![[Experimental]](figures/lifecycle-experimental.svg)
Usage
# S3 method for mmrm_tmb
coef(object, complete = TRUE, ...)
# S3 method for mmrm_tmb
fitted(object, ...)
# S3 method for mmrm_tmb
model.frame(formula, full = FALSE, ...)
# S3 method for mmrm_tmb
logLik(object, ...)
# S3 method for mmrm_tmb
formula(x, ...)
# S3 method for mmrm_tmb
vcov(object, complete = TRUE, ...)
# S3 method for mmrm_tmb
VarCorr(x, sigma = NA, ...)
# S3 method for mmrm_tmb
deviance(object, ...)
# S3 method for mmrm_tmb
AIC(object, corrected = FALSE, ..., k = 2)
# S3 method for mmrm_tmb
BIC(object, ...)
# S3 method for mmrm_tmb
print(x, ...)Arguments
- object
(
mmrm_tmb)
the fitted MMRM object.- complete
(
flag)
whether to include potential non-estimable coefficients.- ...
not used.
- formula
(
mmrm_tmb)
same asobject.- full
(
flag)
whether to include subject, visit and weight variables.- x
(
mmrm_tmb)
same asobject.- sigma
cannot be used (this parameter does not exist in MMRM).
- corrected
(
flag)
whether corrected AIC should be calculated.- k
(
number)
the penalty per parameter to be used; defaultk = 2is the classical AIC.
Functions
coef(mmrm_tmb): obtains the estimated coefficients.fitted(mmrm_tmb): obtains the fitted values.model.frame(mmrm_tmb): obtains the model frame.logLik(mmrm_tmb): obtains the attained log likelihood value.formula(mmrm_tmb): obtains the used formula.vcov(mmrm_tmb): obtains the variance-covariance matrix estimate for the coefficients.VarCorr(mmrm_tmb): obtains the variance-covariance matrix estimate for the residuals.deviance(mmrm_tmb): obtains the deviance, which is defined here as twice the negative log likelihood, which can either be integrated over the coefficients for REML fits or the usual one for ML fits.AIC(mmrm_tmb): obtains the Akaike Information Criterion, where the degrees of freedom are the number of variance parameters (n_theta). Ifcorrected, then this is multiplied withm / (m - n_theta - 1)wheremis the number of observations minus the number of coefficients, orn_theta + 2if it is smaller than that (Hurvich and Tsai 1989; Burnham and Anderson 1998) .BIC(mmrm_tmb): obtains the Bayesian Information Criterion, which is using the natural logarithm of the number of subjects for the penalty parameterk.print(mmrm_tmb): prints the object.
References
Hurvich CM, Tsai C (1989). “Regression and time series model selection in small samples.” Biometrika, 76(2), 297--307. doi:10.2307/2336663 .
Burnham KP, Anderson DR (1998). “Practical use of the information-theoretic approach.” In Model selection and inference, 75--117. Springer. doi:10.1007/978-1-4757-2917-7_3 .
Examples
formula <- FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID)
object <- fit_mmrm(formula, fev_data, weights = rep(1, nrow(fev_data)))
# Estimated coefficients:
coef(object)
#> (Intercept) RACEBlack or African American
#> 30.77747548 1.53049977
#> RACEWhite SEXFemale
#> 5.64356535 0.32606192
#> ARMCDTRT AVISITVIS2
#> 3.77423004 4.83958845
#> AVISITVIS3 AVISITVIS4
#> 10.34211288 15.05389826
#> ARMCDTRT:AVISITVIS2 ARMCDTRT:AVISITVIS3
#> -0.04192625 -0.69368537
#> ARMCDTRT:AVISITVIS4
#> 0.62422703
# Fitted values:
fitted(object)
#> 2 4 6 7 8 10 12 13
#> 41.20593 52.08639 35.61706 41.11959 45.83137 37.47363 47.68794 34.87777
#> 14 16 17 19 20 23 25 26
#> 39.67543 50.55589 32.30798 42.65009 47.36187 42.65009 31.10354 35.94313
#> 28 29 30 31 32 33 34 36
#> 46.15744 32.30798 37.14756 42.65009 47.36187 40.19527 44.99293 55.87340
#> 39 41 42 43 44 45 46 47
#> 42.97615 34.87777 39.67543 44.52619 50.55589 30.77748 35.61706 41.11959
#> 51 52 55 59 60 62 64 65
#> 49.84370 55.87340 42.65009 41.11959 45.83137 35.94313 46.15744 36.74710
#> 68 69 70 72 73 74 75 76
#> 51.80100 34.55171 39.34937 50.22983 34.55171 39.34937 44.20013 50.22983
#> 78 79 82 83 84 85 86 87
#> 45.31900 50.16976 44.99293 49.84370 55.87340 40.19527 44.99293 49.84370
#> 88 89 90 91 93 94 95 96
#> 55.87340 32.63404 37.47363 42.97615 40.52133 45.31900 50.16976 56.19946
#> 97 98 99 100 101 102 103 104
#> 40.52133 45.31900 50.16976 56.19946 40.52133 45.31900 50.16976 56.19946
#> 105 107 108 109 110 111 112 113
#> 36.40827 46.05669 52.08639 36.08221 40.87987 45.73063 51.76033 36.08221
#> 114 116 117 118 119 120 121 123
#> 40.87987 51.76033 34.87777 39.67543 44.52619 50.55589 31.10354 41.44565
#> 125 128 129 130 132 133 134 135
#> 36.08221 51.76033 36.08221 40.87987 51.76033 31.10354 35.94313 41.44565
#> 136 137 138 140 142 144 145 146
#> 46.15744 36.40827 41.20593 52.08639 39.67543 50.55589 36.74710 41.58669
#> 147 148 149 151 153 155 156 157
#> 47.08922 51.80100 31.10354 41.44565 31.10354 41.44565 46.15744 32.30798
#> 158 159 162 163 164 165 168 169
#> 37.14756 42.65009 37.14756 42.65009 47.36187 32.30798 47.36187 36.40827
#> 170 171 172 173 177 178 179 180
#> 41.20593 46.05669 52.08639 36.08221 31.10354 35.94313 41.44565 46.15744
#> 181 182 183 185 186 187 190 191
#> 30.77748 35.61706 41.11959 34.55171 39.34937 44.20013 37.14756 42.65009
#> 193 194 195 197 198 199 201 202
#> 36.74710 41.58669 47.08922 34.87777 39.67543 44.52619 36.40827 41.20593
#> 204 206 208 209 210 217 218 219
#> 52.08639 41.20593 52.08639 36.42104 41.26063 34.55171 39.34937 44.20013
#> 221 224 226 227 228 230 231 233
#> 32.30798 47.36187 40.87987 45.73063 51.76033 45.31900 50.16976 40.52133
#> 235 236 237 238 239 240 241 242
#> 50.16976 56.19946 36.40827 41.20593 46.05669 52.08639 34.55171 39.34937
#> 244 246 250 251 252 253 254 256
#> 50.22983 35.94313 41.58669 47.08922 51.80100 32.63404 37.47363 47.68794
#> 257 258 259 260 261 262 263 264
#> 34.87777 39.67543 44.52619 50.55589 34.55171 39.34937 44.20013 50.22983
#> 265 266 267 268 269 270 273 274
#> 40.52133 45.31900 50.16976 56.19946 40.52133 45.31900 32.30798 37.14756
#> 275 276 277 278 280 281 282 283
#> 42.65009 47.36187 32.30798 37.14756 47.36187 34.55171 39.34937 44.20013
#> 284 285 286 287 291 292 293 295
#> 50.22983 34.87777 39.67543 44.52619 46.76315 51.47494 36.74710 47.08922
#> 296 298 299 300 301 304 305 306
#> 51.80100 41.26063 46.76315 51.47494 40.19527 55.87340 34.55171 39.34937
#> 307 308 310 311 312 316 317 318
#> 44.20013 50.22983 45.31900 50.16976 56.19946 46.15744 34.55171 39.34937
#> 319 322 323 324 325 327 328 329
#> 44.20013 41.26063 46.76315 51.47494 32.63404 42.97615 47.68794 40.19527
#> 330 331 332 336 339 340 341 342
#> 44.99293 49.84370 55.87340 46.15744 41.44565 46.15744 36.40827 41.20593
#> 343 344 345 347 349 351 352 353
#> 46.05669 52.08639 36.42104 46.76315 32.63404 42.97615 47.68794 32.30798
#> 354 355 356 357 363 364 365 367
#> 37.14756 42.65009 47.36187 30.77748 50.16976 56.19946 40.52133 50.16976
#> 368 370 371 372 373 375 376 378
#> 56.19946 37.14756 42.65009 47.36187 31.10354 41.44565 46.15744 35.94313
#> 379 381 382 384 385 386 388 389
#> 41.44565 32.63404 37.47363 47.68794 40.19527 44.99293 55.87340 30.77748
#> 390 391 392 394 397 398 399 402
#> 35.61706 41.11959 45.83137 37.14756 32.63404 37.47363 42.97615 44.99293
#> 403 405 406 407 408 409 410 411
#> 49.84370 31.10354 35.94313 41.44565 46.15744 31.10354 35.94313 41.44565
#> 413 415 416 418 419 421 422 423
#> 32.63404 42.97615 47.68794 41.20593 46.05669 34.87777 39.67543 44.52619
#> 424 427 428 429 430 431 432 434
#> 50.55589 47.08922 51.80100 40.19527 44.99293 49.84370 55.87340 37.47363
#> 435 436 438 439 444 445 447 449
#> 42.97615 47.68794 39.34937 44.20013 51.80100 32.63404 42.97615 30.77748
#> 450 451 453 454 455 456 457 458
#> 35.61706 41.11959 32.30798 37.14756 42.65009 47.36187 31.10354 35.94313
#> 459 461 463 464 465 469 470 471
#> 41.44565 36.08221 45.73063 51.76033 32.30798 31.10354 35.94313 41.44565
#> 473 474 477 484 487 489 490 491
#> 40.19527 44.99293 31.10354 51.76033 41.44565 36.74710 41.58669 47.08922
#> 494 495 496 497 498 499 501 502
#> 39.67543 44.52619 50.55589 36.42104 41.26063 46.76315 34.55171 39.34937
#> 504 505 508 509 510 511 512 513
#> 50.22983 36.40827 52.08639 34.87777 39.67543 44.52619 50.55589 40.19527
#> 518 519 521 522 523 524 526 527
#> 41.26063 46.76315 31.10354 35.94313 41.44565 46.15744 35.61706 41.11959
#> 528 530 531 532 534 535 536 537
#> 45.83137 41.26063 46.76315 51.47494 37.14756 42.65009 47.36187 32.30798
#> 538 539 540 541 544 545 546 547
#> 37.14756 42.65009 47.36187 40.52133 56.19946 34.87777 39.67543 44.52619
#> 548 549 550 551 555 556 557 558
#> 50.55589 34.55171 39.34937 44.20013 44.52619 50.55589 32.63404 37.47363
#> 560 562 564 569 570 572 573 574
#> 47.68794 44.99293 55.87340 36.40827 41.20593 52.08639 31.10354 35.94313
#> 575 576 577 578 579 582 583 584
#> 41.44565 46.15744 32.30798 37.14756 42.65009 39.67543 44.52619 50.55589
#> 585 586 587 590 591 593 594 595
#> 31.10354 35.94313 41.44565 40.87987 45.73063 31.10354 35.94313 41.44565
#> 596 599 600 601 602 604 606 608
#> 46.15744 46.76315 51.47494 31.10354 35.94313 46.15744 39.67543 50.55589
#> 609 610 611 612 613 614 616 617
#> 32.30798 37.14756 42.65009 47.36187 32.63404 37.47363 47.68794 34.55171
#> 619 620 621 622 623 624 625 628
#> 44.20013 50.22983 36.42104 41.26063 46.76315 51.47494 36.08221 51.76033
#> 630 631 632 633 634 638 639 640
#> 40.87987 45.73063 51.76033 34.87777 39.67543 39.34937 44.20013 50.22983
#> 642 645 648 650 652 654 655 656
#> 37.47363 30.77748 45.83137 41.26063 51.47494 37.47363 42.97615 47.68794
#> 657 661 665 666 668 669 670 671
#> 40.52133 34.55171 32.30798 37.14756 47.36187 32.63404 37.47363 42.97615
#> 673 674 678 679 680 682 684 685
#> 36.74710 41.58669 45.31900 50.16976 56.19946 35.94313 46.15744 34.87777
#> 686 687 689 690 691 693 694 695
#> 39.67543 44.52619 40.52133 45.31900 50.16976 36.40827 41.20593 46.05669
#> 697 698 700 701 702 704 707 709
#> 32.63404 37.47363 47.68794 36.08221 40.87987 51.76033 42.65009 32.30798
#> 712 713 715 716 717 718 719 721
#> 47.36187 40.19527 49.84370 55.87340 30.77748 35.61706 41.11959 31.10354
#> 723 724 727 728 729 730 734 736
#> 41.44565 46.15744 46.05669 52.08639 36.40827 41.20593 45.31900 56.19946
#> 738 739 740 741 742 744 745 749
#> 37.47363 42.97615 47.68794 36.42104 41.26063 51.47494 32.63404 32.63404
#> 751 753 754 755 757 758 759 760
#> 42.97615 34.87777 39.67543 44.52619 36.40827 41.20593 46.05669 52.08639
#> 761 764 765 766 767 768 770 771
#> 40.19527 55.87340 40.52133 45.31900 50.16976 56.19946 35.61706 41.11959
#> 772 773 774 775 776 778 779 781
#> 45.83137 32.30798 37.14756 42.65009 47.36187 45.31900 50.16976 30.77748
#> 782 784 788 789 790 792 797 798
#> 35.61706 45.83137 47.36187 36.08221 40.87987 51.76033 32.30798 37.14756
#> 800
#> 47.36187
# Model frame:
model.frame(object)
#> FEV1 RACE SEX ARMCD AVISIT
#> 2 39.97105 Black or African American Female TRT VIS2
#> 4 20.48379 Black or African American Female TRT VIS4
#> 6 31.45522 Asian Male PBO VIS2
#> 7 36.87889 Asian Male PBO VIS3
#> 8 48.80809 Asian Male PBO VIS4
#> 10 35.98699 Black or African American Female PBO VIS2
#> 12 37.16444 Black or African American Female PBO VIS4
#> 13 33.89229 Asian Female TRT VIS1
#> 14 33.74637 Asian Female TRT VIS2
#> 16 54.45055 Asian Female TRT VIS4
#> 17 32.31386 Black or African American Male PBO VIS1
#> 19 46.79361 Black or African American Male PBO VIS3
#> 20 41.71154 Black or African American Male PBO VIS4
#> 23 39.02423 Black or African American Male PBO VIS3
#> 25 31.93050 Asian Female PBO VIS1
#> 26 32.90947 Asian Female PBO VIS2
#> 28 48.28031 Asian Female PBO VIS4
#> 29 32.23021 Black or African American Male PBO VIS1
#> 30 35.91080 Black or African American Male PBO VIS2
#> 31 45.54898 Black or African American Male PBO VIS3
#> 32 53.02877 Black or African American Male PBO VIS4
#> 33 47.16898 White Male TRT VIS1
#> 34 46.64287 White Male TRT VIS2
#> 36 58.09713 White Male TRT VIS4
#> 39 44.97613 Black or African American Female PBO VIS3
#> 41 44.32755 Asian Female TRT VIS1
#> 42 38.97813 Asian Female TRT VIS2
#> 43 43.72862 Asian Female TRT VIS3
#> 44 46.43393 Asian Female TRT VIS4
#> 45 40.34576 Asian Male PBO VIS1
#> 46 42.76568 Asian Male PBO VIS2
#> 47 40.11155 Asian Male PBO VIS3
#> 51 53.31791 White Male TRT VIS3
#> 52 56.07641 White Male TRT VIS4
#> 55 41.90837 Black or African American Male PBO VIS3
#> 59 34.65663 Asian Male PBO VIS3
#> 60 39.07791 Asian Male PBO VIS4
#> 62 35.89612 Asian Female PBO VIS2
#> 64 47.67264 Asian Female PBO VIS4
#> 65 22.65440 White Female PBO VIS1
#> 68 40.85376 White Female PBO VIS4
#> 69 32.60048 Asian Male TRT VIS1
#> 70 33.64329 Asian Male TRT VIS2
#> 72 40.92278 Asian Male TRT VIS4
#> 73 32.14831 Asian Male TRT VIS1
#> 74 46.43604 Asian Male TRT VIS2
#> 75 41.34973 Asian Male TRT VIS3
#> 76 66.30382 Asian Male TRT VIS4
#> 78 47.95358 White Female TRT VIS2
#> 79 53.97364 White Female TRT VIS3
#> 82 56.64544 White Male TRT VIS2
#> 83 49.70872 White Male TRT VIS3
#> 84 60.40497 White Male TRT VIS4
#> 85 45.98525 White Male TRT VIS1
#> 86 51.90911 White Male TRT VIS2
#> 87 41.50787 White Male TRT VIS3
#> 88 53.42727 White Male TRT VIS4
#> 89 23.86859 Black or African American Female PBO VIS1
#> 90 35.98563 Black or African American Female PBO VIS2
#> 91 43.60626 Black or African American Female PBO VIS3
#> 93 29.59773 White Female TRT VIS1
#> 94 35.50688 White Female TRT VIS2
#> 95 55.42944 White Female TRT VIS3
#> 96 52.10530 White Female TRT VIS4
#> 97 31.69644 White Female TRT VIS1
#> 98 32.16159 White Female TRT VIS2
#> 99 51.04735 White Female TRT VIS3
#> 100 55.85987 White Female TRT VIS4
#> 101 49.11706 White Female TRT VIS1
#> 102 49.25544 White Female TRT VIS2
#> 103 51.72211 White Female TRT VIS3
#> 104 69.99128 White Female TRT VIS4
#> 105 22.07169 Black or African American Female TRT VIS1
#> 107 46.08393 Black or African American Female TRT VIS3
#> 108 52.42288 Black or African American Female TRT VIS4
#> 109 37.69466 Black or African American Male TRT VIS1
#> 110 44.59400 Black or African American Male TRT VIS2
#> 111 52.08897 Black or African American Male TRT VIS3
#> 112 58.22961 Black or African American Male TRT VIS4
#> 113 37.22824 Black or African American Male TRT VIS1
#> 114 34.39863 Black or African American Male TRT VIS2
#> 116 36.34012 Black or African American Male TRT VIS4
#> 117 45.44182 Asian Female TRT VIS1
#> 118 41.54847 Asian Female TRT VIS2
#> 119 43.92172 Asian Female TRT VIS3
#> 120 61.83243 Asian Female TRT VIS4
#> 121 27.25656 Asian Female PBO VIS1
#> 123 45.65133 Asian Female PBO VIS3
#> 125 33.19334 Black or African American Male TRT VIS1
#> 128 41.66826 Black or African American Male TRT VIS4
#> 129 27.12753 Black or African American Male TRT VIS1
#> 130 31.74858 Black or African American Male TRT VIS2
#> 132 41.60000 Black or African American Male TRT VIS4
#> 133 39.45250 Asian Female PBO VIS1
#> 134 32.61823 Asian Female PBO VIS2
#> 135 34.62445 Asian Female PBO VIS3
#> 136 45.90515 Asian Female PBO VIS4
#> 137 36.17780 Black or African American Female TRT VIS1
#> 138 39.79796 Black or African American Female TRT VIS2
#> 140 50.08272 Black or African American Female TRT VIS4
#> 142 44.64316 Asian Female TRT VIS2
#> 144 39.73529 Asian Female TRT VIS4
#> 145 34.06164 White Female PBO VIS1
#> 146 40.18592 White Female PBO VIS2
#> 147 41.17584 White Female PBO VIS3
#> 148 57.76669 White Female PBO VIS4
#> 149 38.18460 Asian Female PBO VIS1
#> 151 47.19893 Asian Female PBO VIS3
#> 153 37.32785 Asian Female PBO VIS1
#> 155 43.16048 Asian Female PBO VIS3
#> 156 41.40349 Asian Female PBO VIS4
#> 157 30.15733 Black or African American Male PBO VIS1
#> 158 35.84353 Black or African American Male PBO VIS2
#> 159 40.95250 Black or African American Male PBO VIS3
#> 162 41.37928 Black or African American Male PBO VIS2
#> 163 50.17316 Black or African American Male PBO VIS3
#> 164 45.35226 Black or African American Male PBO VIS4
#> 165 39.06491 Black or African American Male PBO VIS1
#> 168 42.11960 Black or African American Male PBO VIS4
#> 169 29.81042 Black or African American Female TRT VIS1
#> 170 42.57055 Black or African American Female TRT VIS2
#> 171 47.81652 Black or African American Female TRT VIS3
#> 172 68.06024 Black or African American Female TRT VIS4
#> 173 35.62071 Black or African American Male TRT VIS1
#> 177 33.89134 Asian Female PBO VIS1
#> 178 36.42808 Asian Female PBO VIS2
#> 179 37.57519 Asian Female PBO VIS3
#> 180 58.46873 Asian Female PBO VIS4
#> 181 19.54516 Asian Male PBO VIS1
#> 182 31.13541 Asian Male PBO VIS2
#> 183 40.89955 Asian Male PBO VIS3
#> 185 22.18809 Asian Male TRT VIS1
#> 186 41.05857 Asian Male TRT VIS2
#> 187 37.32452 Asian Male TRT VIS3
#> 190 43.12432 Black or African American Male PBO VIS2
#> 191 41.99349 Black or African American Male PBO VIS3
#> 193 44.03080 White Female PBO VIS1
#> 194 38.66417 White Female PBO VIS2
#> 195 53.45993 White Female PBO VIS3
#> 197 29.81948 Asian Female TRT VIS1
#> 198 30.43859 Asian Female TRT VIS2
#> 199 40.18095 Asian Female TRT VIS3
#> 201 26.78578 Black or African American Female TRT VIS1
#> 202 34.55115 Black or African American Female TRT VIS2
#> 204 40.06421 Black or African American Female TRT VIS4
#> 206 43.09329 Black or African American Female TRT VIS2
#> 208 45.71567 Black or African American Female TRT VIS4
#> 209 40.74992 White Male PBO VIS1
#> 210 44.74635 White Male PBO VIS2
#> 217 40.14674 Asian Male TRT VIS1
#> 218 48.75859 Asian Male TRT VIS2
#> 219 46.43462 Asian Male TRT VIS3
#> 221 29.33990 Black or African American Male PBO VIS1
#> 224 47.93165 Black or African American Male PBO VIS4
#> 226 41.11632 Black or African American Male TRT VIS2
#> 227 47.05889 Black or African American Male TRT VIS3
#> 228 52.24599 Black or African American Male TRT VIS4
#> 230 54.14236 White Female TRT VIS2
#> 231 50.44618 White Female TRT VIS3
#> 233 37.53657 White Female TRT VIS1
#> 235 49.45840 White Female TRT VIS3
#> 236 59.12866 White Female TRT VIS4
#> 237 40.31268 Black or African American Female TRT VIS1
#> 238 39.66049 Black or African American Female TRT VIS2
#> 239 50.89726 Black or African American Female TRT VIS3
#> 240 56.13116 Black or African American Female TRT VIS4
#> 241 32.82981 Asian Male TRT VIS1
#> 242 46.53837 Asian Male TRT VIS2
#> 244 51.81265 Asian Male TRT VIS4
#> 246 29.91939 Asian Female PBO VIS2
#> 250 51.05656 White Female PBO VIS2
#> 251 50.50059 White Female PBO VIS3
#> 252 64.11388 White Female PBO VIS4
#> 253 32.21843 Black or African American Female PBO VIS1
#> 254 29.64732 Black or African American Female PBO VIS2
#> 256 45.09919 Black or African American Female PBO VIS4
#> 257 39.75659 Asian Female TRT VIS1
#> 258 37.28894 Asian Female TRT VIS2
#> 259 44.80145 Asian Female TRT VIS3
#> 260 65.95920 Asian Female TRT VIS4
#> 261 33.43439 Asian Male TRT VIS1
#> 262 33.57042 Asian Male TRT VIS2
#> 263 39.91543 Asian Male TRT VIS3
#> 264 49.57098 Asian Male TRT VIS4
#> 265 38.91634 White Female TRT VIS1
#> 266 36.69011 White Female TRT VIS2
#> 267 45.66665 White Female TRT VIS3
#> 268 52.07431 White Female TRT VIS4
#> 269 42.21411 White Female TRT VIS1
#> 270 45.02901 White Female TRT VIS2
#> 273 30.98338 Black or African American Male PBO VIS1
#> 274 44.72932 Black or African American Male PBO VIS2
#> 275 40.68711 Black or African American Male PBO VIS3
#> 276 34.71530 Black or African American Male PBO VIS4
#> 277 27.30752 Black or African American Male PBO VIS1
#> 278 37.31585 Black or African American Male PBO VIS2
#> 280 44.83000 Black or African American Male PBO VIS4
#> 281 32.93042 Asian Male TRT VIS1
#> 282 44.91911 Asian Male TRT VIS2
#> 283 45.68636 Asian Male TRT VIS3
#> 284 65.98800 Asian Male TRT VIS4
#> 285 46.60130 Asian Female TRT VIS1
#> 286 40.89786 Asian Female TRT VIS2
#> 287 46.66708 Asian Female TRT VIS3
#> 291 43.83270 White Male PBO VIS3
#> 292 44.11604 White Male PBO VIS4
#> 293 38.29612 White Female PBO VIS1
#> 295 51.38570 White Female PBO VIS3
#> 296 56.20979 White Female PBO VIS4
#> 298 43.45819 White Male PBO VIS2
#> 299 38.38741 White Male PBO VIS3
#> 300 56.42818 White Male PBO VIS4
#> 301 39.05050 White Male TRT VIS1
#> 304 54.09200 White Male TRT VIS4
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#> 311 46.45368 White Female TRT VIS3
#> 312 64.97366 White Female TRT VIS4
#> 316 43.97847 Asian Female PBO VIS4
#> 317 35.33466 Asian Male TRT VIS1
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#> 319 41.27633 Asian Male TRT VIS3
#> 322 39.83058 White Male PBO VIS2
#> 323 43.49673 White Male PBO VIS3
#> 324 44.06114 White Male PBO VIS4
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#> 329 36.61831 White Male TRT VIS1
#> 330 42.09272 White Male TRT VIS2
#> 331 50.69556 White Male TRT VIS3
#> 332 51.72563 White Male TRT VIS4
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#> 407 42.52157 Asian Female PBO VIS3
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#> 428 69.36099 White Female PBO VIS4
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#> 430 43.72376 White Male TRT VIS2
#> 431 49.47601 White Male TRT VIS3
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#> 522 30.33517 Asian Female PBO VIS2
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#> 532 56.49347 White Male PBO VIS4
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#> 600 63.44051 White Male PBO VIS4
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model.frame(object, full = TRUE)
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#> 772 61.03588 Asian Male PBO VIS4 PT193 1
#> 773 20.36749 Black or African American Male PBO VIS1 PT194 1
#> 774 35.22480 Black or African American Male PBO VIS2 PT194 1
#> 775 37.42847 Black or African American Male PBO VIS3 PT194 1
#> 776 30.20501 Black or African American Male PBO VIS4 PT194 1
#> 778 49.12862 White Female TRT VIS2 PT195 1
#> 779 47.31234 White Female TRT VIS3 PT195 1
#> 781 19.28388 Asian Male PBO VIS1 PT196 1
#> 782 30.00682 Asian Male PBO VIS2 PT196 1
#> 784 49.21768 Asian Male PBO VIS4 PT196 1
#> 788 40.13353 Black or African American Male PBO VIS4 PT197 1
#> 789 42.34534 Black or African American Male TRT VIS1 PT198 1
#> 790 52.32575 Black or African American Male TRT VIS2 PT198 1
#> 792 69.26254 Black or African American Male TRT VIS4 PT198 1
#> 797 35.70341 Black or African American Male PBO VIS1 PT200 1
#> 798 41.64454 Black or African American Male PBO VIS2 PT200 1
#> 800 54.25081 Black or African American Male PBO VIS4 PT200 1
# Log likelihood given the estimated parameters:
logLik(object)
#> [1] -1693.225
# Formula which was used:
formula(object)
#> FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID)
#> <environment: 0x5635e0254b70>
# Variance-covariance matrix estimate for coefficients:
vcov(object)
#> (Intercept) RACEBlack or African American
#> (Intercept) 0.7859971 -0.226212328
#> RACEBlack or African American -0.2262123 0.389969478
#> RACEWhite -0.1771113 0.181466304
#> SEXFemale -0.1684152 0.031537926
#> ARMCDTRT -0.5674809 0.028374129
#> AVISITVIS2 -0.4227565 0.002972514
#> AVISITVIS3 -0.5231223 0.010825469
#> AVISITVIS4 -0.4406442 0.002205681
#> ARMCDTRT:AVISITVIS2 0.4225282 0.005382569
#> ARMCDTRT:AVISITVIS3 0.5218971 0.011420575
#> ARMCDTRT:AVISITVIS4 0.4489247 -0.012589283
#> RACEWhite SEXFemale ARMCDTRT
#> (Intercept) -0.177111308 -0.168415217 -0.567480906
#> RACEBlack or African American 0.181466304 0.031537926 0.028374129
#> RACEWhite 0.443035801 0.023364777 -0.042968995
#> SEXFemale 0.023364777 0.282971189 0.001814594
#> ARMCDTRT -0.042968995 0.001814594 1.153791725
#> AVISITVIS2 -0.003149280 0.006471853 0.419528600
#> AVISITVIS3 -0.002952986 0.006771404 0.517277529
#> AVISITVIS4 -0.008230720 0.004088901 0.440653554
#> ARMCDTRT:AVISITVIS2 0.013485683 -0.016801299 -0.845758354
#> ARMCDTRT:AVISITVIS3 0.006720617 -0.024696304 -1.044355829
#> ARMCDTRT:AVISITVIS4 0.002967665 -0.009038640 -0.877606881
#> AVISITVIS2 AVISITVIS3 AVISITVIS4
#> (Intercept) -0.422756455 -0.523122299 -0.440644229
#> RACEBlack or African American 0.002972514 0.010825469 0.002205681
#> RACEWhite -0.003149280 -0.002952986 -0.008230720
#> SEXFemale 0.006471853 0.006771404 0.004088901
#> ARMCDTRT 0.419528600 0.517277529 0.440653554
#> AVISITVIS2 0.642749706 0.399048940 0.368340113
#> AVISITVIS3 0.399048940 0.676823960 0.401800094
#> AVISITVIS4 0.368340113 0.401800094 1.723478787
#> ARMCDTRT:AVISITVIS2 -0.643020114 -0.399203255 -0.368624024
#> ARMCDTRT:AVISITVIS3 -0.399238901 -0.676484876 -0.401792995
#> ARMCDTRT:AVISITVIS4 -0.368506585 -0.402167824 -1.723586879
#> ARMCDTRT:AVISITVIS2 ARMCDTRT:AVISITVIS3
#> (Intercept) 0.422528163 0.521897062
#> RACEBlack or African American 0.005382569 0.011420575
#> RACEWhite 0.013485683 0.006720617
#> SEXFemale -0.016801299 -0.024696304
#> ARMCDTRT -0.845758354 -1.044355829
#> AVISITVIS2 -0.643020114 -0.399238901
#> AVISITVIS3 -0.399203255 -0.676484876
#> AVISITVIS4 -0.368624024 -0.401792995
#> ARMCDTRT:AVISITVIS2 1.275359305 0.805849821
#> ARMCDTRT:AVISITVIS3 0.805849821 1.410501907
#> ARMCDTRT:AVISITVIS4 0.728711516 0.796418986
#> ARMCDTRT:AVISITVIS4
#> (Intercept) 0.448924745
#> RACEBlack or African American -0.012589283
#> RACEWhite 0.002967665
#> SEXFemale -0.009038640
#> ARMCDTRT -0.877606881
#> AVISITVIS2 -0.368506585
#> AVISITVIS3 -0.402167824
#> AVISITVIS4 -1.723586879
#> ARMCDTRT:AVISITVIS2 0.728711516
#> ARMCDTRT:AVISITVIS3 0.796418986
#> ARMCDTRT:AVISITVIS4 3.425654435
# Variance-covariance matrix estimate for residuals:
VarCorr(object)
#> VIS1 VIS2 VIS3 VIS4
#> VIS1 40.553664 14.396045 4.9747288 13.3866534
#> VIS2 14.396045 26.571483 2.7854661 7.4744790
#> VIS3 4.974729 2.785466 14.8978517 0.9082111
#> VIS4 13.386653 7.474479 0.9082111 95.5568420
# REML criterion (twice the negative log likelihood):
deviance(object)
#> [1] 3386.45
# AIC:
AIC(object)
#> [1] 3406.45
AIC(object, corrected = TRUE)
#> [1] 3406.877
# BIC:
BIC(object)
#> [1] 3439.282