.48171 Mean :0.4117
3rd Qu.:0.5565 3rd Qu.:0.57444 3rd Qu.:0.63424 3rd Qu.:0.5271
Max. :0.8139 Max. :0.90358 Max. :0.93207 Max. :0.9096
Code
fitted(fit1, item = 1)
Output
1 2 3 4 5 6 7 8
0.4364813 0.7866563 0.8870283 0.2254449 0.7950139 0.2979029 0.5093386 0.4258949
9 10 11 12 13 14 15 16
0.7950139 0.7866563 0.8463985 0.7267631 0.4364813 0.5025621 0.7326843 0.3567994
17 18 19 20 21 22 23 24
0.5825445 0.7267631 0.4364813 0.3120550 0.4258949 0.8368960 0.8870283 0.8463985
25 26 27 28 29 30 31 32
0.6607947 0.5025621 0.7866563 0.5025621 0.7267631 0.2639710 0.5025621 0.6585473
33 34 35 36 37 38 39 40
0.7950139 0.3120550 0.4364813 0.5093386 0.6607947 0.8463985 0.5025621 0.5847767
41 42 43 44 45 46 47 48
0.2639710 0.5025621 0.5025621 0.7950139 0.7950139 0.4364813 0.3120550 0.5025621
49 50 51 52 53 54 55 56
0.2254449 0.5093386 0.5093386 0.2639710 0.1726988 0.5093386 0.3120550 0.5825445
57 58 59 60 61 62 63 64
0.3120550 0.5093386 0.5093386 0.5093386 0.9091805 0.6607947 0.3120550 0.2254449
65 66 67 68 69 70 71 72
0.6585473 0.7326843 0.2254449 0.7326843 0.1431489 0.9412824 0.4258949 0.2254449
73 74 75 76 77 78 79 80
0.4364813 0.5825445 0.5025621 0.2254449 0.6585473 0.2500383 0.3120550 0.5025621
81 82 83 84 85 86 87 88
0.5093386 0.5825445 0.2254449 0.3567994 0.5025621 0.6585473 0.2979029 0.3567994
89 90 91 92 93 94 95 96
0.4364813 0.7326843 0.2979029 0.7267631 0.4364813 0.7267631 0.2639710 0.2254449
97 98 99 100 101 102 103 104
0.3698991 0.8463985 0.2639710 0.8774494 0.9582384 0.5093386 0.5093386 0.6607947
105 106 107 108 109 110 111 112
0.3698991 0.2126244 0.7950139 0.7950139 0.6607947 0.1726988 0.6585473 0.4364813
113 114 115 116 117 118 119 120
0.3698991 0.8463985 0.3120550 0.5825445 0.3120550 0.8870283 0.2254449 0.4364813
121 122 123 124 125 126 127 128
0.7866563 0.4364813 0.2639710 0.8463985 0.2639710 0.5025621 0.2639710 0.2500383
129 130 131 132 133 134 135 136
0.2639710 0.2979029 0.4364813 0.4258949 0.4258949 0.8463985 0.3698991 0.3698991
137 138 139 140 141 142 143 144
0.1842596 0.5847767 0.7326843 0.8463985 0.3698991 0.1954771 0.2979029 0.7950139
145 146 147 148 149 150 151 152
0.5025621 0.6607947 0.9181070 0.7326843 0.2254449 0.3120550 0.2639710 0.2254449
153 154 155 156 157 158 159 160
0.6607947 0.4364813 0.8463985 0.3567994 0.2254449 0.9181070 0.9412824 0.3698991
161 162 163 164 165 166 167 168
0.4258949 0.5093386 0.9181070 0.1726988 0.4258949 0.4364813 0.6607947 0.9181070
169 170 171 172 173 174 175 176
0.2639710 0.7950139 0.2979029 0.6607947 0.5847767 0.5825445 0.7326843 0.4258949
177 178 179 180 181 182 183 184
0.4364813 0.7866563 0.8870283 0.1954771 0.3698991 0.8870283 0.9181070 0.8368960
185 186 187 188 189 190 191 192
0.5847767 0.5025621 0.8870283 0.3567994 0.1556872 0.7326843 0.3120550 0.5025621
193 194 195 196 197 198 199 200
0.7326843 0.1726988 0.9181070 0.5825445 0.5825445 0.6607947 0.7950139 0.4364813
201 202 203 204 205 206 207 208
0.3120550 0.5825445 0.2500383 0.7950139 0.8870283 0.7326843 0.7326843 0.4364813
209 210 211 212 213 214 215 216
0.7950139 0.3698991 0.5847767 0.3698991 0.2500383 0.5093386 0.1954771 0.2979029
217 218 219 220 221 222 223 224
0.7326843 0.7950139 0.9412824 0.7326843 0.5847767 0.3698991 0.3698991 0.7326843
225 226 227 228 229 230 231 232
0.3698991 0.4258949 0.7326843 0.5025621 0.5825445 0.5093386 0.3567994 0.9181070
233 234 235 236 237 238 239 240
0.8870283 0.3567994 0.8463985 0.7326843 0.5825445 0.9412824 0.3120550 0.9181070
241 242 243 244 245 246 247 248
0.7326843 0.6585473 0.6607947 0.4364813 0.5025621 0.3120550 0.4364813 0.5825445
249 250 251 252 253 254 255 256
0.5847767 0.3120550 0.8463985 0.5025621 0.7950139 0.6607947 0.5847767 0.5847767
257 258 259 260 261 262 263 264
0.6607947 0.4364813 0.7267631 0.7950139 0.1431489 0.6585473 0.3567994 0.2639710
265 266 267 268 269 270 271 272
0.6607947 0.5825445 0.8463985 0.4364813 0.7866563 0.1726988 0.4364813 0.6607947
273 274 275 276 277 278 279 280
0.7950139 0.3567994 0.9181070 0.3120550 0.5093386 0.1954771 0.3567994 0.5093386
281 282 283 284 285 286 287 288
0.2639710 0.7326843 0.5825445 0.4258949 0.5847767 0.7866563 0.5025621 0.7950139
289 290 291 292 293 294 295 296
0.9412824 0.4258949 0.4258949 0.2639710 0.3120550 0.5825445 0.4364813 0.8463985
297 298 299 300 301 302 303 304
0.5847767 0.5025621 0.5847767 0.8463985 0.5847767 0.3698991 0.3567994 0.2500383
305 306 307 308 309 310 311 312
0.6607947 0.9181070 0.7950139 0.9181070 0.4258949 0.3698991 0.6607947 0.2639710
313 314 315 316 317 318 319 320
0.4364813 0.5093386 0.8774494 0.7950139 0.5093386 0.6585473 0.5825445 0.3120550
321 322 323 324 325 326 327 328
0.2639710 0.5093386 0.7326843 0.1339969 0.6585473 0.7326843 0.3567994 0.7950139
329 330 331 332 333 334 335 336
0.7326843 0.1726988 0.3120550 0.3698991 0.4364813 0.2639710 0.8870283 0.5025621
337 338 339 340 341 342 343 344
0.5825445 0.4364813 0.6607947 0.3698991 0.6607947 0.5847767 0.7950139 0.7326843
345 346 347 348 349 350 351 352
0.5025621 0.5025621 0.4364813 0.4258949 0.8463985 0.2639710 0.1954771 0.8463985
353 354 355 356 357 358 359 360
0.5825445 0.6607947 0.2979029 0.8463985 0.5093386 0.2639710 0.2979029 0.4258949
361 362 363 364 365 366 367 368
0.7866563 0.2639710 0.5093386 0.2979029 0.8870283 0.4364813 0.3567994 0.3120550
369 370 371 372 373 374 375 376
0.2639710 0.5847767 0.3567994 0.5093386 0.2254449 0.4258949 0.5825445 0.3120550
377 378 379 380 381 382 383 384
0.1726988 0.7326843 0.5025621 0.7950139 0.8463985 0.5093386 0.4258949 0.5825445
385 386 387 388 389 390 391 392
0.6607947 0.9412824 0.5025621 0.9704721 0.3567994 0.1556872 0.3120550 0.3698991
393 394 395 396 397 398 399 400
0.7267631 0.5093386 0.5025621 0.3698991 0.4258949 0.8368960 0.8368960 0.8463985
401 402 403 404 405 406 407 408
0.2500383 0.3120550 0.4364813 0.8463985 0.9181070 0.3698991 0.5093386 0.5847767
409 410 411 412 413 414 415 416
0.4364813 0.8368960 0.6585473 0.4364813 0.7950139 0.4258949 0.5825445 0.1954771
417 418 419 420 421 422 423 424
0.5093386 0.2979029 0.4258949 0.1556872 0.3120550 0.6585473 0.4364813 0.4364813
425 426 427 428 429 430 431 432
0.5093386 0.3567994 0.6585473 0.6585473 0.5093386 0.5025621 0.3698991 0.4364813
433 434 435 436 437 438 439 440
0.5025621 0.8463985 0.3120550 0.7866563 0.7950139 0.2639710 0.2500383 0.5093386
441 442 443 444 445 446 447 448
0.5847767 0.5093386 0.7326843 0.2254449 0.3120550 0.7950139 0.6585473 0.6585473
449 450 451 452 453 454 455 456
0.1954771 0.3120550 0.5847767 0.1726988 0.8368960 0.5093386 0.5025621 0.7267631
457 458 459 460 461 462 463 464
0.5093386 0.5847767 0.3120550 0.7326843 0.5093386 0.5847767 0.7267631 0.7950139
465 466 467 468 469 470 471 472
0.2126244 0.2639710 0.5093386 0.7326843 0.5847767 0.6585473 0.5825445 0.6607947
473 474 475 476 477 478 479 480
0.3120550 0.5025621 0.7950139 0.7326843 0.3567994 0.5093386 0.5093386 0.7326843
481 482 483 484 485 486 487 488
0.8463985 0.5847767 0.7950139 0.7326843 0.5025621 0.3120550 0.7950139 0.6607947
489 490 491 492 493 494 495 496
0.4364813 0.6585473 0.7326843 0.2254449 0.2254449 0.3120550 0.5025621 0.1954771
497 498 499 500 501 502 503 504
0.2979029 0.5847767 0.5025621 0.7866563 0.8463985 0.2500383 0.3698991 0.3120550
505 506 507 508 509 510 511 512
0.5025621 0.4258949 0.3120550 0.4364813 0.3120550 0.3120550 0.3120550 0.1842596
513 514 515 516 517 518 519 520
0.2126244 0.6607947 0.7267631 0.2979029 0.3698991 0.4258949 0.2639710 0.5093386
521 522 523 524 525 526 527 528
0.5825445 0.5093386 0.9091805 0.7866563 0.5093386 0.6585473 0.8870283 0.5847767
529 530 531 532 533 534 535 536
0.2639710 0.8463985 0.8463985 0.5847767 0.3698991 0.4258949 0.3120550 0.6607947
537 538 539 540 541 542 543 544
0.6607947 0.7326843 0.5025621 0.5093386 0.2639710 0.3698991 0.6585473 0.3567994
545 546 547 548 549 550 551 552
0.5025621 0.7326843 0.5093386 0.4364813 0.3698991 0.2254449 0.4364813 0.2254449
553 554 555 556 557 558 559 560
0.7326843 0.4364813 0.3567994 0.4364813 0.6585473 0.2639710 0.6607947 0.5093386
561 562 563 564 565 566 567 568
0.8463985 0.2500383 0.5025621 0.7950139 0.4364813 0.5847767 0.2639710 0.7326843
569 570 571 572 573 574 575 576
0.9181070 0.4258949 0.5025621 0.7267631 0.3698991 0.4364813 0.5025621 0.5093386
577 578 579 580 581 582 583 584
0.3698991 0.7326843 0.1726988 0.3698991 0.4258949 0.1954771 0.5025621 0.5025621
585 586 587 588 589 590 591 592
0.5093386 0.2639710 0.4364813 0.1556872 0.7950139 0.4364813 0.8870283 0.2126244
593 594 595 596 597 598 599 600
0.3567994 0.5825445 0.5025621 0.5025621 0.4364813 0.2254449 0.5847767 0.6607947
601 602 603 604 605 606 607 608
0.2639710 0.2500383 0.3698991 0.2639710 0.3120550 0.7326843 0.8463985 0.7326843
609 610 611 612 613 614 615 616
0.7866563 0.5847767 0.6607947 0.5847767 0.5825445 0.7866563 0.4364813 0.1556872
617 618 619 620 621 622 623 624
0.5025621 0.1556872 0.7326843 0.2979029 0.4364813 0.1954771 0.4364813 0.4364813
625 626 627 628 629 630 631 632
0.3698991 0.1954771 0.7950139 0.4258949 0.8870283 0.7326843 0.2254449 0.6607947
633 634 635 636 637 638 639 640
0.5825445 0.7950139 0.7326843 0.8463985 0.6607947 0.7326843 0.5825445 0.8870283
641 642 643 644 645 646 647 648
0.4258949 0.5825445 0.5825445 0.5847767 0.2254449 0.3567994 0.3120550 0.3567994
649 650 651 652 653 654 655 656
0.4258949 0.5025621 0.8774494 0.4258949 0.7326843 0.7326843 0.6607947 0.4364813
657 658 659 660 661 662 663 664
0.7950139 0.7326843 0.1954771 0.7326843 0.3120550 0.7950139 0.2639710 0.3120550
665 666 667 668 669 670 671 672
0.3120550 0.9704721 0.5825445 0.6607947 0.2979029 0.2254449 0.3698991 0.5847767
673 674 675 676 677 678 679 680
0.4364813 0.3120550 0.7326843 0.5847767 0.1954771 0.4364813 0.3120550 0.4364813
681 682 683 684 685 686 687 688
0.1726988 0.4258949 0.3120550 0.1556872 0.6607947 0.8870283 0.4364813 0.8368960
689 690 691 692 693 694 695 696
0.3120550 0.3698991 0.8463985 0.5093386 0.5025621 0.5093386 0.2500383 0.7950139
697 698 699 700 701 702 703 704
0.7950139 0.5025621 0.2639710 0.7950139 0.2500383 0.2639710 0.9181070 0.2639710
705 706 707 708 709 710 711 712
0.3698991 0.2979029 0.7950139 0.4258949 0.4258949 0.3120550 0.1556872 0.8870283
713 714 715 716 717 718 719 720
0.7950139 0.5825445 0.5093386 0.5093386 0.5825445 0.1954771 0.3120550 0.5025621
721 722 723 724 725 726 727 728
0.5093386 0.4258949 0.5025621 0.5825445 0.8463985 0.7267631 0.1954771 0.3698991
729 730 731 732 733 734 735 736
0.5093386 0.6607947 0.3698991 0.7326843 0.5825445 0.3698991 0.6585473 0.3698991
737 738 739 740 741 742 743 744
0.7326843 0.2254449 0.3567994 0.8368960 0.5093386 0.5847767 0.8368960 0.7326843
745 746 747 748 749 750 751 752
0.3120550 0.5093386 0.2500383 0.8870283 0.7267631 0.2979029 0.7267631 0.5847767
753 754 755 756 757 758 759 760
0.5847767 0.6607947 0.5025621 0.7326843 0.4258949 0.3698991 0.6607947 0.9412824
761 762 763 764 765 766 767 768
0.2254449 0.3698991 0.6585473 0.1726988 0.2639710 0.3120550 0.5025621 0.5825445
769 770 771 772 773 774 775 776
0.5025621 0.3567994 0.3567994 0.8870283 0.7326843 0.5093386 0.3120550 0.3567994
777 778 779 780 781 782 783 784
0.6585473 0.7326843 0.3698991 0.8463985 0.6607947 0.3567994 0.7950139 0.3698991
785 786 787 788 789 790 791 792
0.4258949 0.3120550 0.2639710 0.5025621 0.5025621 0.5847767 0.2979029 0.8463985
793 794 795 796 797 798 799 800
0.5025621 0.7326843 0.1954771 0.5025621 0.4364813 0.5825445 0.3567994 0.2639710
801 802 803 804 805 806 807 808
0.5825445 0.5825445 0.7950139 0.8368960 0.7267631 0.7326843 0.3120550 0.4258949
809 810 811 812 813 814 815 816
0.2639710 0.8368960 0.8463985 0.5093386 0.7267631 0.7950139 0.2639710 0.4258949
817 818 819 820 821 822 823 824
0.2254449 0.5093386 0.4364813 0.1954771 0.7950139 0.7326843 0.7950139 0.8870283
825 826 827 828 829 830 831 832
0.8870283 0.3698991 0.4258949 0.1954771 0.5025621 0.4364813 0.2254449 0.2500383
833 834 835 836 837 838 839 840
0.5093386 0.5093386 0.3120550 0.2254449 0.4364813 0.3567994 0.8463985 0.5847767
841 842 843 844 845 846 847 848
0.6607947 0.4258949 0.7326843 0.2979029 0.5847767 0.3698991 0.2639710 0.2639710
849 850 851 852 853 854 855 856
0.7326843 0.1726988 0.5025621 0.4258949 0.5825445 0.2500383 0.5825445 0.3698991
857 858 859 860 861 862 863 864
0.5093386 0.5825445 0.5093386 0.7326843 0.5093386 0.7326843 0.6585473 0.7866563
865 866 867 868 869 870 871 872
0.4364813 0.5847767 0.8368960 0.5093386 0.5825445 0.2639710 0.5825445 0.8870283
873 874 875 876 877 878 879 880
0.6607947 0.3698991 0.9181070 0.7326843 0.5825445 0.3567994 0.4364813 0.6585473
881 882 883 884 885 886 887 888
0.3698991 0.1954771 0.5025621 0.8870283 0.5825445 0.4364813 0.6607947 0.5825445
889 890 891 892 893 894 895 896
0.3698991 0.7950139 0.4258949 0.1954771 0.4258949 0.6585473 0.5847767 0.4364813
897 898 899 900 901 902 903 904
0.3120550 0.8368960 0.6585473 0.6607947 0.5825445 0.3120550 0.7866563 0.3120550
905 906 907 908 909 910 911 912
0.7326843 0.2639710 0.7326843 0.5847767 0.3120550 0.7950139 0.6607947 0.3698991
913 914 915 916 917 918 919 920
0.5847767 0.5825445 0.3698991 0.7950139 0.3698991 0.4364813 0.5847767 0.8870283
921 922 923 924 925 926 927 928
0.5847767 0.3567994 0.4258949 0.5025621 0.6607947 0.1954771 0.5825445 0.5847767
929 930 931 932 933 934 935 936
0.5025621 0.5847767 0.7326843 0.7866563 0.5825445 0.4258949 0.5825445 0.3698991
937 938 939 940 941 942 943 944
0.5847767 0.7267631 0.1954771 0.7326843 0.5093386 0.4258949 0.3698991 0.8870283
945 946 947 948 949 950 951 952
0.3120550 0.5847767 0.1954771 0.2500383 0.5825445 0.5025621 0.5847767 0.5093386
953 954 955 956 957 958 959 960
0.3698991 0.8870283 0.6607947 0.1954771 0.5025621 0.5093386 0.2254449 0.8463985
961 962 963 964 965 966 967 968
0.7866563 0.2639710 0.1954771 0.6607947 0.2254449 0.2254449 0.4364813 0.6585473
969 970 971 972 973 974 975 976
0.4364813 0.2639710 0.3698991 0.1726988 0.5825445 0.7267631 0.3698991 0.6607947
977 978 979 980 981 982 983 984
0.7267631 0.7267631 0.6607947 0.7326843 0.6585473 0.9181070 0.5825445 0.7950139
985 986 987 988 989 990 991 992
0.2254449 0.6607947 0.5825445 0.2639710 0.5093386 0.5847767 0.1339969 0.5847767
993 994 995 996 997 998 999 1000
0.5093386 0.3698991 0.7326843 0.3567994 0.9181070 0.3698991 0.8870283 0.6607947
1001 1002 1003 1004 1005 1006 1007 1008
0.5825445 0.3567994 0.2254449 0.4258949 0.7326843 0.2979029 0.9582384 0.6607947
1009 1010 1011 1012 1013 1014 1015 1016
0.5847767 0.5025621 0.2254449 0.6607947 0.1632496 0.4364813 0.5825445 0.2639710
1017 1018 1019 1020 1021 1022 1023 1024
0.1726988 0.7326843 0.3567994 0.5825445 0.7326843 0.4258949 0.9181070 0.4258949
1025 1026 1027 1028 1029 1030 1031 1032
0.6585473 0.4364813 0.6607947 0.2254449 0.3120550 0.6607947 0.4258949 0.5847767
1033 1034 1035 1036 1037 1038 1039 1040
0.5093386 0.1954771 0.7267631 0.5825445 0.1556872 0.4258949 0.7326843 0.1842596
1041 1042 1043 1044 1045 1046 1047 1048
0.6607947 0.3698991 0.4258949 0.6585473 0.6585473 0.6607947 0.3120550 0.3567994
1049 1050 1051 1052 1053 1054 1055 1056
0.3698991 0.7950139 0.2639710 0.3120550 0.6607947 0.4258949 0.5093386 0.4364813
1057 1058 1059 1060 1061 1062 1063 1064
0.2500383 0.4364813 0.3698991 0.3698991 0.2979029 0.3567994 0.5847767 0.5025621
1065 1066 1067 1068 1069 1070 1071 1072
0.7326843 0.7326843 0.7950139 0.8463985 0.3567994 0.6607947 0.4364813 0.4258949
1073 1074 1075 1076 1077 1078 1079 1080
0.4364813 0.1556872 0.7950139 0.7950139 0.1431489 0.2254449 0.5025621 0.2639710
1081 1082 1083 1084 1085 1086 1087 1088
0.4258949 0.2254449 0.9412824 0.1726988 0.5025621 0.2639710 0.2500383 0.5025621
1089 1090 1091 1092 1093 1094 1095 1096
0.2126244 0.4258949 0.5847767 0.7950139 0.8870283 0.7950139 0.6607947 0.5093386
1097 1098 1099 1100 1101 1102 1103 1104
0.5847767 0.1954771 0.3120550 0.3120550 0.9412824 0.2500383 0.7326843 0.2639710
1105 1106 1107 1108 1109 1110 1111 1112
0.7267631 0.5847767 0.3120550 0.4258949 0.5093386 0.4258949 0.5847767 0.4364813
1113 1114 1115 1116 1117 1118 1119 1120
0.3698991 0.2500383 0.7326843 0.6585473 0.5025621 0.6607947 0.5093386 0.7950139
1121 1122 1123 1124 1125 1126 1127 1128
0.8870283 0.7326843 0.5025621 0.6607947 0.5825445 0.5825445 0.3567994 0.3698991
1129 1130 1131 1132 1133 1134 1135 1136
0.4258949 0.6607947 0.1726988 0.3698991 0.8368960 0.4258949 0.8870283 0.4258949
1137 1138 1139 1140 1141 1142 1143 1144
0.2979029 0.5025621 0.2500383 0.5825445 0.3698991 0.1726988 0.3698991 0.3698991
1145 1146 1147 1148 1149 1150 1151 1152
0.5847767 0.5847767 0.7950139 0.5093386 0.3698991 0.4364813 0.6607947 0.2500383
1153 1154 1155 1156 1157 1158 1159 1160
0.2500383 0.2254449 0.7950139 0.9181070 0.8870283 0.4364813 0.9181070 0.7326843
1161 1162 1163 1164 1165 1166 1167 1168
0.2126244 0.2254449 0.5025621 0.7267631 0.7326843 0.2126244 0.5025621 0.2254449
1169 1170 1171 1172 1173 1174 1175 1176
0.5025621 0.7326843 0.4258949 0.4258949 0.2979029 0.9091805 0.9412824 0.7267631
1177 1178 1179 1180 1181 1182 1183 1184
0.7950139 0.5093386 0.1556872 0.5825445 0.7950139 0.5025621 0.5025621 0.6607947
1185 1186 1187 1188 1189 1190 1191 1192
0.4364813 0.5825445 0.3120550 0.5847767 0.5093386 0.4258949 0.6607947 0.8774494
1193 1194 1195 1196 1197 1198 1199 1200
0.7866563 0.5093386 0.7950139 0.3120550 0.7950139 0.4364813 0.2500383 0.7866563
1201 1202 1203 1204 1205 1206 1207 1208
0.6585473 0.5847767 0.3567994 0.6607947 0.4258949 0.8870283 0.9181070 0.2639710
1209 1210 1211 1212 1213 1214 1215 1216
0.9181070 0.3120550 0.3120550 0.6607947 0.3698991 0.9412824 0.3698991 0.4364813
1217 1218 1219 1220 1221 1222 1223 1224
0.6585473 0.6585473 0.8368960 0.1726988 0.5093386 0.7950139 0.4364813 0.5825445
1225 1226 1227 1228 1229 1230 1231 1232
0.3120550 0.7950139 0.3698991 0.7267631 0.7326843 0.4364813 0.5093386 0.7950139
1233 1234 1235 1236 1237 1238 1239 1240
0.5093386 0.7326843 0.9181070 0.3567994 0.7950139 0.4258949 0.7326843 0.3120550
1241 1242 1243 1244 1245 1246 1247 1248
0.5825445 0.6607947 0.5825445 0.7326843 0.7267631 0.5025621 0.7267631 0.5825445
1249 1250 1251 1252 1253 1254 1255 1256
0.9412824 0.3120550 0.3120550 0.3120550 0.4364813 0.5093386 0.3698991 0.2254449
1257 1258 1259 1260 1261 1262 1263 1264
0.5093386 0.7326843 0.5025621 0.5825445 0.5093386 0.1954771 0.3120550 0.6607947
1265 1266 1267 1268 1269 1270 1271 1272
0.5825445 0.1726988 0.5025621 0.6607947 0.5025621 0.2979029 0.2979029 0.5093386
1273 1274 1275 1276 1277 1278 1279 1280
0.7267631 0.3567994 0.7866563 0.7326843 0.3120550 0.8463985 0.4364813 0.6585473
1281 1282 1283 1284 1285 1286 1287 1288
0.3120550 0.9181070 0.1726988 0.5847767 0.5847767 0.2500383 0.5025621 0.9412824
1289 1290 1291 1292 1293 1294 1295 1296
0.1726988 0.5093386 0.8463985 0.1726988 0.5847767 0.6585473 0.6607947 0.4364813
1297 1298 1299 1300 1301 1302 1303 1304
0.6585473 0.8463985 0.3698991 0.5847767 0.7950139 0.1726988 0.7950139 0.3120550
1305 1306 1307 1308 1309 1310 1311 1312
0.7866563 0.6607947 0.2639710 0.5825445 0.5025621 0.8463985 0.8463985 0.4258949
1313 1314 1315 1316 1317 1318 1319 1320
0.3698991 0.2254449 0.5093386 0.7326843 0.7950139 0.8870283 0.7267631 0.6607947
1321 1322 1323 1324 1325 1326 1327 1328
0.5847767 0.3698991 0.5025621 0.7950139 0.6585473 0.2639710 0.3120550 0.5093386
1329 1330 1331 1332 1333 1334 1335 1336
0.8463985 0.2979029 0.6585473 0.5025621 0.7326843 0.3698991 0.5025621 0.7950139
1337 1338 1339 1340 1341 1342 1343 1344
0.2639710 0.5825445 0.7950139 0.2639710 0.3120550 0.9181070 0.5825445 0.2979029
1345 1346 1347 1348 1349 1350 1351 1352
0.5825445 0.4364813 0.5025621 0.2254449 0.3120550 0.5093386 0.3698991 0.5025621
1353 1354 1355 1356 1357 1358 1359 1360
0.8870283 0.7866563 0.3567994 0.5847767 0.5093386 0.5825445 0.7866563 0.8463985
1361 1362 1363 1364 1365 1366 1367 1368
0.2639710 0.6607947 0.5825445 0.2254449 0.5093386 0.6607947 0.9181070 0.3698991
1369 1370 1371 1372 1373 1374 1375 1376
0.6607947 0.9181070 0.6585473 0.6607947 0.9091805 0.3120550 0.4364813 0.5093386
1377 1378 1379 1380 1381 1382 1383 1384
0.7950139 0.4364813 0.5825445 0.4258949 0.2254449 0.5025621 0.5825445 0.1954771
1385 1386 1387 1388 1389 1390 1391 1392
0.7950139 0.3120550 0.3698991 0.1954771 0.1556872 0.7950139 0.5093386 0.5093386
1393 1394 1395 1396 1397 1398 1399 1400
0.7326843 0.7866563 0.7866563 0.7326843 0.4258949 0.5847767 0.4364813 0.2126244
1401 1402 1403 1404 1405 1406 1407 1408
0.7950139 0.9704721 0.4258949 0.7950139 0.3698991 0.3698991 0.6607947 0.5093386
1409 1410 1411 1412 1413 1414 1415 1416
0.8774494 0.7950139 0.2500383 0.5025621 0.2639710 0.3567994 0.4258949 0.9181070
1417 1418 1419 1420 1421 1422 1423 1424
0.6585473 0.7326843 0.2126244 0.5847767 0.3120550 0.5093386 0.4364813 0.8463985
1425 1426 1427 1428 1429 1430 1431 1432
0.6607947 0.7326843 0.6607947 0.2979029 0.4258949 0.1954771 0.7267631 0.3120550
1433 1434 1435 1436 1437 1438 1439 1440
0.5825445 0.5025621 0.4258949 0.1954771 0.7326843 0.9582384 0.7326843 0.5025621
1441 1442 1443 1444 1445 1446 1447 1448
0.1954771 0.7326843 0.5847767 0.5825445 0.8463985 0.2639710 0.7866563 0.3567994
1449 1450 1451 1452 1453 1454 1455 1456
0.6607947 0.7267631 0.8870283 0.4364813 0.3698991 0.4258949 0.5847767 0.6585473
1457 1458 1459 1460 1461 1462 1463 1464
0.7326843 0.3120550 0.5093386 0.2500383 0.5025621 0.5847767 0.3120550 0.2639710
1465 1466 1467 1468 1469 1470 1471 1472
0.7950139 0.5093386 0.5093386 0.5093386 0.6607947 0.1954771 0.5025621 0.8368960
1473 1474 1475 1476 1477 1478 1479 1480
0.2126244 0.7950139 0.3698991 0.2979029 0.7326843 0.1556872 0.8463985 0.7326843
1481 1482 1483 1484 1485 1486 1487 1488
0.3698991 0.8463985 0.3698991 0.9582384 0.4364813 0.5093386 0.2979029 0.7267631
1489 1490 1491 1492 1493 1494 1495 1496
0.4364813 0.6607947 0.6607947 0.3120550 0.5093386 0.7326843 0.5825445 0.3698991
1497 1498 1499 1500 1501 1502 1503 1504
0.4364813 0.7950139 0.6607947 0.5825445 0.7326843 0.5093386 0.9181070 0.1954771
1505 1506 1507 1508 1509 1510 1511 1512
0.7950139 0.6607947 0.2254449 0.5093386 0.1954771 0.5093386 0.2639710 0.4364813
1513 1514 1515 1516 1517 1518 1519 1520
0.3698991 0.2639710 0.5825445 0.4258949 0.5825445 0.5025621 0.1726988 0.7326843
1521 1522 1523 1524 1525 1526 1527 1528
0.4364813 0.6585473 0.5025621 0.7950139 0.5093386 0.6607947 0.2500383 0.6607947
1529 1530 1531 1532 1533 1534 1535 1536
0.5825445 0.6607947 0.4258949 0.7866563 0.7326843 0.4364813 0.3120550 0.9412824
1537 1538 1539 1540 1541 1542 1543 1544
0.2639710 0.5025621 0.6607947 0.5825445 0.8463985 0.6607947 0.2500383 0.3698991
1545 1546 1547 1548 1549 1550 1551 1552
0.8463985 0.8463985 0.3120550 0.6607947 0.6607947 0.1726988 0.2639710 0.2254449
1553 1554 1555 1556 1557 1558 1559 1560
0.2979029 0.3698991 0.4364813 0.3120550 0.5847767 0.2254449 0.6585473 0.3567994
1561 1562 1563 1564 1565 1566 1567 1568
0.1842596 0.5847767 0.9412824 0.5847767 0.6585473 0.6607947 0.5025621 0.9412824
1569 1570 1571 1572 1573 1574 1575 1576
0.1726988 0.4364813 0.7950139 0.6585473 0.7326843 0.4364813 0.3698991 0.5847767
1577 1578 1579 1580 1581 1582 1583 1584
0.3120550 0.5847767 0.5847767 0.8463985 0.2500383 0.3698991 0.8368960 0.1726988
1585 1586 1587 1588 1589 1590 1591 1592
0.8870283 0.7267631 0.5847767 0.2979029 0.1954771 0.7326843 0.7326843 0.3567994
1593 1594 1595 1596 1597 1598 1599 1600
0.5847767 0.2639710 0.5847767 0.5093386 0.7950139 0.2639710 0.7950139 0.5093386
1601 1602 1603 1604 1605 1606 1607 1608
0.5847767 0.9704721 0.1632496 0.3698991 0.6607947 0.7326843 0.3698991 0.3698991
1609 1610 1611 1612 1613 1614 1615 1616
0.2979029 0.5093386 0.5825445 0.3698991 0.7326843 0.2500383 0.8870283 0.5825445
1617 1618 1619 1620 1621 1622 1623 1624
0.5093386 0.2639710 0.4364813 0.1954771 0.5847767 0.7950139 0.5825445 0.5093386
1625 1626 1627 1628 1629 1630 1631 1632
0.3567994 0.2126244 0.6607947 0.7326843 0.8870283 0.7326843 0.5825445 0.4258949
1633 1634 1635 1636 1637 1638 1639 1640
0.5093386 0.6607947 0.2639710 0.7326843 0.7326843 0.7326843 0.3120550 0.5093386
1641 1642 1643 1644 1645 1646 1647 1648
0.8870283 0.6607947 0.4258949 0.6607947 0.5093386 0.7950139 0.2979029 0.7326843
1649 1650 1651 1652 1653 1654 1655 1656
0.5025621 0.5025621 0.3120550 0.7950139 0.3120550 0.5093386 0.4364813 0.5825445
1657 1658 1659 1660 1661 1662 1663 1664
0.2639710 0.4364813 0.1954771 0.7950139 0.6585473 0.7950139 0.9412824 0.5093386
1665 1666 1667 1668 1669 1670 1671 1672
0.2979029 0.5093386 0.5847767 0.8463985 0.1431489 0.6607947 0.3120550 0.3120550
1673 1674 1675 1676 1677 1678 1679 1680
0.5025621 0.2126244 0.4258949 0.5025621 0.7326843 0.5093386 0.5093386 0.7326843
1681 1682 1683 1684 1685 1686 1687 1688
0.4258949 0.2979029 0.5825445 0.7326843 0.5025621 0.4258949 0.2639710 0.2639710
1689 1690 1691 1692 1693 1694 1695 1696
0.2254449 0.7267631 0.3698991 0.5825445 0.5025621 0.2500383 0.6585473 0.3120550
1697 1698 1699 1700 1701 1702 1703 1704
0.3698991 0.6585473 0.4364813 0.4364813 0.1556872 0.1431489 0.5825445 0.5025621
1705 1706 1707 1708 1709 1710 1711 1712
0.3698991 0.2639710 0.7267631 0.6607947 0.9181070 0.5825445 0.1954771 0.3567994
1713 1714 1715 1716 1717 1718 1719 1720
0.2639710 0.5825445 0.3120550 0.4364813 0.9412824 0.5025621 0.2254449 0.2639710
1721 1722 1723 1724 1725 1726 1727 1728
0.6607947 0.2254449 0.7326843 0.3698991 0.5093386 0.6585473 0.5847767 0.2254449
1729 1730 1731 1732 1733 1734 1735 1736
0.5847767 0.1954771 0.3698991 0.4364813 0.8463985 0.5825445 0.6607947 0.5093386
1737 1738 1739 1740 1741 1742 1743 1744
0.1726988 0.6585473 0.2639710 0.5093386 0.5847767 0.2979029 0.4364813 0.9704721
1745 1746 1747 1748 1749 1750 1751 1752
0.5093386 0.3698991 0.2639710 0.3120550 0.8463985 0.1726988 0.4258949 0.7326843
1753 1754 1755 1756 1757 1758 1759 1760
0.2639710 0.9582384 0.3120550 0.6585473 0.5025621 0.2639710 0.3120550 0.7267631
1761 1762 1763 1764 1765 1766 1767 1768
0.8463985 0.2500383 0.3120550 0.5025621 0.5847767 0.8774494 0.9582384 0.6607947
1769 1770 1771 1772 1773 1774 1775 1776
0.5093386 0.8463985 0.3567994 0.2979029 0.7950139 0.5847767 0.1842596 0.8870283
1777 1778 1779 1780 1781 1782 1783 1784
0.6607947 0.4364813 0.2254449 0.3698991 0.7866563 0.2979029 0.2639710 0.5093386
1785 1786 1787 1788 1789 1790 1791 1792
0.2126244 0.1726988 0.3698991 0.6585473 0.7950139 0.7950139 0.4258949 0.5847767
1793 1794 1795 1796 1797 1798 1799 1800
0.7950139 0.5847767 0.9412824 0.6585473 0.3698991 0.7950139 0.5847767 0.5825445
1801 1802 1803 1804 1805 1806 1807 1808
0.3120550 0.5847767 0.7866563 0.3120550 0.5825445 0.5825445 0.3567994 0.5025621
1809 1810 1811 1812 1813 1814 1815 1816
0.4364813 0.5825445 0.4258949 0.8870283 0.3120550 0.5093386 0.2126244 0.3698991
1817 1818 1819 1820 1821 1822 1823 1824
0.8463985 0.2254449 0.6607947 0.2254449 0.3698991 0.9181070 0.4364813 0.3120550
1825 1826 1827 1828 1829 1830 1831 1832
0.2254449 0.2639710 0.7326843 0.7866563 0.1726988 0.1726988 0.5825445 0.5093386
1833 1834 1835 1836 1837 1838 1839 1840
0.6607947 0.3698991 0.2254449 0.2979029 0.5093386 0.3567994 0.4364813 0.5093386
1841 1842 1843 1844 1845 1846 1847 1848
0.5025621 0.7326843 0.7866563 0.3698991 0.5025621 0.7866563 0.6607947 0.1726988
1849 1850 1851 1852 1853 1854 1855 1856
0.6607947 0.1954771 0.5025621 0.2639710 0.2639710 0.6607947 0.5847767 0.8463985
1857 1858 1859 1860 1861 1862 1863 1864
0.2254449 0.3120550 0.5825445 0.4364813 0.4364813 0.3567994 0.2126244 0.2254449
1865 1866 1867 1868 1869 1870 1871 1872
0.3120550 0.7950139 0.7950139 0.4364813 0.2639710 0.5847767 0.6585473 0.5825445
1873 1874 1875 1876 1877 1878 1879 1880
0.8463985 0.6607947 0.6585473 0.3698991 0.3567994 0.6585473 0.6607947 0.7950139
1881 1882 1883 1884 1885 1886 1887 1888
0.8870283 0.5825445 0.3698991 0.2979029 0.7267631 0.6585473 0.7326843 0.4364813
1889 1890 1891 1892 1893 1894 1895 1896
0.6585473 0.4258949 0.1726988 0.5025621 0.1954771 0.5093386 0.4258949 0.5025621
1897 1898 1899 1900 1901 1902 1903 1904
0.5825445 0.5825445 0.8463985 0.5825445 0.4364813 0.5847767 0.6607947 0.3120550
1905 1906 1907 1908 1909 1910 1911 1912
0.7326843 0.7267631 0.2979029 0.7326843 0.7326843 0.3120550 0.7326843 0.6607947
1913 1914 1915 1916 1917 1918 1919 1920
0.3120550 0.8463985 0.3698991 0.5025621 0.7267631 0.7950139 0.3120550 0.7326843
1921 1922 1923 1924 1925 1926 1927 1928
0.3698991 0.3698991 0.1954771 0.1726988 0.9181070 0.2639710 0.5825445 0.3567994
1929 1930 1931 1932 1933 1934 1935 1936
0.6585473 0.4258949 0.2500383 0.5093386 0.6607947 0.2639710 0.5025621 0.5093386
1937 1938 1939 1940 1941 1942 1943 1944
0.8463985 0.2639710 0.2500383 0.5825445 0.4364813 0.5093386 0.6607947 0.2254449
1945 1946 1947 1948 1949 1950 1951 1952
0.4364813 0.8870283 0.5847767 0.7326843 0.7326843 0.3120550 0.6585473 0.9091805
1953 1954 1955 1956 1957 1958 1959 1960
0.2979029 0.7267631 0.5025621 0.4258949 0.6607947 0.3120550 0.7326843 0.7326843
1961 1962 1963 1964 1965 1966 1967 1968
0.3567994 0.8463985 0.5093386 0.5847767 0.5825445 0.3567994 0.6585473 0.7267631
1969 1970 1971 1972 1973 1974 1975 1976
0.4364813 0.8463985 0.5847767 0.5825445 0.5825445 0.1954771 0.5825445 0.7326843
1977 1978 1979 1980 1981 1982 1983 1984
0.5025621 0.4258949 0.3120550 0.1556872 0.3567994 0.6607947 0.5093386 0.3567994
1985 1986 1987 1988 1989 1990 1991 1992
0.2639710 0.3567994 0.4364813 0.7326843 0.2979029 0.8368960 0.3120550 0.6607947
1993 1994 1995 1996 1997 1998 1999 2000
0.2979029 0.7326843 0.8368960 0.5093386 0.4364813 0.7326843 0.7326843 0.7267631
Code
summary(residuals(fit1))
Output
Item1 Item2 Item3
Min. :-0.9412824 Min. :-0.9314870 Min. :-0.932174
1st Qu.:-0.3698991 1st Qu.:-0.4272403 1st Qu.:-0.503827
Median : 0.0818930 Median : 0.1573856 Median : 0.177257
Mean :-0.0001467 Mean :-0.0006539 Mean :-0.002202
3rd Qu.: 0.4152233 3rd Qu.: 0.3637518 3rd Qu.: 0.292406
Max. : 0.8443128 Max. : 0.8829579 Max. : 0.653850
Item4 Item5 Item6
Min. :-0.936174 Min. :-0.968908 Min. :-0.9010826
1st Qu.: 0.077120 1st Qu.: 0.043100 1st Qu.:-0.4807352
Median : 0.158674 Median : 0.108763 Median : 0.1642555
Mean : 0.000033 Mean : 0.002275 Mean : 0.0004391
3rd Qu.: 0.220781 3rd Qu.: 0.186779 3rd Qu.: 0.3180502
Max. : 0.590711 Max. : 0.574219 Max. : 0.8244711
Item7 Item8 Item9
Min. :-0.8851430 Min. :-0.9101997 Min. :-0.8614888
1st Qu.:-0.4890386 1st Qu.:-0.4831021 1st Qu.:-0.5056860
Median : 0.2111020 Median : 0.2142025 Median : 0.2282535
Mean : 0.0005126 Mean : 0.0009005 Mean : 0.0003707
3rd Qu.: 0.3426624 3rd Qu.: 0.3589697 3rd Qu.: 0.3974888
Max. : 0.7690036 Max. : 0.7267992 Max. : 0.7788047
Item10 Item11 Item12
Min. :-0.8362669 Min. :-0.9316052 Min. :-0.8873538
1st Qu.:-0.5002406 1st Qu.:-0.4619166 1st Qu.:-0.4532789
Median : 0.2289536 Median : 0.1821138 Median : 0.1806710
Mean : 0.0004637 Mean : 0.0003882 Mean : 0.0006462
3rd Qu.: 0.4020989 3rd Qu.: 0.3379863 3rd Qu.: 0.4403448
Max. : 0.7747230 Max. : 0.8171048 Max. : 0.7330316
Item13 Item14 Item15
Min. :-0.9364053 Min. :-0.846923 Min. :-0.8203574
1st Qu.:-0.4702768 1st Qu.:-0.391395 1st Qu.:-0.4952739
Median : 0.1899360 Median :-0.206135 Median : 0.2097287
Mean :-0.0001134 Mean : 0.000785 Mean : 0.0006185
3rd Qu.: 0.2708100 3rd Qu.: 0.482109 3rd Qu.: 0.4567820
Max. : 0.5898026 Max. : 0.865499 Max. : 0.7635355
Item16 Item17 Item18
Min. :-0.896687 Min. :-0.7793370 Min. :-0.8303705
1st Qu.:-0.380265 1st Qu.:-0.4142573 1st Qu.:-0.3447702
Median :-0.176930 Median :-0.2407092 Median :-0.1413968
Mean : 0.001382 Mean : 0.0005929 Mean : 0.0007223
3rd Qu.: 0.465682 3rd Qu.: 0.4923666 3rd Qu.: 0.4255570
Max. : 0.870413 Max. : 0.8175671 Max. : 0.9089239
Item19 Item20
Min. :-0.9068390 Min. :-0.8715910
1st Qu.:-0.4038863 1st Qu.:-0.3379118
Median :-0.1494888 Median :-0.2490140
Mean : 0.0007949 Mean : 0.0007879
3rd Qu.: 0.4376773 3rd Qu.: 0.4729031
Max. : 0.8915756 Max. : 0.7866203
Code
residuals(fit1, item = 1)
Output
1 2 3 4 5 6
-0.43648127 0.21334366 0.11297173 -0.22544489 0.20498605 -0.29790295
7 8 9 10 11 12
0.49066136 0.57410507 0.20498605 0.21334366 0.15360152 0.27323691
13 14 15 16 17 18
-0.43648127 -0.50256206 -0.73268430 -0.35679939 0.41745549 0.27323691
19 20 21 22 23 24
-0.43648127 -0.31205497 0.57410507 0.16310397 0.11297173 0.15360152
25 26 27 28 29 30
0.33920533 0.49743794 0.21334366 -0.50256206 0.27323691 -0.26397099
31 32 33 34 35 36
-0.50256206 -0.65854732 -0.79501395 -0.31205497 0.56351873 -0.50933864
37 38 39 40 41 42
-0.66079467 0.15360152 0.49743794 -0.58477674 -0.26397099 0.49743794
43 44 45 46 47 48
-0.50256206 0.20498605 0.20498605 -0.43648127 -0.31205497 0.49743794
49 50 51 52 53 54
0.77455511 -0.50933864 0.49066136 -0.26397099 0.82730124 -0.50933864
55 56 57 58 59 60
0.68794503 0.41745549 -0.31205497 0.49066136 -0.50933864 0.49066136
61 62 63 64 65 66
0.09081953 -0.66079467 0.68794503 -0.22544489 0.34145268 -0.73268430
67 68 69 70 71 72
-0.22544489 0.26731570 -0.14314887 0.05871759 -0.42589493 -0.22544489
73 74 75 76 77 78
0.56351873 0.41745549 -0.50256206 0.77455511 0.34145268 -0.25003829
79 80 81 82 83 84
-0.31205497 0.49743794 0.49066136 -0.58254451 -0.22544489 -0.35679939
85 86 87 88 89 90
-0.50256206 -0.65854732 0.70209705 -0.35679939 -0.43648127 -0.73268430
91 92 93 94 95 96
-0.29790295 0.27323691 -0.43648127 0.27323691 0.73602901 -0.22544489
97 98 99 100 101 102
0.63010089 0.15360152 -0.26397099 0.12255063 0.04176155 0.49066136
103 104 105 106 107 108
0.49066136 0.33920533 -0.36989911 -0.21262439 0.20498605 -0.79501395
109 110 111 112 113 114
0.33920533 -0.17269876 0.34145268 -0.43648127 0.63010089 0.15360152
115 116 117 118 119 120
-0.31205497 -0.58254451 0.68794503 0.11297173 -0.22544489 0.56351873
121 122 123 124 125 126
0.21334366 -0.43648127 -0.26397099 0.15360152 -0.26397099 0.49743794
127 128 129 130 131 132
0.73602901 -0.25003829 -0.26397099 -0.29790295 0.56351873 -0.42589493
133 134 135 136 137 138
-0.42589493 0.15360152 -0.36989911 -0.36989911 -0.18425962 0.41522326
139 140 141 142 143 144
0.26731570 0.15360152 -0.36989911 0.80452289 -0.29790295 0.20498605
145 146 147 148 149 150
0.49743794 -0.66079467 0.08189303 0.26731570 -0.22544489 0.68794503
151 152 153 154 155 156
-0.26397099 -0.22544489 0.33920533 -0.43648127 0.15360152 -0.35679939
157 158 159 160 161 162
-0.22544489 0.08189303 0.05871759 0.63010089 0.57410507 0.49066136
163 164 165 166 167 168
-0.91810697 -0.17269876 0.57410507 -0.43648127 0.33920533 -0.91810697
169 170 171 172 173 174
-0.26397099 0.20498605 -0.29790295 0.33920533 -0.58477674 0.41745549
175 176 177 178 179 180
-0.73268430 0.57410507 -0.43648127 0.21334366 0.11297173 -0.19547711
181 182 183 184 185 186
0.63010089 0.11297173 0.08189303 0.16310397 -0.58477674 -0.50256206
187 188 189 190 191 192
0.11297173 -0.35679939 -0.15568724 0.26731570 -0.31205497 0.49743794
193 194 195 196 197 198
0.26731570 -0.17269876 0.08189303 0.41745549 -0.58254451 -0.66079467
199 200 201 202 203 204
0.20498605 0.56351873 -0.31205497 0.41745549 -0.25003829 0.20498605
205 206 207 208 209 210
0.11297173 -0.73268430 0.26731570 -0.43648127 0.20498605 -0.36989911
211 212 213 214 215 216
-0.58477674 0.63010089 0.74996171 -0.50933864 -0.19547711 -0.29790295
217 218 219 220 221 222
0.26731570 0.20498605 0.05871759 0.26731570 0.41522326 -0.36989911
223 224 225 226 227 228
-0.36989911 -0.73268430 -0.36989911 0.57410507 0.26731570 0.49743794
229 230 231 232 233 234
0.41745549 -0.50933864 0.64320061 0.08189303 0.11297173 0.64320061
235 236 237 238 239 240
0.15360152 -0.73268430 0.41745549 -0.94128241 -0.31205497 0.08189303
241 242 243 244 245 246
0.26731570 -0.65854732 0.33920533 0.56351873 0.49743794 -0.31205497
247 248 249 250 251 252
-0.43648127 -0.58254451 0.41522326 -0.31205497 0.15360152 0.49743794
253 254 255 256 257 258
0.20498605 -0.66079467 -0.58477674 -0.58477674 0.33920533 0.56351873
259 260 261 262 263 264
0.27323691 0.20498605 -0.14314887 0.34145268 -0.35679939 -0.26397099
265 266 267 268 269 270
0.33920533 -0.58254451 0.15360152 -0.43648127 0.21334366 -0.17269876
271 272 273 274 275 276
-0.43648127 -0.66079467 0.20498605 -0.35679939 0.08189303 -0.31205497
277 278 279 280 281 282
-0.50933864 -0.19547711 0.64320061 -0.50933864 -0.26397099 0.26731570
283 284 285 286 287 288
0.41745549 -0.42589493 -0.58477674 -0.78665634 0.49743794 -0.79501395
289 290 291 292 293 294
0.05871759 -0.42589493 -0.42589493 -0.26397099 0.68794503 -0.58254451
295 296 297 298 299 300
0.56351873 0.15360152 0.41522326 -0.50256206 0.41522326 0.15360152
301 302 303 304 305 306
0.41522326 -0.36989911 -0.35679939 -0.25003829 -0.66079467 0.08189303
307 308 309 310 311 312
0.20498605 0.08189303 0.57410507 -0.36989911 -0.66079467 -0.26397099
313 314 315 316 317 318
-0.43648127 -0.50933864 0.12255063 0.20498605 -0.50933864 -0.65854732
319 320 321 322 323 324
0.41745549 -0.31205497 0.73602901 0.49066136 0.26731570 -0.13399692
325 326 327 328 329 330
0.34145268 0.26731570 -0.35679939 0.20498605 0.26731570 -0.17269876
331 332 333 334 335 336
-0.31205497 -0.36989911 -0.43648127 0.73602901 0.11297173 0.49743794
337 338 339 340 341 342
-0.58254451 0.56351873 0.33920533 -0.36989911 -0.66079467 0.41522326
343 344 345 346 347 348
0.20498605 -0.73268430 0.49743794 0.49743794 0.56351873 0.57410507
349 350 351 352 353 354
0.15360152 -0.26397099 -0.19547711 0.15360152 -0.58254451 0.33920533
355 356 357 358 359 360
-0.29790295 0.15360152 -0.50933864 -0.26397099 -0.29790295 0.57410507
361 362 363 364 365 366
0.21334366 0.73602901 -0.50933864 -0.29790295 0.11297173 -0.43648127
367 368 369 370 371 372
-0.35679939 -0.31205497 -0.26397099 -0.58477674 -0.35679939 -0.50933864
373 374 375 376 377 378
0.77455511 -0.42589493 0.41745549 0.68794503 -0.17269876 -0.73268430
379 380 381 382 383 384
0.49743794 0.20498605 0.15360152 -0.50933864 0.57410507 -0.58254451
385 386 387 388 389 390
-0.66079467 0.05871759 -0.50256206 0.02952791 -0.35679939 -0.15568724
391 392 393 394 395 396
-0.31205497 -0.36989911 0.27323691 -0.50933864 -0.50256206 0.63010089
397 398 399 400 401 402
-0.42589493 0.16310397 -0.83689603 0.15360152 -0.25003829 -0.31205497
403 404 405 406 407 408
-0.43648127 -0.84639848 0.08189303 -0.36989911 0.49066136 0.41522326
409 410 411 412 413 414
0.56351873 0.16310397 0.34145268 0.56351873 0.20498605 -0.42589493
415 416 417 418 419 420
-0.58254451 -0.19547711 0.49066136 -0.29790295 0.57410507 0.84431276
421 422 423 424 425 426
-0.31205497 0.34145268 -0.43648127 -0.43648127 0.49066136 -0.35679939
427 428 429 430 431 432
-0.65854732 0.34145268 -0.50933864 0.49743794 -0.36989911 0.56351873
433 434 435 436 437 438
0.49743794 0.15360152 -0.31205497 0.21334366 0.20498605 0.73602901
439 440 441 442 443 444
0.74996171 -0.50933864 0.41522326 0.49066136 0.26731570 -0.22544489
445 446 447 448 449 450
-0.31205497 0.20498605 -0.65854732 -0.65854732 -0.19547711 -0.31205497
451 452 453 454 455 456
0.41522326 -0.17269876 0.16310397 0.49066136 0.49743794 0.27323691
457 458 459 460 461 462
0.49066136 0.41522326 0.68794503 0.26731570 0.49066136 0.41522326
463 464 465 466 467 468
-0.72676309 -0.79501395 0.78737561 0.73602901 -0.50933864 0.26731570
469 470 471 472 473 474
-0.58477674 0.34145268 -0.58254451 0.33920533 -0.31205497 -0.50256206
475 476 477 478 479 480
0.20498605 0.26731570 -0.35679939 -0.50933864 -0.50933864 0.26731570
481 482 483 484 485 486
0.15360152 -0.58477674 0.20498605 0.26731570 0.49743794 -0.31205497
487 488 489 490 491 492
0.20498605 -0.66079467 -0.43648127 0.34145268 -0.73268430 -0.22544489
493 494 495 496 497 498
0.77455511 -0.31205497 -0.50256206 0.80452289 0.70209705 0.41522326
499 500 501 502 503 504
-0.50256206 0.21334366 -0.84639848 -0.25003829 -0.36989911 0.68794503
505 506 507 508 509 510
-0.50256206 0.57410507 0.68794503 -0.43648127 0.68794503 0.68794503
511 512 513 514 515 516
-0.31205497 -0.18425962 -0.21262439 0.33920533 -0.72676309 -0.29790295
517 518 519 520 521 522
-0.36989911 0.57410507 -0.26397099 0.49066136 0.41745549 0.49066136
523 524 525 526 527 528
0.09081953 -0.78665634 0.49066136 0.34145268 0.11297173 -0.58477674
529 530 531 532 533 534
-0.26397099 -0.84639848 0.15360152 -0.58477674 -0.36989911 0.57410507
535 536 537 538 539 540
0.68794503 0.33920533 0.33920533 0.26731570 0.49743794 0.49066136
541 542 543 544 545 546
-0.26397099 -0.36989911 0.34145268 -0.35679939 0.49743794 0.26731570
547 548 549 550 551 552
-0.50933864 -0.43648127 -0.36989911 0.77455511 -0.43648127 -0.22544489
553 554 555 556 557 558
0.26731570 0.56351873 0.64320061 -0.43648127 0.34145268 -0.26397099
559 560 561 562 563 564
0.33920533 0.49066136 0.15360152 -0.25003829 0.49743794 0.20498605
565 566 567 568 569 570
0.56351873 0.41522326 -0.26397099 -0.73268430 0.08189303 0.57410507
571 572 573 574 575 576
-0.50256206 -0.72676309 -0.36989911 0.56351873 0.49743794 -0.50933864
577 578 579 580 581 582
-0.36989911 0.26731570 0.82730124 -0.36989911 0.57410507 -0.19547711
583 584 585 586 587 588
0.49743794 -0.50256206 0.49066136 -0.26397099 -0.43648127 -0.15568724
589 590 591 592 593 594
-0.79501395 0.56351873 0.11297173 -0.21262439 0.64320061 0.41745549
595 596 597 598 599 600
-0.50256206 0.49743794 0.56351873 -0.22544489 0.41522326 -0.66079467
601 602 603 604 605 606
0.73602901 -0.25003829 0.63010089 0.73602901 -0.31205497 0.26731570
607 608 609 610 611 612
-0.84639848 -0.73268430 0.21334366 0.41522326 0.33920533 -0.58477674
613 614 615 616 617 618
-0.58254451 0.21334366 0.56351873 -0.15568724 -0.50256206 -0.15568724
619 620 621 622 623 624
-0.73268430 -0.29790295 -0.43648127 0.80452289 -0.43648127 -0.43648127
625 626 627 628 629 630
-0.36989911 -0.19547711 -0.79501395 0.57410507 0.11297173 0.26731570
631 632 633 634 635 636
-0.22544489 -0.66079467 0.41745549 0.20498605 0.26731570 0.15360152
637 638 639 640 641 642
0.33920533 0.26731570 0.41745549 0.11297173 -0.42589493 -0.58254451
643 644 645 646 647 648
0.41745549 -0.58477674 0.77455511 -0.35679939 0.68794503 0.64320061
649 650 651 652 653 654
-0.42589493 0.49743794 -0.87744937 -0.42589493 0.26731570 0.26731570
655 656 657 658 659 660
-0.66079467 -0.43648127 -0.79501395 0.26731570 -0.19547711 0.26731570
661 662 663 664 665 666
-0.31205497 0.20498605 -0.26397099 -0.31205497 0.68794503 0.02952791
667 668 669 670 671 672
-0.58254451 0.33920533 0.70209705 -0.22544489 -0.36989911 0.41522326
673 674 675 676 677 678
-0.43648127 -0.31205497 0.26731570 -0.58477674 -0.19547711 -0.43648127
679 680 681 682 683 684
-0.31205497 0.56351873 -0.17269876 -0.42589493 -0.31205497 -0.15568724
685 686 687 688 689 690
0.33920533 -0.88702827 -0.43648127 0.16310397 -0.31205497 0.63010089
691 692 693 694 695 696
0.15360152 -0.50933864 0.49743794 -0.50933864 -0.25003829 0.20498605
697 698 699 700 701 702
0.20498605 0.49743794 0.73602901 0.20498605 0.74996171 0.73602901
703 704 705 706 707 708
0.08189303 -0.26397099 0.63010089 0.70209705 -0.79501395 -0.42589493
709 710 711 712 713 714
0.57410507 0.68794503 0.84431276 0.11297173 0.20498605 0.41745549
715 716 717 718 719 720
-0.50933864 -0.50933864 0.41745549 -0.19547711 -0.31205497 -0.50256206
721 722 723 724 725 726
0.49066136 0.57410507 -0.50256206 -0.58254451 -0.84639848 -0.72676309
727 728 729 730 731 732
-0.19547711 -0.36989911 -0.50933864 0.33920533 -0.36989911 -0.73268430
733 734 735 736 737 738
-0.58254451 0.63010089 -0.65854732 0.63010089 0.26731570 -0.22544489
739 740 741 742 743 744
-0.35679939 0.16310397 -0.50933864 -0.58477674 0.16310397 0.26731570
745 746 747 748 749 750
-0.31205497 0.49066136 -0.25003829 -0.88702827 0.27323691 -0.29790295
751 752 753 754 755 756
0.27323691 -0.58477674 -0.58477674 0.33920533 -0.50256206 0.26731570
757 758 759 760 761 762
-0.42589493 0.63010089 -0.66079467 0.05871759 -0.22544489 0.63010089
763 764 765 766 767 768
0.34145268 -0.17269876 0.73602901 -0.31205497 -0.50256206 -0.58254451
769 770 771 772 773 774
-0.50256206 -0.35679939 -0.35679939 0.11297173 0.26731570 -0.50933864
775 776 777 778 779 780
-0.31205497 -0.35679939 0.34145268 0.26731570 -0.36989911 0.15360152
781 782 783 784 785 786
0.33920533 0.64320061 0.20498605 -0.36989911 -0.42589493 0.68794503
787 788 789 790 791 792
-0.26397099 0.49743794 0.49743794 0.41522326 0.70209705 0.15360152
793 794 795 796 797 798
-0.50256206 0.26731570 -0.19547711 0.49743794 0.56351873 -0.58254451
799 800 801 802 803 804
-0.35679939 -0.26397099 0.41745549 -0.58254451 0.20498605 0.16310397
805 806 807 808 809 810
0.27323691 -0.73268430 -0.31205497 -0.42589493 -0.26397099 0.16310397
811 812 813 814 815 816
0.15360152 -0.50933864 -0.72676309 0.20498605 -0.26397099 -0.42589493
817 818 819 820 821 822
-0.22544489 -0.50933864 -0.43648127 -0.19547711 -0.79501395 0.26731570
823 824 825 826 827 828
0.20498605 0.11297173 0.11297173 0.63010089 0.57410507 -0.19547711
829 830 831 832 833 834
-0.50256206 -0.43648127 -0.22544489 -0.25003829 -0.50933864 0.49066136
835 836 837 838 839 840
-0.31205497 -0.22544489 0.56351873 -0.35679939 -0.84639848 0.41522326
841 842 843 844 845 846
0.33920533 0.57410507 -0.73268430 -0.29790295 -0.58477674 -0.36989911
847 848 849 850 851 852
-0.26397099 -0.26397099 0.26731570 -0.17269876 -0.50256206 0.57410507
853 854 855 856 857 858
-0.58254451 -0.25003829 0.41745549 -0.36989911 -0.50933864 0.41745549
859 860 861 862 863 864
0.49066136 -0.73268430 0.49066136 0.26731570 -0.65854732 0.21334366
865 866 867 868 869 870
0.56351873 -0.58477674 0.16310397 0.49066136 0.41745549 -0.26397099
871 872 873 874 875 876
0.41745549 0.11297173 0.33920533 0.63010089 0.08189303 0.26731570
877 878 879 880 881 882
0.41745549 0.64320061 0.56351873 0.34145268 -0.36989911 -0.19547711
883 884 885 886 887 888
0.49743794 0.11297173 0.41745549 0.56351873 0.33920533 0.41745549
889 890 891 892 893 894
-0.36989911 0.20498605 0.57410507 -0.19547711 0.57410507 0.34145268
895 896 897 898 899 900
0.41522326 0.56351873 -0.31205497 0.16310397 -0.65854732 0.33920533
901 902 903 904 905 906
-0.58254451 -0.31205497 0.21334366 0.68794503 0.26731570 -0.26397099
907 908 909 910 911 912
0.26731570 0.41522326 0.68794503 0.20498605 0.33920533 -0.36989911
913 914 915 916 917 918
-0.58477674 0.41745549 -0.36989911 0.20498605 0.63010089 0.56351873
919 920 921 922 923 924
0.41522326 0.11297173 -0.58477674 0.64320061 -0.42589493 -0.50256206
925 926 927 928 929 930
0.33920533 -0.19547711 0.41745549 0.41522326 0.49743794 -0.58477674
931 932 933 934 935 936
0.26731570 0.21334366 0.41745549 -0.42589493 -0.58254451 -0.36989911
937 938 939 940 941 942
-0.58477674 -0.72676309 -0.19547711 -0.73268430 -0.50933864 -0.42589493
943 944 945 946 947 948
-0.36989911 0.11297173 -0.31205497 -0.58477674 -0.19547711 -0.25003829
949 950 951 952 953 954
0.41745549 0.49743794 0.41522326 0.49066136 -0.36989911 0.11297173
955 956 957 958 959 960
0.33920533 0.80452289 -0.50256206 -0.50933864 0.77455511 0.15360152
961 962 963 964 965 966
0.21334366 -0.26397099 0.80452289 -0.66079467 -0.22544489 -0.22544489
967 968 969 970 971 972
-0.43648127 0.34145268 0.56351873 0.73602901 0.63010089 -0.17269876
973 974 975 976 977 978
-0.58254451 -0.72676309 -0.36989911 0.33920533 -0.72676309 0.27323691
979 980 981 982 983 984
0.33920533 -0.73268430 0.34145268 0.08189303 -0.58254451 -0.79501395
985 986 987 988 989 990
0.77455511 -0.66079467 -0.58254451 -0.26397099 0.49066136 0.41522326
991 992 993 994 995 996
-0.13399692 0.41522326 0.49066136 -0.36989911 0.26731570 -0.35679939
997 998 999 1000 1001 1002
-0.91810697 -0.36989911 0.11297173 0.33920533 0.41745549 0.64320061
1003 1004 1005 1006 1007 1008
-0.22544489 0.57410507 0.26731570 -0.29790295 0.04176155 -0.66079467
1009 1010 1011 1012 1013 1014
0.41522326 0.49743794 -0.22544489 -0.66079467 -0.16324961 0.56351873
1015 1016 1017 1018 1019 1020
0.41745549 0.73602901 0.82730124 0.26731570 -0.35679939 0.41745549
1021 1022 1023 1024 1025 1026
0.26731570 -0.42589493 0.08189303 0.57410507 0.34145268 0.56351873
1027 1028 1029 1030 1031 1032
0.33920533 0.77455511 -0.31205497 0.33920533 -0.42589493 0.41522326
1033 1034 1035 1036 1037 1038
0.49066136 -0.19547711 0.27323691 -0.58254451 -0.15568724 -0.42589493
1039 1040 1041 1042 1043 1044
-0.73268430 -0.18425962 0.33920533 0.63010089 -0.42589493 -0.65854732
1045 1046 1047 1048 1049 1050
0.34145268 -0.66079467 -0.31205497 0.64320061 -0.36989911 0.20498605
1051 1052 1053 1054 1055 1056
-0.26397099 0.68794503 -0.66079467 -0.42589493 0.49066136 -0.43648127
1057 1058 1059 1060 1061 1062
0.74996171 0.56351873 -0.36989911 0.63010089 -0.29790295 0.64320061
1063 1064 1065 1066 1067 1068
0.41522326 0.49743794 0.26731570 0.26731570 0.20498605 0.15360152
1069 1070 1071 1072 1073 1074
-0.35679939 -0.66079467 -0.43648127 -0.42589493 -0.43648127 -0.15568724
1075 1076 1077 1078 1079 1080
0.20498605 0.20498605 -0.14314887 0.77455511 -0.50256206 -0.26397099
1081 1082 1083 1084 1085 1086
0.57410507 0.77455511 0.05871759 -0.17269876 0.49743794 0.73602901
1087 1088 1089 1090 1091 1092
-0.25003829 0.49743794 -0.21262439 0.57410507 0.41522326 0.20498605
1093 1094 1095 1096 1097 1098
-0.88702827 0.20498605 -0.66079467 -0.50933864 -0.58477674 0.80452289
1099 1100 1101 1102 1103 1104
0.68794503 0.68794503 0.05871759 -0.25003829 0.26731570 0.73602901
1105 1106 1107 1108 1109 1110
-0.72676309 -0.58477674 -0.31205497 0.57410507 0.49066136 0.57410507
1111 1112 1113 1114 1115 1116
0.41522326 -0.43648127 -0.36989911 -0.25003829 -0.73268430 0.34145268
1117 1118 1119 1120 1121 1122
0.49743794 0.33920533 0.49066136 0.20498605 0.11297173 0.26731570
1123 1124 1125 1126 1127 1128
0.49743794 0.33920533 0.41745549 0.41745549 -0.35679939 0.63010089
1129 1130 1131 1132 1133 1134
0.57410507 0.33920533 -0.17269876 -0.36989911 0.16310397 -0.42589493
1135 1136 1137 1138 1139 1140
0.11297173 0.57410507 -0.29790295 -0.50256206 0.74996171 0.41745549
1141 1142 1143 1144 1145 1146
-0.36989911 -0.17269876 -0.36989911 0.63010089 0.41522326 -0.58477674
1147 1148 1149 1150 1151 1152
0.20498605 -0.50933864 -0.36989911 -0.43648127 0.33920533 -0.25003829
1153 1154 1155 1156 1157 1158
-0.25003829 -0.22544489 -0.79501395 0.08189303 0.11297173 -0.43648127
1159 1160 1161 1162 1163 1164
0.08189303 -0.73268430 -0.21262439 -0.22544489 0.49743794 0.27323691
1165 1166 1167 1168 1169 1170
0.26731570 -0.21262439 -0.50256206 0.77455511 0.49743794 -0.73268430
1171 1172 1173 1174 1175 1176
0.57410507 -0.42589493 0.70209705 0.09081953 0.05871759 -0.72676309
1177 1178 1179 1180 1181 1182
-0.79501395 0.49066136 -0.15568724 0.41745549 0.20498605 -0.50256206
1183 1184 1185 1186 1187 1188
0.49743794 -0.66079467 -0.43648127 -0.58254451 -0.31205497 0.41522326
1189 1190 1191 1192 1193 1194
-0.50933864 0.57410507 0.33920533 0.12255063 -0.78665634 0.49066136
1195 1196 1197 1198 1199 1200
0.20498605 0.68794503 -0.79501395 0.56351873 0.74996171 0.21334366
1201 1202 1203 1204 1205 1206
0.34145268 0.41522326 -0.35679939 0.33920533 -0.42589493 0.11297173
1207 1208 1209 1210 1211 1212
0.08189303 -0.26397099 0.08189303 0.68794503 0.68794503 -0.66079467
1213 1214 1215 1216 1217 1218
-0.36989911 0.05871759 0.63010089 0.56351873 -0.65854732 -0.65854732
1219 1220 1221 1222 1223 1224
-0.83689603 -0.17269876 -0.50933864 0.20498605 0.56351873 -0.58254451
1225 1226 1227 1228 1229 1230
-0.31205497 0.20498605 0.63010089 -0.72676309 0.26731570 0.56351873
1231 1232 1233 1234 1235 1236
0.49066136 -0.79501395 -0.50933864 -0.73268430 0.08189303 -0.35679939
1237 1238 1239 1240 1241 1242
0.20498605 -0.42589493 -0.73268430 -0.31205497 0.41745549 0.33920533
1243 1244 1245 1246 1247 1248
0.41745549 0.26731570 -0.72676309 -0.50256206 0.27323691 -0.58254451
1249 1250 1251 1252 1253 1254
0.05871759 -0.31205497 0.68794503 -0.31205497 0.56351873 -0.50933864
1255 1256 1257 1258 1259 1260
0.63010089 -0.22544489 -0.50933864 -0.73268430 0.49743794 -0.58254451
1261 1262 1263 1264 1265 1266
0.49066136 -0.19547711 -0.31205497 0.33920533 -0.58254451 -0.17269876
1267 1268 1269 1270 1271 1272
-0.50256206 0.33920533 0.49743794 -0.29790295 0.70209705 -0.50933864
1273 1274 1275 1276 1277 1278
0.27323691 -0.35679939 0.21334366 0.26731570 0.68794503 0.15360152
1279 1280 1281 1282 1283 1284
-0.43648127 -0.65854732 -0.31205497 0.08189303 -0.17269876 0.41522326
1285 1286 1287 1288 1289 1290
0.41522326 -0.25003829 0.49743794 0.05871759 -0.17269876 -0.50933864
1291 1292 1293 1294 1295 1296
-0.84639848 -0.17269876 0.41522326 0.34145268 -0.66079467 -0.43648127
1297 1298 1299 1300 1301 1302
0.34145268 0.15360152 0.63010089 -0.58477674 0.20498605 -0.17269876
1303 1304 1305 1306 1307 1308
0.20498605 -0.31205497 0.21334366 -0.66079467 -0.26397099 -0.58254451
1309 1310 1311 1312 1313 1314
-0.50256206 0.15360152 0.15360152 0.57410507 -0.36989911 0.77455511
1315 1316 1317 1318 1319 1320
-0.50933864 -0.73268430 0.20498605 0.11297173 0.27323691 0.33920533
1321 1322 1323 1324 1325 1326
0.41522326 0.63010089 -0.50256206 0.20498605 -0.65854732 -0.26397099
1327 1328 1329 1330 1331 1332
0.68794503 0.49066136 0.15360152 0.70209705 0.34145268 -0.50256206
1333 1334 1335 1336 1337 1338
-0.73268430 0.63010089 -0.50256206 0.20498605 0.73602901 0.41745549
1339 1340 1341 1342 1343 1344
0.20498605 0.73602901 0.68794503 0.08189303 0.41745549 -0.29790295
1345 1346 1347 1348 1349 1350
0.41745549 -0.43648127 -0.50256206 0.77455511 -0.31205497 0.49066136
1351 1352 1353 1354 1355 1356
0.63010089 -0.50256206 0.11297173 0.21334366 -0.35679939 0.41522326
1357 1358 1359 1360 1361 1362
-0.50933864 0.41745549 0.21334366 0.15360152 -0.26397099 0.33920533
1363 1364 1365 1366 1367 1368
0.41745549 0.77455511 0.49066136 0.33920533 0.08189303 0.63010089
1369 1370 1371 1372 1373 1374
0.33920533 0.08189303 0.34145268 0.33920533 0.09081953 -0.31205497
1375 1376 1377 1378 1379 1380
0.56351873 0.49066136 0.20498605 -0.43648127 -0.58254451 -0.42589493
1381 1382 1383 1384 1385 1386
-0.22544489 -0.50256206 -0.58254451 -0.19547711 0.20498605 -0.31205497
1387 1388 1389 1390 1391 1392
-0.36989911 -0.19547711 -0.15568724 0.20498605 0.49066136 -0.50933864
1393 1394 1395 1396 1397 1398
0.26731570 0.21334366 0.21334366 0.26731570 -0.42589493 0.41522326
1399 1400 1401 1402 1403 1404
-0.43648127 -0.21262439 0.20498605 0.02952791 -0.42589493 0.20498605
1405 1406 1407 1408 1409 1410
-0.36989911 0.63010089 -0.66079467 -0.50933864 0.12255063 0.20498605
1411 1412 1413 1414 1415 1416
-0.25003829 0.49743794 -0.26397099 -0.35679939 -0.42589493 -0.91810697
1417 1418 1419 1420 1421 1422
0.34145268 -0.73268430 -0.21262439 -0.58477674 -0.31205497 0.49066136
1423 1424 1425 1426 1427 1428
0.56351873 0.15360152 0.33920533 0.26731570 0.33920533 -0.29790295
1429 1430 1431 1432 1433 1434
-0.42589493 -0.19547711 -0.72676309 -0.31205497 0.41745549 0.49743794
1435 1436 1437 1438 1439 1440
-0.42589493 0.80452289 -0.73268430 0.04176155 0.26731570 0.49743794
1441 1442 1443 1444 1445 1446
0.80452289 -0.73268430 -0.58477674 0.41745549 0.15360152 -0.26397099
1447 1448 1449 1450 1451 1452
0.21334366 0.64320061 0.33920533 0.27323691 -0.88702827 -0.43648127
1453 1454 1455 1456 1457 1458
-0.36989911 0.57410507 0.41522326 0.34145268 -0.73268430 -0.31205497
1459 1460 1461 1462 1463 1464
0.49066136 -0.25003829 0.49743794 -0.58477674 0.68794503 0.73602901
1465 1466 1467 1468 1469 1470
0.20498605 0.49066136 0.49066136 -0.50933864 -0.66079467 0.80452289
1471 1472 1473 1474 1475 1476
-0.50256206 0.16310397 -0.21262439 0.20498605 -0.36989911 -0.29790295
1477 1478 1479 1480 1481 1482
-0.73268430 -0.15568724 0.15360152 0.26731570 0.63010089 -0.84639848
1483 1484 1485 1486 1487 1488
0.63010089 0.04176155 0.56351873 -0.50933864 -0.29790295 0.27323691
1489 1490 1491 1492 1493 1494
0.56351873 -0.66079467 0.33920533 -0.31205497 -0.50933864 0.26731570
1495 1496 1497 1498 1499 1500
-0.58254451 -0.36989911 -0.43648127 0.20498605 0.33920533 -0.58254451
1501 1502 1503 1504 1505 1506
0.26731570 -0.50933864 0.08189303 0.80452289 0.20498605 -0.66079467
1507 1508 1509 1510 1511 1512
-0.22544489 -0.50933864 -0.19547711 -0.50933864 -0.26397099 -0.43648127
1513 1514 1515 1516 1517 1518
0.63010089 -0.26397099 0.41745549 0.57410507 -0.58254451 0.49743794
1519 1520 1521 1522 1523 1524
-0.17269876 0.26731570 -0.43648127 0.34145268 0.49743794 0.20498605
1525 1526 1527 1528 1529 1530
-0.50933864 0.33920533 -0.25003829 0.33920533 -0.58254451 0.33920533
1531 1532 1533 1534 1535 1536
0.57410507 0.21334366 0.26731570 -0.43648127 -0.31205497 0.05871759
1537 1538 1539 1540 1541 1542
-0.26397099 0.49743794 0.33920533 0.41745549 0.15360152 -0.66079467
1543 1544 1545 1546 1547 1548
-0.25003829 -0.36989911 0.15360152 0.15360152 -0.31205497 0.33920533
1549 1550 1551 1552 1553 1554
-0.66079467 -0.17269876 0.73602901 -0.22544489 -0.29790295 0.63010089
1555 1556 1557 1558 1559 1560
-0.43648127 -0.31205497 0.41522326 0.77455511 0.34145268 0.64320061
1561 1562 1563 1564 1565 1566
-0.18425962 0.41522326 -0.94128241 0.41522326 0.34145268 0.33920533
1567 1568 1569 1570 1571 1572
0.49743794 -0.94128241 0.82730124 -0.43648127 0.20498605 0.34145268
1573 1574 1575 1576 1577 1578
0.26731570 -0.43648127 -0.36989911 0.41522326 -0.31205497 -0.58477674
1579 1580 1581 1582 1583 1584
0.41522326 -0.84639848 -0.25003829 -0.36989911 0.16310397 0.82730124
1585 1586 1587 1588 1589 1590
0.11297173 0.27323691 -0.58477674 -0.29790295 -0.19547711 0.26731570
1591 1592 1593 1594 1595 1596
0.26731570 -0.35679939 0.41522326 -0.26397099 -0.58477674 -0.50933864
1597 1598 1599 1600 1601 1602
-0.79501395 -0.26397099 0.20498605 -0.50933864 0.41522326 0.02952791
1603 1604 1605 1606 1607 1608
-0.16324961 -0.36989911 0.33920533 0.26731570 0.63010089 -0.36989911
1609 1610 1611 1612 1613 1614
-0.29790295 0.49066136 0.41745549 -0.36989911 0.26731570 0.74996171
1615 1616 1617 1618 1619 1620
0.11297173 -0.58254451 0.49066136 -0.26397099 -0.43648127 -0.19547711
1621 1622 1623 1624 1625 1626
-0.58477674 0.20498605 0.41745549 0.49066136 -0.35679939 0.78737561
1627 1628 1629 1630 1631 1632
0.33920533 0.26731570 0.11297173 -0.73268430 0.41745549 -0.42589493
1633 1634 1635 1636 1637 1638
0.49066136 -0.66079467 -0.26397099 -0.73268430 0.26731570 -0.73268430
1639 1640 1641 1642 1643 1644
-0.31205497 0.49066136 0.11297173 -0.66079467 0.57410507 0.33920533
1645 1646 1647 1648 1649 1650
-0.50933864 0.20498605 0.70209705 -0.73268430 -0.50256206 0.49743794
1651 1652 1653 1654 1655 1656
0.68794503 0.20498605 -0.31205497 -0.50933864 0.56351873 0.41745549
1657 1658 1659 1660 1661 1662
-0.26397099 0.56351873 -0.19547711 -0.79501395 -0.65854732 0.20498605
1663 1664 1665 1666 1667 1668
0.05871759 -0.50933864 0.70209705 0.49066136 0.41522326 0.15360152
1669 1670 1671 1672 1673 1674
-0.14314887 0.33920533 -0.31205497 0.68794503 -0.50256206 0.78737561
1675 1676 1677 1678 1679 1680
-0.42589493 -0.50256206 -0.73268430 -0.50933864 0.49066136 0.26731570
1681 1682 1683 1684 1685 1686
-0.42589493 -0.29790295 -0.58254451 0.26731570 0.49743794 -0.42589493
1687 1688 1689 1690 1691 1692
0.73602901 -0.26397099 0.77455511 0.27323691 -0.36989911 0.41745549
1693 1694 1695 1696 1697 1698
0.49743794 -0.25003829 0.34145268 -0.31205497 0.63010089 0.34145268
1699 1700 1701 1702 1703 1704
-0.43648127 0.56351873 -0.15568724 -0.14314887 0.41745549 -0.50256206
1705 1706 1707 1708 1709 1710
-0.36989911 -0.26397099 0.27323691 0.33920533 -0.91810697 -0.58254451
1711 1712 1713 1714 1715 1716
-0.19547711 0.64320061 -0.26397099 -0.58254451 -0.31205497 -0.43648127
1717 1718 1719 1720 1721 1722
0.05871759 -0.50256206 -0.22544489 -0.26397099 0.33920533 -0.22544489
1723 1724 1725 1726 1727 1728
-0.73268430 -0.36989911 0.49066136 0.34145268 0.41522326 -0.22544489
1729 1730 1731 1732 1733 1734
0.41522326 -0.19547711 -0.36989911 0.56351873 0.15360152 0.41745549
1735 1736 1737 1738 1739 1740
0.33920533 -0.50933864 -0.17269876 -0.65854732 -0.26397099 0.49066136
1741 1742 1743 1744 1745 1746
-0.58477674 -0.29790295 -0.43648127 0.02952791 0.49066136 -0.36989911
1747 1748 1749 1750 1751 1752
-0.26397099 -0.31205497 0.15360152 -0.17269876 -0.42589493 0.26731570
1753 1754 1755 1756 1757 1758
-0.26397099 0.04176155 0.68794503 0.34145268 -0.50256206 -0.26397099
1759 1760 1761 1762 1763 1764
-0.31205497 0.27323691 0.15360152 -0.25003829 -0.31205497 0.49743794
1765 1766 1767 1768 1769 1770
0.41522326 0.12255063 0.04176155 0.33920533 -0.50933864 0.15360152
1771 1772 1773 1774 1775 1776
-0.35679939 -0.29790295 0.20498605 0.41522326 -0.18425962 0.11297173
1777 1778 1779 1780 1781 1782
-0.66079467 0.56351873 -0.22544489 0.63010089 0.21334366 -0.29790295
1783 1784 1785 1786 1787 1788
0.73602901 0.49066136 -0.21262439 -0.17269876 -0.36989911 0.34145268
1789 1790 1791 1792 1793 1794
0.20498605 0.20498605 -0.42589493 0.41522326 -0.79501395 0.41522326
1795 1796 1797 1798 1799 1800
0.05871759 0.34145268 0.63010089 0.20498605 0.41522326 0.41745549
1801 1802 1803 1804 1805 1806
-0.31205497 -0.58477674 0.21334366 -0.31205497 -0.58254451 -0.58254451
1807 1808 1809 1810 1811 1812
0.64320061 0.49743794 -0.43648127 -0.58254451 -0.42589493 0.11297173
1813 1814 1815 1816 1817 1818
-0.31205497 0.49066136 -0.21262439 -0.36989911 0.15360152 -0.22544489
1819 1820 1821 1822 1823 1824
0.33920533 0.77455511 0.63010089 0.08189303 0.56351873 0.68794503
1825 1826 1827 1828 1829 1830
-0.22544489 -0.26397099 0.26731570 -0.78665634 0.82730124 -0.17269876
1831 1832 1833 1834 1835 1836
-0.58254451 0.49066136 0.33920533 -0.36989911 -0.22544489 -0.29790295
1837 1838 1839 1840 1841 1842
0.49066136 -0.35679939 -0.43648127 -0.50933864 -0.50256206 0.26731570
1843 1844 1845 1846 1847 1848
0.21334366 -0.36989911 0.49743794 0.21334366 0.33920533 -0.17269876
1849 1850 1851 1852 1853 1854
-0.66079467 -0.19547711 -0.50256206 -0.26397099 -0.26397099 0.33920533
1855 1856 1857 1858 1859 1860
-0.58477674 0.15360152 -0.22544489 -0.31205497 -0.58254451 0.56351873
1861 1862 1863 1864 1865 1866
-0.43648127 -0.35679939 -0.21262439 -0.22544489 0.68794503 0.20498605
1867 1868 1869 1870 1871 1872
0.20498605 0.56351873 -0.26397099 -0.58477674 -0.65854732 -0.58254451
1873 1874 1875 1876 1877 1878
0.15360152 -0.66079467 0.34145268 0.63010089 0.64320061 0.34145268
1879 1880 1881 1882 1883 1884
0.33920533 0.20498605 0.11297173 -0.58254451 -0.36989911 -0.29790295
1885 1886 1887 1888 1889 1890
0.27323691 -0.65854732 0.26731570 0.56351873 -0.65854732 0.57410507
1891 1892 1893 1894 1895 1896
-0.17269876 0.49743794 -0.19547711 0.49066136 0.57410507 -0.50256206
1897 1898 1899 1900 1901 1902
0.41745549 -0.58254451 0.15360152 -0.58254451 -0.43648127 -0.58477674
1903 1904 1905 1906 1907 1908
0.33920533 -0.31205497 0.26731570 -0.72676309 -0.29790295 0.26731570
1909 1910 1911 1912 1913 1914
0.26731570 -0.31205497 0.26731570 0.33920533 -0.31205497 0.15360152
1915 1916 1917 1918 1919 1920
0.63010089 -0.50256206 0.27323691 0.20498605 -0.31205497 0.26731570
1921 1922 1923 1924 1925 1926
-0.36989911 0.63010089 -0.19547711 -0.17269876 0.08189303 -0.26397099
1927 1928 1929 1930 1931 1932
0.41745549 0.64320061 0.34145268 0.57410507 -0.25003829 0.49066136
1933 1934 1935 1936 1937 1938
0.33920533 -0.26397099 -0.50256206 -0.50933864 0.15360152 0.73602901
1939 1940 1941 1942 1943 1944
-0.25003829 0.41745549 0.56351873 0.49066136 0.33920533 -0.22544489
1945 1946 1947 1948 1949 1950
-0.43648127 0.11297173 -0.58477674 0.26731570 0.26731570 -0.31205497
1951 1952 1953 1954 1955 1956
0.34145268 0.09081953 0.70209705 0.27323691 -0.50256206 -0.42589493
1957 1958 1959 1960 1961 1962
0.33920533 0.68794503 0.26731570 0.26731570 -0.35679939 0.15360152
1963 1964 1965 1966 1967 1968
-0.50933864 0.41522326 -0.58254451 0.64320061 0.34145268 0.27323691
1969 1970 1971 1972 1973 1974
-0.43648127 0.15360152 0.41522326 -0.58254451 0.41745549 -0.19547711
1975 1976 1977 1978 1979 1980
-0.58254451 0.26731570 -0.50256206 -0.42589493 -0.31205497 -0.15568724
1981 1982 1983 1984 1985 1986
-0.35679939 0.33920533 -0.50933864 0.64320061 -0.26397099 0.64320061
1987 1988 1989 1990 1991 1992
0.56351873 0.26731570 0.70209705 0.16310397 0.68794503 0.33920533
1993 1994 1995 1996 1997 1998
-0.29790295 0.26731570 0.16310397 -0.50933864 -0.43648127 0.26731570
1999 2000
0.26731570 0.27323691
Code
summary(predict(fit1))
Output
item match group prob
Item1 : 2000 Min. :-2.7699 0:20000 Min. :0.06246
Item2 : 2000 1st Qu.:-0.5161 1:20000 1st Qu.:0.43153
Item3 : 2000 Median : 0.1278 Median :0.59037
Item4 : 2000 Mean : 0.0000 Mean :0.57972
Item5 : 2000 3rd Qu.: 0.7718 3rd Qu.:0.74058
Item6 : 2000 Max. : 2.7036 Max. :0.98863
(Other):28000
Code
predict(fit1, item = 1)
Output
item match group prob
1 Item1 0.1278239 1 0.4364813
2 Item1 1.7376967 1 0.7866563
3 Item1 1.4157221 0 0.8870283
4 Item1 -1.1600743 1 0.2254449
5 Item1 0.7717730 0 0.7950139
6 Item1 -1.4820489 0 0.2979029
7 Item1 0.4497985 1 0.5093386
8 Item1 -0.8380998 0 0.4258949
9 Item1 0.7717730 0 0.7950139
10 Item1 1.7376967 1 0.7866563
11 Item1 1.0937476 0 0.8463985
12 Item1 1.4157221 1 0.7267631
13 Item1 0.1278239 1 0.4364813
14 Item1 -0.5161252 0 0.5025621
15 Item1 0.4497985 0 0.7326843
16 Item1 -1.1600743 0 0.3567994
17 Item1 -0.1941507 0 0.5825445
18 Item1 1.4157221 1 0.7267631
19 Item1 0.1278239 1 0.4364813
20 Item1 -0.5161252 1 0.3120550
21 Item1 -0.8380998 0 0.4258949
22 Item1 2.0596712 1 0.8368960
23 Item1 1.4157221 0 0.8870283
24 Item1 1.0937476 0 0.8463985
25 Item1 0.1278239 0 0.6607947
26 Item1 -0.5161252 0 0.5025621
27 Item1 1.7376967 1 0.7866563
28 Item1 -0.5161252 0 0.5025621
29 Item1 1.4157221 1 0.7267631
30 Item1 -0.8380998 1 0.2639710
31 Item1 -0.5161252 0 0.5025621
32 Item1 1.0937476 1 0.6585473
33 Item1 0.7717730 0 0.7950139
34 Item1 -0.5161252 1 0.3120550
35 Item1 0.1278239 1 0.4364813
36 Item1 0.4497985 1 0.5093386
37 Item1 0.1278239 0 0.6607947
38 Item1 1.0937476 0 0.8463985
39 Item1 -0.5161252 0 0.5025621
40 Item1 0.7717730 1 0.5847767
41 Item1 -0.8380998 1 0.2639710
42 Item1 -0.5161252 0 0.5025621
43 Item1 -0.5161252 0 0.5025621
44 Item1 0.7717730 0 0.7950139
45 Item1 0.7717730 0 0.7950139
46 Item1 0.1278239 1 0.4364813
47 Item1 -0.5161252 1 0.3120550
48 Item1 -0.5161252 0 0.5025621
49 Item1 -1.1600743 1 0.2254449
50 Item1 0.4497985 1 0.5093386
51 Item1 0.4497985 1 0.5093386
52 Item1 -0.8380998 1 0.2639710
53 Item1 -1.8040234 1 0.1726988
54 Item1 0.4497985 1 0.5093386
55 Item1 -0.5161252 1 0.3120550
56 Item1 -0.1941507 0 0.5825445
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1959 Item1 0.4497985 0 0.7326843
1960 Item1 0.4497985 0 0.7326843
1961 Item1 -1.1600743 0 0.3567994
1962 Item1 1.0937476 0 0.8463985
1963 Item1 0.4497985 1 0.5093386
1964 Item1 0.7717730 1 0.5847767
1965 Item1 -0.1941507 0 0.5825445
1966 Item1 -1.1600743 0 0.3567994
1967 Item1 1.0937476 1 0.6585473
1968 Item1 1.4157221 1 0.7267631
1969 Item1 0.1278239 1 0.4364813
1970 Item1 1.0937476 0 0.8463985
1971 Item1 0.7717730 1 0.5847767
1972 Item1 -0.1941507 0 0.5825445
1973 Item1 -0.1941507 0 0.5825445
1974 Item1 -1.4820489 1 0.1954771
1975 Item1 -0.1941507 0 0.5825445
1976 Item1 0.4497985 0 0.7326843
1977 Item1 -0.5161252 0 0.5025621
1978 Item1 -0.8380998 0 0.4258949
1979 Item1 -0.5161252 1 0.3120550
1980 Item1 -2.1259980 1 0.1556872
1981 Item1 -1.1600743 0 0.3567994
1982 Item1 0.1278239 0 0.6607947
1983 Item1 0.4497985 1 0.5093386
1984 Item1 -1.1600743 0 0.3567994
1985 Item1 -0.8380998 1 0.2639710
1986 Item1 -1.1600743 0 0.3567994
1987 Item1 0.1278239 1 0.4364813
1988 Item1 0.4497985 0 0.7326843
1989 Item1 -1.4820489 0 0.2979029
1990 Item1 2.0596712 1 0.8368960
1991 Item1 -0.5161252 1 0.3120550
1992 Item1 0.1278239 0 0.6607947
1993 Item1 -1.4820489 0 0.2979029
1994 Item1 0.4497985 0 0.7326843
1995 Item1 2.0596712 1 0.8368960
1996 Item1 0.4497985 1 0.5093386
1997 Item1 0.1278239 1 0.4364813
1998 Item1 0.4497985 0 0.7326843
1999 Item1 0.4497985 0 0.7326843
2000 Item1 1.4157221 1 0.7267631
Code
predict(fit1, item = 1, match = 0, group = c(0, 1))
Output
item match group prob
1 Item1 0 0 0.6302562
2 Item1 0 1 0.4091277
Code
AIC(fit1)
Output
[1] 2515.030 2549.043 2342.008 2013.198 1670.650 2455.279 2518.547 2598.912
[9] 2693.296 2695.274 2371.996 2694.215 2221.522 2624.144 2737.446 2527.643
[17] 2738.945 2484.224 2607.867 2613.890
Code
BIC(fit1)
Output
[1] 2543.035 2577.048 2358.811 2030.000 1687.453 2472.082 2546.552 2615.714
[9] 2710.099 2712.076 2388.799 2711.018 2238.325 2640.947 2754.248 2544.446
[17] 2755.748 2501.027 2635.871 2630.693
Code
logLik(fit1)
Output
[1] -1252.515 -1269.522 -1168.004 -1003.599 -832.325 -1224.639 -1254.274
[8] -1296.456 -1343.648 -1344.637 -1182.998 -1344.108 -1107.761 -1309.072
[15] -1365.723 -1260.821 -1366.473 -1239.112 -1298.933 -1303.945
Code
AIC(fit1, item = 1)
Output
[1] 2515.03
Code
BIC(fit1, item = 1)
Output
[1] 2543.035
Code
logLik(fit1, item = 1)
Output
'log Lik.' -1252.515 (df=5)
Code
(fit2 <- difNLR(Data, group, focal.name = 1, model = "3PLcg", test = "W"))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression Wald test statistics
based on 3PL model with fixed guessing for groups
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 41.9145 0.0000 ***
Item2 14.7397 0.0006 ***
Item3 0.6903 0.7081
Item4 2.8622 0.2390
Item5 1.0890 0.5801
Item6 0.1547 0.9256
Item7 5.6898 0.0581 .
Item8 2.4127 0.2993
Item9 0.4219 0.8098
Item10 1.1699 0.5571
Item11 1.2022 0.5482
Item12 1.0143 0.6022
Item13 3.8549 0.1455
Item14 1.4149 0.4929
Item15 1.1084 0.5745
Item16 0.1394 0.9327
Item17 2.5649 0.2774
Item18 1.9413 0.3788
Item19 4.8067 0.0904 .
Item20 3.1155 0.2106
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 5.9915 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Code
(fit3 <- difNLR(Data, group, focal.name = 1, model = "3PLcg", test = "F"))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression F-test statistics
based on 3PL model with fixed guessing for groups
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
F-value P-value
Item1 41.7833 0.0000 ***
Item2 14.2267 0.0000 ***
Item3 0.3414 0.7108
Item4 1.6500 0.1923
Item5 0.5979 0.5501
Item6 0.0784 0.9246
Item7 4.1498 0.0159 *
Item8 1.4304 0.2395
Item9 0.2269 0.7970
Item10 0.6739 0.5098
Item11 0.6202 0.5379
Item12 0.5257 0.5912
Item13 2.2039 0.1106
Item14 0.7454 0.4747
Item15 0.6525 0.5208
Item16 0.0710 0.9314
Item17 1.5809 0.2060
Item18 1.0083 0.3650
Item19 3.1244 0.0442 *
Item20 1.7407 0.1757
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 3.0002 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Item7
Item19
Code
(fit4 <- difNLR(Data, group, focal.name = 1, model = "3PLcg", sandwich = TRUE))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 3PL model with fixed guessing for groups
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 82.0689 0.0000 ***
Item2 28.3232 0.0000 ***
Item3 0.6845 0.7102
Item4 3.3055 0.1915
Item5 1.1984 0.5492
Item6 0.1573 0.9244
Item7 8.3032 0.0157 *
Item8 2.8660 0.2386
Item9 0.4549 0.7966
Item10 1.3507 0.5090
Item11 1.2431 0.5371
Item12 1.0537 0.5905
Item13 4.4139 0.1100
Item14 1.4940 0.4738
Item15 1.3079 0.5200
Item16 0.1424 0.9313
Item17 3.1673 0.2052
Item18 2.0206 0.3641
Item19 6.2546 0.0438 *
Item20 3.4871 0.1749
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 5.9915 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Item7
Item19
Code
(fit5 <- difNLR(Data, group, focal.name = 1, model = "3PLcg", p.adjust.method = "BH")
)
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 3PL model with fixed guessing for groups
Parameters were estimated using non-linear least squares
Item purification was not applied
Multiple comparisons made with Benjamini-Hochberg adjustment of p-values
Chisq-value P-value Adj. P-value
Item1 82.0689 0.0000 0.0000 ***
Item2 28.3232 0.0000 0.0000 ***
Item3 0.6845 0.7102 0.8355
Item4 3.3055 0.1915 0.5131
Item5 1.1984 0.5492 0.7323
Item6 0.1573 0.9244 0.9313
Item7 8.3032 0.0157 0.1049
Item8 2.8660 0.2386 0.5302
Item9 0.4549 0.7966 0.8851
Item10 1.3507 0.5090 0.7323
Item11 1.2431 0.5371 0.7323
Item12 1.0537 0.5905 0.7381
Item13 4.4139 0.1100 0.4401
Item14 1.4940 0.4738 0.7323
Item15 1.3079 0.5200 0.7323
Item16 0.1424 0.9313 0.9313
Item17 3.1673 0.2052 0.5131
Item18 2.0206 0.3641 0.7282
Item19 6.2546 0.0438 0.2192
Item20 3.4871 0.1749 0.5131
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 5.9915 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Code
(fit6 <- difNLR(Data, group, focal.name = 1, model = "3PLcg", purify = TRUE))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 3PL model with fixed guessing for groups
Parameters were estimated using non-linear least squares
Item purification was applied with 2 iterations.
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 78.1279 0.0000 ***
Item2 27.7354 0.0000 ***
Item3 0.0027 0.9986
Item4 2.0028 0.3674
Item5 1.8701 0.3926
Item6 1.1317 0.5679
Item7 4.2269 0.1208
Item8 3.8019 0.1494
Item9 0.2162 0.8975
Item10 0.3917 0.8221
Item11 3.1631 0.2057
Item12 0.2259 0.8932
Item13 2.4394 0.2953
Item14 0.4483 0.7992
Item15 0.4197 0.8107
Item16 1.0382 0.5950
Item17 1.4373 0.4874
Item18 0.1553 0.9253
Item19 3.1764 0.2043
Item20 5.3659 0.0684 .
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 5.9915 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Code
(fit7 <- difNLR(Data, group, focal.name = 1, model = "3PLcg", match = "score"))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 3PL model with fixed guessing for groups
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 82.0689 0.0000 ***
Item2 28.3232 0.0000 ***
Item3 0.6845 0.7102
Item4 3.3055 0.1915
Item5 1.1984 0.5492
Item6 0.1573 0.9244
Item7 8.3032 0.0157 *
Item8 2.8660 0.2386
Item9 0.4549 0.7966
Item10 1.3507 0.5090
Item11 1.2431 0.5371
Item12 1.0537 0.5905
Item13 4.4139 0.1100
Item14 1.4940 0.4738
Item15 1.3079 0.5200
Item16 0.1424 0.9313
Item17 3.1673 0.2052
Item18 2.0206 0.3641
Item19 6.2546 0.0438 *
Item20 3.4871 0.1749
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 5.9915 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Item7
Item19
Code
(fit8 <- difNLR(Data, group, focal.name = 1, model = "4PLcgdg", type = "udif"))
Output
Detection of uniform differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 4PL model with fixed guessing and inattention parameter for groups
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 81.9549 0.0000 ***
Item2 14.5542 0.0001 ***
Item3 0.3153 0.5745
Item4 2.4851 0.1149
Item5 0.0007 0.9793
Item6 0.0716 0.7890
Item7 7.9741 0.0047 **
Item8 0.1516 0.6970
Item9 0.0948 0.7582
Item10 0.9293 0.3350
Item11 0.3832 0.5359
Item12 0.5839 0.4448
Item13 2.0124 0.1560
Item14 1.1316 0.2874
Item15 0.8492 0.3568
Item16 0.0204 0.8864
Item17 2.8934 0.0889 .
Item18 1.7608 0.1845
Item19 2.1632 0.1414
Item20 1.6239 0.2025
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 3.8415 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Item7
Code
(fit9 <- difNLR(Data, group, focal.name = 1, model = "2PL", type = "nudif"))
Output
Detection of non-uniform differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 2PL model
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 1.4694 0.2254
Item2 12.8761 0.0003 ***
Item3 0.2241 0.6359
Item4 0.8464 0.3576
Item5 1.7056 0.1916
Item6 0.0902 0.7639
Item7 0.3291 0.5662
Item8 2.7878 0.0950 .
Item9 0.3482 0.5551
Item10 0.4159 0.5190
Item11 0.8829 0.3474
Item12 1.1279 0.2882
Item13 4.2949 0.0382 *
Item14 0.4103 0.5218
Item15 0.4587 0.4982
Item16 0.2215 0.6379
Item17 0.1550 0.6938
Item18 0.1909 0.6622
Item19 3.6780 0.0551 .
Item20 1.0065 0.3157
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 3.8415 (significance level: 0.05)
Items detected as DIF items:
Item2
Item13
Code
(fit10 <- difNLR(Data, group, focal.name = 1, model = "4PL", constraints = "ac",
type = "b"))
Output
Detection of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 4PL model with constraints on parameters a, c
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 17.3612 0.0000 ***
Item2 17.5307 0.0000 ***
Item3 0.0337 0.8543
Item4 0.0239 0.8771
Item5 0.0007 0.9793
Item6 0.0507 0.8218
Item7 3.3095 0.0689 .
Item8 1.3556 0.2443
Item9 0.1936 0.6599
Item10 0.4760 0.4902
Item11 0.0073 0.9319
Item12 0.2910 0.5896
Item13 0.1490 0.6995
Item14 0.4523 0.5012
Item15 0.5203 0.4707
Item16 0.0204 0.8864
Item17 1.0202 0.3125
Item18 1.3394 0.2471
Item19 4.8954 0.0269 *
Item20 1.0550 0.3044
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 3.8415 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Item19
Code
(fit11 <- difNLR(Data, group, focal.name = 1, model = "3PLcg", method = "mle"))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 3PL model with fixed guessing for groups
Parameters were estimated using maximum likelihood method
with the L-BFGS-B algorithm
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 74.3235 0.0000 ***
Item2 27.1120 0.0000 ***
Item3 0.7401 0.6907
Item4 3.4097 0.1818
Item5 0.1188 0.9423
Item6 0.0989 0.9517
Item7 7.2097 0.0272 *
Item8 5.5471 0.0624 .
Item9 0.4107 0.8144
Item10 1.2497 0.5353
Item11 0.9490 0.6222
Item12 0.6327 0.7288
Item13 2.5885 0.2741
Item14 1.8876 0.3891
Item15 1.4200 0.4916
Item16 0.5303 0.7671
Item17 3.1490 0.2071
Item18 1.3752 0.5028
Item19 5.6279 0.0600 .
Item20 3.1076 0.2114
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 5.9915 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Item7
Code
(fit13 <- difNLR(Data, group, focal.name = 1, model = "3PLcg", method = "plf"))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 3PL model with fixed guessing for groups
Parameters were estimated using maximum likelihood method
with the PLF algorithm
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 73.5354 0.0000 ***
Item2 27.6368 0.0000 ***
Item3 0.7401 0.6907
Item4 3.4110 0.1817
Item5 0.6804 0.7116
Item6 0.1073 0.9478
Item7 7.2092 0.0272 *
Item8 4.9733 0.0832 .
Item9 0.4276 0.8075
Item10 1.3559 0.5076
Item11 1.0097 0.6036
Item12 1.0773 0.5835
Item13 3.2177 0.2001
Item14 2.3594 0.3074
Item15 1.4200 0.4916
Item16 0.8646 0.6490
Item17 3.1339 0.2087
Item18 1.3752 0.5028
Item19 5.0306 0.0808 .
Item20 3.7682 0.1520
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 5.9915 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Item7
Code
(fit14 <- difNLR(Data, group, focal.name = 1, model = "2PL", method = "irls"))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 2PL model
Parameters were estimated using maximum likelihood method
with the iteratively reweighted least squares algorithm
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 73.5146 0.0000 ***
Item2 27.5609 0.0000 ***
Item3 0.7401 0.6907
Item4 3.4110 0.1817
Item5 0.6860 0.7096
Item6 0.1072 0.9478
Item7 7.2092 0.0272 *
Item8 4.9704 0.0833 .
Item9 0.4278 0.8074
Item10 1.3567 0.5075
Item11 1.0111 0.6032
Item12 1.0835 0.5817
Item13 3.2220 0.1997
Item14 2.3760 0.3048
Item15 1.4200 0.4916
Item16 0.8831 0.6430
Item17 3.1335 0.2087
Item18 1.3752 0.5028
Item19 5.0045 0.0819 .
Item20 3.7580 0.1527
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 5.9915 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Item7
testing paper code - R Journal 2020 - generated data
Code
head(df[, c(1:5, 16)])
Output
Item1 Item2 Item3 Item4 Item5 group
1 0 1 1 1 1 0
2 0 1 1 0 1 0
3 0 1 0 0 1 0
4 1 1 1 0 1 0
5 1 1 0 1 1 0
6 0 1 0 0 1 0
Code
(fit1 <- difNLR(DataDIF, groupDIF, focal.name = 1, model = "4PL", type = "all"))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 4PL model
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 6.2044 0.1844
Item2 0.2802 0.9911
Item3 2.7038 0.6086
Item4 5.8271 0.2124
Item5 48.0052 0.0000 ***
Item6 7.2060 0.1254
Item7 3.2390 0.5187
Item8 16.8991 0.0020 **
Item9 2.1595 0.7064
Item10 4.6866 0.3210
Item11 69.5328 0.0000 ***
Item12 8.1931 0.0848 .
Item13 2.5850 0.6295
Item14 2.9478 0.5666
Item15 20.6589 0.0004 ***
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 9.4877 (significance level: 0.05)
Items detected as DIF items:
Item5
Item8
Item11
Item15
Code
round(coef(fit1, simplify = TRUE), 3)
Output
a b c d aDif bDif cDif dDif
Item1 estimate 1.484 1.294 0.049 1.000 0.000 0.000 0.000 0.000
Item1 CI2.5 0.730 0.726 -0.022 0.633 0.000 0.000 0.000 0.000
Item1 CI97.5 2.237 1.861 0.119 1.367 0.000 0.000 0.000 0.000
Item2 estimate 1.176 0.153 0.000 1.000 0.000 0.000 0.000 0.000
Item2 CI2.5 0.417 -0.279 -0.242 0.751 0.000 0.000 0.000 0.000
Item2 CI97.5 1.936 0.584 0.242 1.249 0.000 0.000 0.000 0.000
Item3 estimate 1.281 1.766 0.001 1.000 0.000 0.000 0.000 0.000
Item3 CI2.5 0.548 0.773 -0.063 0.381 0.000 0.000 0.000 0.000
Item3 CI97.5 2.014 2.758 0.065 1.619 0.000 0.000 0.000 0.000
Item4 estimate 1.450 0.421 0.000 1.000 0.000 0.000 0.000 0.000
Item4 CI2.5 0.785 0.122 -0.128 0.802 0.000 0.000 0.000 0.000
Item4 CI97.5 2.115 0.719 0.128 1.198 0.000 0.000 0.000 0.000
Item5 estimate 1.965 -1.147 0.000 0.868 -0.408 0.769 0.023 -0.006
Item5 CI2.5 0.310 -1.939 -0.602 0.780 -2.455 -0.177 -0.654 -0.188
Item5 CI97.5 3.619 -0.356 0.602 0.955 1.640 1.715 0.699 0.177
Item6 estimate 1.458 -0.527 0.000 0.954 0.000 0.000 0.000 0.000
Item6 CI2.5 0.670 -0.953 -0.288 0.837 0.000 0.000 0.000 0.000
Item6 CI97.5 2.246 -0.101 0.288 1.071 0.000 0.000 0.000 0.000
Item7 estimate 0.888 1.392 0.000 1.000 0.000 0.000 0.000 0.000
Item7 CI2.5 -0.076 -0.526 -0.209 0.059 0.000 0.000 0.000 0.000
Item7 CI97.5 1.852 3.311 0.209 1.941 0.000 0.000 0.000 0.000
Item8 estimate 1.162 1.407 0.000 0.866 -0.117 0.974 0.007 0.134
Item8 CI2.5 -0.004 -0.078 -0.139 0.109 -1.975 -3.428 -0.173 -2.270
Item8 CI97.5 2.329 2.892 0.139 1.622 1.741 5.375 0.186 2.539
Item9 estimate 1.482 -1.337 0.000 0.928 0.000 0.000 0.000 0.000
Item9 CI2.5 0.426 -2.385 -0.705 0.850 0.000 0.000 0.000 0.000
Item9 CI97.5 2.538 -0.290 0.705 1.005 0.000 0.000 0.000 0.000
Item10 estimate 1.375 -0.570 0.007 0.967 0.000 0.000 0.000 0.000
Item10 CI2.5 0.572 -1.070 -0.323 0.841 0.000 0.000 0.000 0.000
Item10 CI97.5 2.178 -0.069 0.338 1.093 0.000 0.000 0.000 0.000
Item11 estimate 1.071 -1.027 0.000 0.969 1.173 -0.499 0.000 0.011
Item11 CI2.5 -0.199 -2.862 -1.022 0.763 -0.948 -2.500 -1.293 -0.204
Item11 CI97.5 2.341 0.808 1.022 1.175 3.294 1.502 1.293 0.225
Item12 estimate 1.051 1.560 0.080 1.000 0.000 0.000 0.000 0.000
Item12 CI2.5 0.035 -0.162 -0.056 0.141 0.000 0.000 0.000 0.000
Item12 CI97.5 2.066 3.283 0.215 1.859 0.000 0.000 0.000 0.000
Item13 estimate 1.009 1.348 0.084 1.000 0.000 0.000 0.000 0.000
Item13 CI2.5 -0.013 -0.253 -0.084 0.217 0.000 0.000 0.000 0.000
Item13 CI97.5 2.030 2.949 0.253 1.783 0.000 0.000 0.000 0.000
Item14 estimate 1.093 1.659 0.141 1.000 0.000 0.000 0.000 0.000
Item14 CI2.5 -0.065 -0.300 0.016 0.064 0.000 0.000 0.000 0.000
Item14 CI97.5 2.252 3.618 0.266 1.936 0.000 0.000 0.000 0.000
Item15 estimate 0.875 -0.565 0.000 0.945 0.205 0.348 0.000 -0.142
Item15 CI2.5 -0.789 -2.871 -1.192 0.460 -2.042 -2.203 -1.312 -0.739
Item15 CI97.5 2.539 1.740 1.192 1.429 2.452 2.900 1.312 0.454
Code
round(coef(fit1, SE = TRUE)[[5]], 3)
Output
a b aDif bDif c cDif d dDif
estimate 1.965 -1.147 -0.408 0.769 0.000 0.023 0.868 -0.006
SE 0.844 0.404 1.045 0.483 0.307 0.345 0.044 0.093
CI2.5 0.310 -1.939 -2.455 -0.177 -0.602 -0.654 0.780 -0.188
CI97.5 3.619 -0.356 1.640 1.715 0.602 0.699 0.955 0.177
Code
(fit2 <- difNLR(DataDIF, groupDIF, focal.name = 1, model = model, type = "all"))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 3.7724 0.0521 .
Item2 0.2487 0.8831
Item3 2.0373 0.3611
Item4 5.8151 0.1210
Item5 46.2121 0.0000 ***
Item6 6.9990 0.0719 .
Item7 3.2390 0.3562
Item8 16.8991 0.0020 **
Item9 2.1595 0.7064
Item10 4.6866 0.3210
Item11 69.5328 0.0000 ***
Item12 8.1931 0.0848 .
Item13 2.5850 0.6295
Item14 2.9478 0.5666
Item15 20.6589 0.0004 ***
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DIF items:
Item5
Item8
Item11
Item15
Code
(fit3 <- difNLR(DataDIF, groupDIF, focal.name = 1, model = model, type = type))
Output
Detection of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 3.7724 0.0521 .
Item2 0.2487 0.8831
Item3 2.0373 0.3611
Item4 5.8151 0.1210
Item5 10.0923 0.0015 **
Item6 6.9990 0.0719 .
Item7 3.2390 0.3562
Item8 0.0681 0.7941
Item9 2.1595 0.7064
Item10 4.6866 0.3210
Item11 0.0000 1.0000
Item12 8.1931 0.0848 .
Item13 2.5850 0.6295
Item14 2.9478 0.5666
Item15 0.3552 0.5512
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DIF items:
Item5
Code
(fit4 <- difNLR(DataDIF, groupDIF, focal.name = 1, model = model, constraints = constraints,
type = type))
Output
Detection of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 3.7724 0.0521 .
Item2 0.2487 0.8831
Item3 2.0373 0.3611
Item4 5.8151 0.1210
Item5 46.1905 0.0000 ***
Item6 6.9990 0.0719 .
Item7 3.2390 0.3562
Item8 11.6006 0.0007 ***
Item9 2.1595 0.7064
Item10 4.6866 0.3210
Item11 35.2213 0.0000 ***
Item12 8.1931 0.0848 .
Item13 2.5850 0.6295
Item14 2.9478 0.5666
Item15 17.7716 0.0000 ***
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DIF items:
Item5
Item8
Item11
Item15
Code
(df <- data.frame(AIC = c(AIC(fit2), AIC(fit3), AIC(fit4)), BIC = c(BIC(fit2),
BIC(fit3), BIC(fit4)), Fit = paste("fit", rep(2:4, each = 15), sep = ""), Item = as.factor(
rep(1:15, 3))))
Output
AIC BIC Fit Item
1 903.5972 913.4127 fit2 1
2 1183.4245 1193.2400 fit2 2
3 557.5339 567.3495 fit2 3
4 1054.5443 1069.2676 fit2 4
5 1169.9188 1199.3653 fit2 5
6 1138.6881 1153.4114 fit2 6
7 1035.9053 1050.6286 fit2 7
8 641.3773 680.6393 fit2 8
9 1004.7217 1024.3527 fit2 9
10 1143.9437 1163.5747 fit2 10
11 860.0053 899.2674 fit2 11
12 1096.9640 1116.5950 fit2 12
13 1172.5120 1192.1430 fit2 13
14 1190.0824 1209.7134 fit2 14
15 1311.7767 1351.0387 fit2 15
16 903.5972 913.4127 fit3 1
17 1183.4245 1193.2400 fit3 2
18 557.5339 567.3495 fit3 3
19 1054.5443 1069.2676 fit3 4
20 1169.9188 1199.3653 fit3 5
21 1138.6881 1153.4114 fit3 6
22 1035.9053 1050.6286 fit3 7
23 639.4454 673.7997 fit3 8
24 1004.7217 1024.3527 fit3 9
25 1143.9437 1163.5747 fit3 10
26 858.0053 892.3596 fit3 11
27 1096.9640 1116.5950 fit3 12
28 1172.5120 1192.1430 fit3 13
29 1190.0824 1209.7134 fit3 14
30 1310.1319 1344.4861 fit3 15
31 903.5972 913.4127 fit4 1
32 1183.4245 1193.2400 fit4 2
33 557.5339 567.3495 fit4 3
34 1054.5443 1069.2676 fit4 4
35 1165.9404 1185.5715 fit4 5
36 1138.6881 1153.4114 fit4 6
37 1035.9053 1050.6286 fit4 7
38 640.6757 665.2145 fit4 8
39 1004.7217 1024.3527 fit4 9
40 1143.9437 1163.5747 fit4 10
41 888.3168 912.8556 fit4 11
42 1096.9640 1116.5950 fit4 12
43 1172.5120 1192.1430 fit4 13
44 1190.0824 1209.7134 fit4 14
45 1308.6640 1333.2028 fit4 15
Code
logLik(fit3, item = 8)
Output
'log Lik.' -312.7227 (df=7)
Code
logLik(fit4, item = 8)
Output
'log Lik.' -315.3379 (df=5)
Code
predict(fit1, item = 5, group = c(0, 1), match = 0)
Output
item match group prob
1 Item5 0 0 0.7851726
2 Item5 0 1 0.5624891
Code
fit9$difPur
Output
Item1 Item2 Item3 Item4 Item5 Item6
Step0 0 0 0 0 1 1
Step1 0 0 0 0 1 0
Step2 0 0 0 0 1 0
Code
fit14$difPur
Output
Item1 Item2 Item3 Item4 Item5 Item6 Item7 Item8 Item9 Item10 Item11
Step0 0 0 0 0 1 0 0 1 0 0 1
Step1 1 0 0 0 1 0 0 1 0 0 1
Step2 0 0 0 0 1 0 0 1 0 0 1
Step3 1 0 0 0 1 0 0 1 0 0 1
Step4 0 0 0 0 1 0 0 1 0 0 1
Step5 1 0 0 0 1 0 0 1 0 0 1
Step6 0 0 0 0 1 0 0 1 0 0 1
Step7 1 0 0 0 1 0 0 1 0 0 1
Step8 0 0 0 0 1 0 0 1 0 0 1
Step9 1 0 0 0 1 0 0 1 0 0 1
Step10 0 0 0 0 1 0 0 1 0 0 1
Item12
Step0 0
Step1 0
Step2 0
Step3 0
Step4 0
Step5 0
Step6 0
Step7 0
Step8 0
Step9 0
Step10 0
Code
predict(fitex3, match = rep(c(-1, 0, 1), 2), group = rep(c(0, 1), each = 3),
item = 1, interval = "confidence")
Output
item match group prob lwr.conf upr.conf
1 Item6A_9 -1 0 0.6785773 0.6188773 0.7382773
2 Item6A_9 0 0 0.7773050 0.7269066 0.8277034
3 Item6A_9 1 0 0.8781427 0.8114574 0.9448281
4 Item6A_9 -1 1 0.7802954 0.7186997 0.8418912
5 Item6A_9 0 1 0.8431037 0.7869870 0.8992204
6 Item6A_9 1 1 0.9290799 0.8549497 1.0032100
testing paper code - R Journal 2020 - special cases (not included)
Code
head(df[, c(1:5, 16)])
Output
Item1 Item2 Item3 Item4 Item5 group
1 0 1 1 1 1 0
2 0 1 1 0 1 0
3 0 1 0 0 1 0
4 1 1 1 0 1 0
5 1 1 0 1 1 0
6 0 1 0 0 1 0
Code
coef(fit12b, item = 14)
Output
$Item14
a b c d
estimate 0.9294854 1.329411 0.06799893 1
CI2.5 NA NA NA NA
CI97.5 NA NA NA NA
Code
fit12c
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression Wald test statistics
based on 4PL model
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 43.3878 0.0000 ***
Item2 155.8403 0.0000 ***
Item3 153.4009 0.0000 ***
Item4 111.2974 0.0000 ***
Item5 533.3646 0.0000 ***
Item6 107.9470 0.0000 ***
Item7 11.5948 0.0206 *
Item8 53.3736 0.0000 ***
Item9 453.2297 0.0000 ***
Item10 145.8386 0.0000 ***
Item11 910.1895 0.0000 ***
Item12 68.7672 0.0000 ***
Item13 28.3833 0.0000 ***
Item14 NA NA
Item15 96.6385 0.0000 ***
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 9.4877 (significance level: 0.05)
Items detected as DIF items:
Item1
Item2
Item3
Item4
Item5
Item6
Item7
Item8
Item9
Item10
Item11
Item12
Item13
Item15
Code
fit12d
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 4PL model
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 2.0935 0.7186
Item2 0.7896 0.9398
Item3 17.6515 0.0014 **
Item4 4.8403 0.3041
Item5 13.1573 0.0105 *
Item6 3.8714 0.4237
Item7 1.5778 0.8128
Item8 10.9093 0.0276 *
Item9 0.6097 0.9620
Item10 2.4853 0.6473
Item11 26.6071 0.0000 ***
Item12 5.2838 0.2594
Item13 2.8016 0.5916
Item14 NA NA
Item15 12.8104 0.0122 *
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 9.4877 (significance level: 0.05)
Items detected as DIF items:
Item3
Item5
Item8
Item11
Item15
Code
(fit15a <- difNLR(DataDIF[, -c(5, 8, 11, 15)], groupDIF, focal.name = 1, model = "4PL",
type = "all"))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 4PL model
Parameters were estimated using non-linear least squares
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 9.1344 0.0578 .
Item2 1.1944 0.8790
Item3 4.5018 0.3423
Item4 2.9407 0.5678
Item6 6.2007 0.1847
Item7 0.8918 0.9257
Item9 1.2215 0.8746
Item10 1.6226 0.8047
Item12 7.2585 0.1228
Item13 5.6248 0.2290
Item14 1.5104 0.8248
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 9.4877 (significance level: 0.05)
None of items is detected as DIF
Code
(fit15b <- difNLR(DataDIF[, -c(5, 8, 11, 15)], groupDIF, focal.name = 1, model = "4PL",
type = "all", purify = TRUE))
Output
Detection of all types of differential item functioning
using the generalized logistic regression model
Generalized logistic regression likelihood ratio chi-square statistics
based on 4PL model
Parameters were estimated using non-linear least squares
Item purification was applied with 0 iteration.
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 9.1344 0.0578 .
Item2 1.1944 0.8790
Item3 4.5018 0.3423
Item4 2.9407 0.5678
Item6 6.2007 0.1847
Item7 0.8918 0.9257
Item9 1.2215 0.8746
Item10 1.6226 0.8047
Item12 7.2585 0.1228
Item13 5.6248 0.2290
Item14 1.5104 0.8248
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Detection thresholds: 9.4877 (significance level: 0.05)
None of items is detected as DIF
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