![[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
predict(
object,
newdata,
se.fit = FALSE,
interval = c("none", "confidence", "prediction"),
level = 0.95,
nsim = 1000L,
...
)
# S3 method for mmrm_tmb
model.frame(
formula,
data,
include = c("subject_var", "visit_var", "group_var", "response_var"),
full,
na.action = "na.omit",
...
)
# S3 method for mmrm_tmb
model.matrix(object, data, include = NULL, ...)
# S3 method for mmrm_tmb
terms(x, include = "response_var", ...)
# 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, ...)
# S3 method for mmrm_tmb
residuals(object, type = c("response", "pearson", "normalized"), ...)
# S3 method for mmrm_tmb
simulate(
object,
nsim = 1,
seed = NULL,
newdata,
...,
method = c("conditional", "marginal")
)Arguments
- object
(
mmrm_tmb)
the fitted MMRM object.- complete
(
flag)
whether to include potential non-estimable coefficients.- ...
mostly not used; Exception is
model.matrix()passing...to the default method.- newdata
(
data.frame)
optional new data, otherwise data fromobjectis used.- se.fit
(
flag)
indicator if standard errors are required.- interval
(
string)
type of interval calculation. Can be abbreviated.- level
(
number)
tolerance/confidence level.- nsim
(
count)
number of simulations to use.- formula
(
mmrm_tmb)
same asobject.- data
(
data.frame)
object in which to construct the frame.- include
(
character)
names of variable types to include. Must beNULLor one or more ofc("subject_var", "visit_var", "group_var", "response_var").- full
(
flag)
indicator whether to return full model frame (deprecated).- na.action
(
string)
na action.- 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.- type
(
string)
unscaled (response),pearsonornormalized. Default isresponse, and this is the only type available for use with models with a spatial covariance structure.- seed
unused argument from
stats::simulate().- method
(
string)
simulation method to use. If "conditional", simulated values are sampled given the estimated covariance matrix ofobject. If "marginal", the variance of the estimated covariance matrix is taken into account.
Details
include argument controls the variables the returned model frame will include.
Possible options are "response_var", "subject_var", "visit_var" and "group_var", representing the
response variable, subject variable, visit variable or group variable.
character values in new data will always be factorized according to the data in the fit
to avoid mismatched in levels or issues in model.matrix.
Functions
coef(mmrm_tmb): obtains the estimated coefficients.fitted(mmrm_tmb): obtains the fitted values.predict(mmrm_tmb): predict conditional means for new data; optionally with standard errors and confidence or prediction intervals. Returns a vector of predictions ifse.fit == FALSEandinterval == "none"; otherwise it returns a data.frame with multiple columns and one row per input data row.model.frame(mmrm_tmb): obtains the model frame.model.matrix(mmrm_tmb): obtains the model matrix.terms(mmrm_tmb): obtains the terms object.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.residuals(mmrm_tmb): to obtain residuals - either unscaled ('response'), 'pearson' or 'normalized'.simulate(mmrm_tmb): simulate responses from a fitted model according to the simulationmethod, returning adata.frameof dimension[n, m]where n is the number of rows innewdata, and m is the numbernsimof simulated responses.
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 .
Gałecki A, Burzykowski T (2013). “Linear mixed-effects model.” In Linear mixed-effects models using R, 245--273. Springer.
See also
mmrm_methods, mmrm_tidiers for additional methods.
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
predict(object, newdata = fev_data)
#> 1 2 3 4 5 6 7 8
#> 32.61649 39.97105 45.88555 20.48379 28.03626 31.45522 36.87889 48.80809
#> 9 10 11 12 13 14 15 16
#> 30.81897 35.98699 42.80683 37.16444 33.89229 33.74637 44.09341 54.45055
#> 17 18 19 20 21 22 23 24
#> 32.31386 37.27959 46.79361 41.71154 31.09722 36.46963 39.02423 47.14083
#> 25 26 27 28 29 30 31 32
#> 31.93050 32.90947 41.36743 48.28031 32.23021 35.91080 45.54898 53.02877
#> 33 34 35 36 37 38 39 40
#> 47.16898 46.64287 50.65969 58.09713 33.30188 37.84756 44.97613 47.80986
#> 41 42 43 44 45 46 47 48
#> 44.32755 38.97813 43.72862 46.43393 40.34576 42.76568 40.11155 49.60886
#> 49 50 51 52 53 54 55 56
#> 41.35419 45.64184 53.31791 56.07641 32.06030 37.00888 41.90837 47.31666
#> 57 58 59 60 61 62 63 64
#> 27.74812 33.92226 34.65663 39.07791 31.23078 35.89612 41.44276 47.67264
#> 65 66 67 68 69 70 71 72
#> 22.65440 36.39591 45.41116 40.85376 32.60048 33.64329 43.79894 40.92278
#> 73 74 75 76 77 78 79 80
#> 32.14831 46.43604 41.34973 66.30382 42.78575 47.95358 53.97364 56.97067
#> 81 82 83 84 85 86 87 88
#> 46.31259 56.64544 49.70872 60.40497 45.98525 51.90911 41.50787 53.42727
#> 89 90 91 92 93 94 95 96
#> 23.86859 35.98563 43.60626 44.90173 29.59773 35.50688 55.42944 52.10530
#> 97 98 99 100 101 102 103 104
#> 31.69644 32.16159 51.04735 55.85987 49.11706 49.25544 51.72211 69.99128
#> 105 106 107 108 109 110 111 112
#> 22.07169 36.40124 46.08393 52.42288 37.69466 44.59400 52.08897 58.22961
#> 113 114 115 116 117 118 119 120
#> 37.22824 34.39863 45.68591 36.34012 45.44182 41.54847 43.92172 61.83243
#> 121 122 123 124 125 126 127 128
#> 27.25656 34.91127 45.65133 44.64728 33.19334 39.58138 45.44985 41.66826
#> 129 130 131 132 133 134 135 136
#> 27.12753 31.74858 44.41071 41.60000 39.45250 32.61823 34.62445 45.90515
#> 137 138 139 140 141 142 143 144
#> 36.17780 39.79796 45.98215 50.08272 36.34850 44.64316 45.03066 39.73529
#> 145 146 147 148 149 150 151 152
#> 34.06164 40.18592 41.17584 57.76669 38.18460 38.80536 47.19893 48.24396
#> 153 154 155 156 157 158 159 160
#> 37.32785 38.01467 43.16048 41.40349 30.15733 35.84353 40.95250 46.66874
#> 161 162 163 164 165 166 167 168
#> 35.95571 41.37928 50.17316 45.35226 39.06491 39.32297 43.53914 42.11960
#> 169 170 171 172 173 174 175 176
#> 29.81042 42.57055 47.81652 68.06024 35.62071 40.71604 45.67402 51.60799
#> 177 178 179 180 181 182 183 184
#> 33.89134 36.42808 37.57519 58.46873 19.54516 31.13541 40.89955 41.98929
#> 185 186 187 188 189 190 191 192
#> 22.18809 41.05857 37.32452 47.26465 35.24164 43.12432 41.99349 49.03216
#> 193 194 195 196 197 198 199 200
#> 44.03080 38.66417 53.45993 53.15869 29.81948 30.43859 40.18095 48.14662
#> 201 202 203 204 205 206 207 208
#> 26.78578 34.55115 44.80264 40.06421 36.74126 43.09329 46.24534 45.71567
#> 209 210 211 212 213 214 215 216
#> 40.74992 44.74635 47.38677 53.15113 40.52133 45.31900 50.16976 56.19946
#> 217 218 219 220 221 222 223 224
#> 40.14674 48.75859 46.43462 52.94589 29.33990 36.14022 42.27352 47.93165
#> 225 226 227 228 229 230 231 232
#> 36.56023 41.11632 47.05889 52.24599 45.14782 54.14236 50.44618 58.67591
#> 233 234 235 236 237 238 239 240
#> 37.53657 44.35350 49.45840 59.12866 40.31268 39.66049 50.89726 56.13116
#> 241 242 243 244 245 246 247 248
#> 32.82981 46.53837 44.34841 51.81265 27.83996 29.91939 40.81419 44.46298
#> 249 250 251 252 253 254 255 256
#> 43.41057 51.05656 50.50059 64.11388 32.21843 29.64732 42.57444 45.09919
#> 257 258 259 260 261 262 263 264
#> 39.75659 37.28894 44.80145 65.95920 33.43439 33.57042 39.91543 49.57098
#> 265 266 267 268 269 270 271 272
#> 38.91634 36.69011 45.66665 52.07431 42.21411 45.02901 50.33509 56.64523
#> 273 274 275 276 277 278 279 280
#> 30.98338 44.72932 40.68711 34.71530 27.30752 37.31585 42.13972 44.83000
#> 281 282 283 284 285 286 287 288
#> 32.93042 44.91911 45.68636 65.98800 46.60130 40.89786 46.66708 54.00187
#> 289 290 291 292 293 294 295 296
#> 34.45883 40.16352 43.83270 44.11604 38.29612 42.55473 51.38570 56.20979
#> 297 298 299 300 301 302 303 304
#> 36.00870 43.45819 38.38741 56.42818 39.05050 44.54463 49.71457 54.09200
#> 305 306 307 308 309 310 311 312
#> 31.40521 46.13330 45.29845 28.06936 39.13956 42.50283 46.45368 64.97366
#> 313 314 315 316 317 318 319 320
#> 30.79828 35.77269 41.42494 43.97847 35.33466 39.34378 41.27633 50.63458
#> 321 322 323 324 325 326 327 328
#> 34.21113 39.83058 43.49673 44.06114 41.43742 40.86389 46.16954 54.24024
#> 329 330 331 332 333 334 335 336
#> 36.61831 42.09272 50.69556 51.72563 32.18813 36.54871 41.51923 53.89947
#> 337 338 339 340 341 342 343 344
#> 32.02131 36.45473 39.94420 56.42482 41.86385 34.56420 38.68927 62.88743
#> 345 346 347 348 349 350 351 352
#> 28.85343 38.82112 49.29495 48.79915 28.74029 36.50625 43.59994 57.38616
#> 353 354 355 356 357 358 359 360
#> 35.36824 43.06110 31.27551 54.13245 25.97050 33.91065 40.52992 44.24460
#> 361 362 363 364 365 366 367 368
#> 39.78563 44.90878 51.17493 48.44043 43.33128 46.62121 55.93546 54.15312
#> 369 370 371 372 373 374 375 376
#> 33.74300 40.60252 44.44715 40.54161 33.95563 36.96071 43.67802 42.76023
#> 377 378 379 380 381 382 383 384
#> 34.22200 42.82678 39.59218 48.07181 33.49216 35.39266 43.01854 42.36266
#> 385 386 387 388 389 390 391 392
#> 48.54368 43.94366 50.77423 47.91204 20.72928 28.00599 40.19255 37.79360
#> 393 394 395 396 397 398 399 400
#> 32.09354 36.75177 42.60860 47.25054 34.59822 39.32034 40.65702 48.64064
#> 401 402 403 404 405 406 407 408
#> 40.32404 43.03255 54.65715 55.36478 35.55742 43.70215 42.52157 54.89337
#> 409 410 411 412 413 414 415 416
#> 32.03460 29.45107 45.35138 45.34981 38.73784 39.40007 41.42283 47.32385
#> 417 418 419 420 421 422 423 424
#> 40.40301 47.55310 49.06509 53.89183 29.22591 40.08175 45.68142 41.47403
#> 425 426 427 428 429 430 431 432
#> 37.66506 42.09675 42.51970 69.36099 42.39760 43.72376 49.47601 51.94188
#> 433 434 435 436 437 438 439 440
#> 31.85636 40.59100 39.97833 31.69049 33.93860 37.20517 46.28740 49.64641
#> 441 442 443 444 445 446 447 448
#> 35.31624 40.78777 46.99214 41.58720 32.17365 37.15136 40.69375 47.65030
#> 449 450 451 452 453 454 455 456
#> 32.28771 41.76205 40.06768 47.13255 29.14213 39.50989 43.32349 47.16756
#> 457 458 459 460 461 462 463 464
#> 40.93020 42.19406 41.21057 49.84692 38.54330 41.30121 43.96324 42.67652
#> 465 466 467 468 469 470 471 472
#> 22.79584 33.77087 41.48323 44.22194 31.43559 38.85064 48.24288 46.21933
#> 473 474 475 476 477 478 479 480
#> 44.71302 51.85370 50.64763 58.03156 30.56757 35.75286 41.37990 45.98051
#> 481 482 483 484 485 486 487 488
#> 37.22316 41.51692 45.80804 59.90473 33.88039 37.49795 49.76150 46.66439
#> 489 490 491 492 493 494 495 496
#> 47.21985 40.34525 48.29793 54.61927 36.20463 44.39634 41.71421 47.37535
#> 497 498 499 500 501 502 503 504
#> 42.03797 37.56100 45.11793 52.73195 34.62530 45.28206 44.37019 63.57761
#> 505 506 507 508 509 510 511 512
#> 35.80878 41.01656 45.97684 52.67314 35.88734 38.73222 46.70361 53.65398
#> 513 514 515 516 517 518 519 520
#> 36.71543 43.75763 49.41683 54.72471 37.73352 41.54317 51.67909 51.59612
#> 521 522 523 524 525 526 527 528
#> 27.40130 30.33517 37.73092 29.11668 29.98429 32.08830 41.66067 53.90815
#> 529 530 531 532 533 534 535 536
#> 33.99107 35.06937 47.17615 56.49347 33.93047 38.88006 47.54070 43.53705
#> 537 538 539 540 541 542 543 544
#> 31.82054 39.62816 44.95543 21.11543 34.74671 43.34074 49.44207 56.69249
#> 545 546 547 548 549 550 551 552
#> 22.73126 32.50075 42.37206 42.89847 55.62582 45.38998 52.66743 56.64286
#> 553 554 555 556 557 558 559 560
#> 30.83308 37.41167 34.18931 45.59740 28.89198 38.46147 42.59713 49.90357
#> 561 562 563 564 565 566 567 568
#> 39.69499 44.14167 49.75394 55.24278 36.08221 40.87987 45.73063 51.76033
#> 569 570 571 572 573 574 575 576
#> 27.38001 33.63251 44.82922 39.34410 26.98575 24.04175 42.16648 44.75380
#> 577 578 579 580 581 582 583 584
#> 31.55469 44.42696 44.10343 47.99857 34.85973 37.87445 48.31828 50.21520
#> 585 586 587 588 589 590 591 592
#> 41.94615 39.62690 46.69763 49.47735 37.96055 43.75255 47.38873 52.57999
#> 593 594 595 596 597 598 599 600
#> 32.43412 43.07163 42.99551 53.82759 39.32508 42.88409 50.64802 63.44051
#> 601 602 603 604 605 606 607 608
#> 34.48949 40.08056 42.00218 47.46553 32.13434 37.11697 44.23987 36.25120
#> 609 610 611 612 613 614 615 616
#> 29.20171 31.53773 42.35683 64.78352 32.72757 37.50022 42.89869 57.03861
#> 617 618 619 620 621 622 623 624
#> 36.32475 40.01336 41.46725 59.01411 30.14970 34.91740 52.13900 58.73839
#> 625 626 627 628 629 630 631 632
#> 35.83185 40.93247 45.66178 56.41409 37.79034 43.55593 44.26320 59.25579
#> 633 634 635 636 637 638 639 640
#> 28.47314 47.47581 44.21910 49.71965 39.25880 46.47483 51.22677 45.82777
#> 641 642 643 644 645 646 647 648
#> 33.49775 39.06783 43.14327 48.13638 29.99542 35.59648 40.95436 54.17796
#> 649 650 651 652 653 654 655 656
#> 39.22564 44.55743 47.12235 62.59579 31.81104 35.48396 44.07768 46.57837
#> 657 658 659 660 661 662 663 664
#> 47.67979 47.86016 51.04789 58.56245 22.15439 34.94847 42.67935 46.13751
#> 665 666 667 668 669 670 671 672
#> 34.27765 36.90059 42.91664 40.54285 29.09494 37.21768 43.08491 46.61656
#> 673 674 675 676 677 678 679 680
#> 27.12174 34.11916 45.71605 48.10986 36.05580 40.80230 45.89269 43.69153
#> 681 682 683 684 685 686 687 688
#> 28.60623 29.22869 40.75701 55.68362 31.90698 37.31061 40.75546 49.61178
#> 689 690 691 692 693 694 695 696
#> 42.19474 44.87228 47.55198 56.78797 50.62894 45.47551 48.62168 56.62985
#> 697 698 699 700 701 702 703 704
#> 29.66493 34.57406 42.60371 38.11676 33.77204 34.26148 45.09052 58.81037
#> 705 706 707 708 709 710 711 712
#> 31.38338 36.62986 39.88119 47.19307 31.62708 36.93821 42.55784 48.22049
#> 713 714 715 716 717 718 719 720
#> 42.58829 45.69481 49.33262 53.74331 29.71857 30.45651 38.29800 45.02430
#> 721 722 723 724 725 726 727 728
#> 36.81040 37.72068 42.35045 39.39860 36.16701 41.07320 49.73629 41.58082
#> 729 730 731 732 733 734 735 736
#> 43.58901 40.16762 46.76714 54.00167 39.66119 41.08206 49.74477 69.37409
#> 737 738 739 740 741 742 743 744
#> 34.11979 41.27625 44.76138 39.69815 38.44296 48.20586 47.47269 35.50735
#> 745 746 747 748 749 750 751 752
#> 32.08153 37.27749 42.90837 47.50555 44.69256 41.59238 42.18689 51.78495
#> 753 754 755 756 757 758 759 760
#> 37.01741 38.26920 49.28806 50.70843 40.45953 45.10337 45.58250 62.96989
#> 761 762 763 764 765 766 767 768
#> 30.78252 41.41048 48.75402 44.69667 32.72491 45.78702 48.74886 84.08449
#> 769 770 771 772 773 774 775 776
#> 28.61581 30.19495 36.78573 61.03588 20.36749 35.22480 37.42847 30.20501
#> 777 778 779 780 781 782 783 784
#> 41.81258 49.12862 47.31234 57.24330 19.28388 30.00682 39.56686 49.21768
#> 785 786 787 788 789 790 791 792
#> 31.29535 36.58216 42.58139 40.13353 42.34534 52.32575 46.80142 69.26254
#> 793 794 795 796 797 798 799 800
#> 40.19527 44.99293 49.84370 55.87340 35.70341 41.64454 43.17210 54.25081
# Model frame:
model.frame(object)
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#> 96 52.10530 White Female TRT VIS4 PT24
#> 97 31.69644 White Female TRT VIS1 PT25
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#> 108 52.42288 Black or African American Female TRT VIS4 PT27
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#> 112 58.22961 Black or African American Male TRT VIS4 PT28
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#> 129 27.12753 Black or African American Male TRT VIS1 PT33
#> 130 31.74858 Black or African American Male TRT VIS2 PT33
#> 132 41.60000 Black or African American Male TRT VIS4 PT33
#> 133 39.45250 Asian Female PBO VIS1 PT34
#> 134 32.61823 Asian Female PBO VIS2 PT34
#> 135 34.62445 Asian Female PBO VIS3 PT34
#> 136 45.90515 Asian Female PBO VIS4 PT34
#> 137 36.17780 Black or African American Female TRT VIS1 PT35
#> 138 39.79796 Black or African American Female TRT VIS2 PT35
#> 140 50.08272 Black or African American Female TRT VIS4 PT35
#> 142 44.64316 Asian Female TRT VIS2 PT36
#> 144 39.73529 Asian Female TRT VIS4 PT36
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#> 146 40.18592 White Female PBO VIS2 PT37
#> 147 41.17584 White Female PBO VIS3 PT37
#> 148 57.76669 White Female PBO VIS4 PT37
#> 149 38.18460 Asian Female PBO VIS1 PT38
#> 151 47.19893 Asian Female PBO VIS3 PT38
#> 153 37.32785 Asian Female PBO VIS1 PT39
#> 155 43.16048 Asian Female PBO VIS3 PT39
#> 156 41.40349 Asian Female PBO VIS4 PT39
#> 157 30.15733 Black or African American Male PBO VIS1 PT40
#> 158 35.84353 Black or African American Male PBO VIS2 PT40
#> 159 40.95250 Black or African American Male PBO VIS3 PT40
#> 162 41.37928 Black or African American Male PBO VIS2 PT41
#> 163 50.17316 Black or African American Male PBO VIS3 PT41
#> 164 45.35226 Black or African American Male PBO VIS4 PT41
#> 165 39.06491 Black or African American Male PBO VIS1 PT42
#> 168 42.11960 Black or African American Male PBO VIS4 PT42
#> 169 29.81042 Black or African American Female TRT VIS1 PT43
#> 170 42.57055 Black or African American Female TRT VIS2 PT43
#> 171 47.81652 Black or African American Female TRT VIS3 PT43
#> 172 68.06024 Black or African American Female TRT VIS4 PT43
#> 173 35.62071 Black or African American Male TRT VIS1 PT44
#> 177 33.89134 Asian Female PBO VIS1 PT45
#> 178 36.42808 Asian Female PBO VIS2 PT45
#> 179 37.57519 Asian Female PBO VIS3 PT45
#> 180 58.46873 Asian Female PBO VIS4 PT45
#> 181 19.54516 Asian Male PBO VIS1 PT46
#> 182 31.13541 Asian Male PBO VIS2 PT46
#> 183 40.89955 Asian Male PBO VIS3 PT46
#> 185 22.18809 Asian Male TRT VIS1 PT47
#> 186 41.05857 Asian Male TRT VIS2 PT47
#> 187 37.32452 Asian Male TRT VIS3 PT47
#> 190 43.12432 Black or African American Male PBO VIS2 PT48
#> 191 41.99349 Black or African American Male PBO VIS3 PT48
#> 193 44.03080 White Female PBO VIS1 PT49
#> 194 38.66417 White Female PBO VIS2 PT49
#> 195 53.45993 White Female PBO VIS3 PT49
#> 197 29.81948 Asian Female TRT VIS1 PT50
#> 198 30.43859 Asian Female TRT VIS2 PT50
#> 199 40.18095 Asian Female TRT VIS3 PT50
#> 201 26.78578 Black or African American Female TRT VIS1 PT51
#> 202 34.55115 Black or African American Female TRT VIS2 PT51
#> 204 40.06421 Black or African American Female TRT VIS4 PT51
#> 206 43.09329 Black or African American Female TRT VIS2 PT52
#> 208 45.71567 Black or African American Female TRT VIS4 PT52
#> 209 40.74992 White Male PBO VIS1 PT53
#> 210 44.74635 White Male PBO VIS2 PT53
#> 217 40.14674 Asian Male TRT VIS1 PT55
#> 218 48.75859 Asian Male TRT VIS2 PT55
#> 219 46.43462 Asian Male TRT VIS3 PT55
#> 221 29.33990 Black or African American Male PBO VIS1 PT56
#> 224 47.93165 Black or African American Male PBO VIS4 PT56
#> 226 41.11632 Black or African American Male TRT VIS2 PT57
#> 227 47.05889 Black or African American Male TRT VIS3 PT57
#> 228 52.24599 Black or African American Male TRT VIS4 PT57
#> 230 54.14236 White Female TRT VIS2 PT58
#> 231 50.44618 White Female TRT VIS3 PT58
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#> 235 49.45840 White Female TRT VIS3 PT59
#> 236 59.12866 White Female TRT VIS4 PT59
#> 237 40.31268 Black or African American Female TRT VIS1 PT60
#> 238 39.66049 Black or African American Female TRT VIS2 PT60
#> 239 50.89726 Black or African American Female TRT VIS3 PT60
#> 240 56.13116 Black or African American Female TRT VIS4 PT60
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#> 244 51.81265 Asian Male TRT VIS4 PT61
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#> 250 51.05656 White Female PBO VIS2 PT63
#> 251 50.50059 White Female PBO VIS3 PT63
#> 252 64.11388 White Female PBO VIS4 PT63
#> 253 32.21843 Black or African American Female PBO VIS1 PT64
#> 254 29.64732 Black or African American Female PBO VIS2 PT64
#> 256 45.09919 Black or African American Female PBO VIS4 PT64
#> 257 39.75659 Asian Female TRT VIS1 PT65
#> 258 37.28894 Asian Female TRT VIS2 PT65
#> 259 44.80145 Asian Female TRT VIS3 PT65
#> 260 65.95920 Asian Female TRT VIS4 PT65
#> 261 33.43439 Asian Male TRT VIS1 PT66
#> 262 33.57042 Asian Male TRT VIS2 PT66
#> 263 39.91543 Asian Male TRT VIS3 PT66
#> 264 49.57098 Asian Male TRT VIS4 PT66
#> 265 38.91634 White Female TRT VIS1 PT67
#> 266 36.69011 White Female TRT VIS2 PT67
#> 267 45.66665 White Female TRT VIS3 PT67
#> 268 52.07431 White Female TRT VIS4 PT67
#> 269 42.21411 White Female TRT VIS1 PT68
#> 270 45.02901 White Female TRT VIS2 PT68
#> 273 30.98338 Black or African American Male PBO VIS1 PT69
#> 274 44.72932 Black or African American Male PBO VIS2 PT69
#> 275 40.68711 Black or African American Male PBO VIS3 PT69
#> 276 34.71530 Black or African American Male PBO VIS4 PT69
#> 277 27.30752 Black or African American Male PBO VIS1 PT70
#> 278 37.31585 Black or African American Male PBO VIS2 PT70
#> 280 44.83000 Black or African American Male PBO VIS4 PT70
#> 281 32.93042 Asian Male TRT VIS1 PT71
#> 282 44.91911 Asian Male TRT VIS2 PT71
#> 283 45.68636 Asian Male TRT VIS3 PT71
#> 284 65.98800 Asian Male TRT VIS4 PT71
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#> 286 40.89786 Asian Female TRT VIS2 PT72
#> 287 46.66708 Asian Female TRT VIS3 PT72
#> 291 43.83270 White Male PBO VIS3 PT73
#> 292 44.11604 White Male PBO VIS4 PT73
#> 293 38.29612 White Female PBO VIS1 PT74
#> 295 51.38570 White Female PBO VIS3 PT74
#> 296 56.20979 White Female PBO VIS4 PT74
#> 298 43.45819 White Male PBO VIS2 PT75
#> 299 38.38741 White Male PBO VIS3 PT75
#> 300 56.42818 White Male PBO VIS4 PT75
#> 301 39.05050 White Male TRT VIS1 PT76
#> 304 54.09200 White Male TRT VIS4 PT76
#> 305 31.40521 Asian Male TRT VIS1 PT77
#> 306 46.13330 Asian Male TRT VIS2 PT77
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#> 322 39.83058 White Male PBO VIS2 PT81
#> 323 43.49673 White Male PBO VIS3 PT81
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#> 327 46.16954 Black or African American Female PBO VIS3 PT82
#> 328 54.24024 Black or African American Female PBO VIS4 PT82
#> 329 36.61831 White Male TRT VIS1 PT83
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#> 344 62.88743 Black or African American Female TRT VIS4 PT86
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#> 347 49.29495 White Male PBO VIS3 PT87
#> 349 28.74029 Black or African American Female PBO VIS1 PT88
#> 351 43.59994 Black or African American Female PBO VIS3 PT88
#> 352 57.38616 Black or African American Female PBO VIS4 PT88
#> 353 35.36824 Black or African American Male PBO VIS1 PT89
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#> 355 31.27551 Black or African American Male PBO VIS3 PT89
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#> 376 42.76023 Asian Female PBO VIS4 PT94
#> 378 42.82678 Asian Female PBO VIS2 PT95
#> 379 39.59218 Asian Female PBO VIS3 PT95
#> 381 33.49216 Black or African American Female PBO VIS1 PT96
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#> 385 48.54368 White Male TRT VIS1 PT97
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#> 389 20.72928 Asian Male PBO VIS1 PT98
#> 390 28.00599 Asian Male PBO VIS2 PT98
#> 391 40.19255 Asian Male PBO VIS3 PT98
#> 392 37.79360 Asian Male PBO VIS4 PT98
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#> 397 34.59822 Black or African American Female PBO VIS1 PT100
#> 398 39.32034 Black or African American Female PBO VIS2 PT100
#> 399 40.65702 Black or African American Female PBO VIS3 PT100
#> 402 43.03255 White Male TRT VIS2 PT101
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#> 407 42.52157 Asian Female PBO VIS3 PT102
#> 408 54.89337 Asian Female PBO VIS4 PT102
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#> 416 47.32385 Black or African American Female PBO VIS4 PT104
#> 418 47.55310 Black or African American Female TRT VIS2 PT105
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#> 429 42.39760 White Male TRT VIS1 PT108
#> 430 43.72376 White Male TRT VIS2 PT108
#> 431 49.47601 White Male TRT VIS3 PT108
#> 432 51.94188 White Male TRT VIS4 PT108
#> 434 40.59100 Black or African American Female PBO VIS2 PT109
#> 435 39.97833 Black or African American Female PBO VIS3 PT109
#> 436 31.69049 Black or African American Female PBO VIS4 PT109
#> 438 37.20517 Asian Male TRT VIS2 PT110
#> 439 46.28740 Asian Male TRT VIS3 PT110
#> 444 41.58720 White Female PBO VIS4 PT111
#> 445 32.17365 Black or African American Female PBO VIS1 PT112
#> 447 40.69375 Black or African American Female PBO VIS3 PT112
#> 449 32.28771 Asian Male PBO VIS1 PT113
#> 450 41.76205 Asian Male PBO VIS2 PT113
#> 451 40.06768 Asian Male PBO VIS3 PT113
#> 453 29.14213 Black or African American Male PBO VIS1 PT114
#> 454 39.50989 Black or African American Male PBO VIS2 PT114
#> 455 43.32349 Black or African American Male PBO VIS3 PT114
#> 456 47.16756 Black or African American Male PBO VIS4 PT114
#> 457 40.93020 Asian Female PBO VIS1 PT115
#> 458 42.19406 Asian Female PBO VIS2 PT115
#> 459 41.21057 Asian Female PBO VIS3 PT115
#> 461 38.54330 Black or African American Male TRT VIS1 PT116
#> 463 43.96324 Black or African American Male TRT VIS3 PT116
#> 464 42.67652 Black or African American Male TRT VIS4 PT116
#> 465 22.79584 Black or African American Male PBO VIS1 PT117
#> 469 31.43559 Asian Female PBO VIS1 PT118
#> 470 38.85064 Asian Female PBO VIS2 PT118
#> 471 48.24288 Asian Female PBO VIS3 PT118
#> 473 44.71302 White Male TRT VIS1 PT119
#> 474 51.85370 White Male TRT VIS2 PT119
#> 477 30.56757 Asian Female PBO VIS1 PT120
#> 484 59.90473 Black or African American Male TRT VIS4 PT121
#> 487 49.76150 Asian Female PBO VIS3 PT122
#> 489 47.21985 White Female PBO VIS1 PT123
#> 490 40.34525 White Female PBO VIS2 PT123
#> 491 48.29793 White Female PBO VIS3 PT123
#> 494 44.39634 Asian Female TRT VIS2 PT124
#> 495 41.71421 Asian Female TRT VIS3 PT124
#> 496 47.37535 Asian Female TRT VIS4 PT124
#> 497 42.03797 White Male PBO VIS1 PT125
#> 498 37.56100 White Male PBO VIS2 PT125
#> 499 45.11793 White Male PBO VIS3 PT125
#> 501 34.62530 Asian Male TRT VIS1 PT126
#> 502 45.28206 Asian Male TRT VIS2 PT126
#> 504 63.57761 Asian Male TRT VIS4 PT126
#> 505 35.80878 Black or African American Female TRT VIS1 PT127
#> 508 52.67314 Black or African American Female TRT VIS4 PT127
#> 509 35.88734 Asian Female TRT VIS1 PT128
#> 510 38.73222 Asian Female TRT VIS2 PT128
#> 511 46.70361 Asian Female TRT VIS3 PT128
#> 512 53.65398 Asian Female TRT VIS4 PT128
#> 513 36.71543 White Male TRT VIS1 PT129
#> 518 41.54317 White Male PBO VIS2 PT130
#> 519 51.67909 White Male PBO VIS3 PT130
#> 521 27.40130 Asian Female PBO VIS1 PT131
#> 522 30.33517 Asian Female PBO VIS2 PT131
#> 523 37.73092 Asian Female PBO VIS3 PT131
#> 524 29.11668 Asian Female PBO VIS4 PT131
#> 526 32.08830 Asian Male PBO VIS2 PT132
#> 527 41.66067 Asian Male PBO VIS3 PT132
#> 528 53.90815 Asian Male PBO VIS4 PT132
#> 530 35.06937 White Male PBO VIS2 PT133
#> 531 47.17615 White Male PBO VIS3 PT133
#> 532 56.49347 White Male PBO VIS4 PT133
#> 534 38.88006 Black or African American Male PBO VIS2 PT134
#> 535 47.54070 Black or African American Male PBO VIS3 PT134
#> 536 43.53705 Black or African American Male PBO VIS4 PT134
#> 537 31.82054 Black or African American Male PBO VIS1 PT135
#> 538 39.62816 Black or African American Male PBO VIS2 PT135
#> 539 44.95543 Black or African American Male PBO VIS3 PT135
#> 540 21.11543 Black or African American Male PBO VIS4 PT135
#> 541 34.74671 White Female TRT VIS1 PT136
#> 544 56.69249 White Female TRT VIS4 PT136
#> 545 22.73126 Asian Female TRT VIS1 PT137
#> 546 32.50075 Asian Female TRT VIS2 PT137
#> 547 42.37206 Asian Female TRT VIS3 PT137
#> 548 42.89847 Asian Female TRT VIS4 PT137
#> 549 55.62582 Asian Male TRT VIS1 PT138
#> 550 45.38998 Asian Male TRT VIS2 PT138
#> 551 52.66743 Asian Male TRT VIS3 PT138
#> 555 34.18931 Asian Female TRT VIS3 PT139
#> 556 45.59740 Asian Female TRT VIS4 PT139
#> 557 28.89198 Black or African American Female PBO VIS1 PT140
#> 558 38.46147 Black or African American Female PBO VIS2 PT140
#> 560 49.90357 Black or African American Female PBO VIS4 PT140
#> 562 44.14167 White Male TRT VIS2 PT141
#> 564 55.24278 White Male TRT VIS4 PT141
#> 569 27.38001 Black or African American Female TRT VIS1 PT143
#> 570 33.63251 Black or African American Female TRT VIS2 PT143
#> 572 39.34410 Black or African American Female TRT VIS4 PT143
#> 573 26.98575 Asian Female PBO VIS1 PT144
#> 574 24.04175 Asian Female PBO VIS2 PT144
#> 575 42.16648 Asian Female PBO VIS3 PT144
#> 576 44.75380 Asian Female PBO VIS4 PT144
#> 577 31.55469 Black or African American Male PBO VIS1 PT145
#> 578 44.42696 Black or African American Male PBO VIS2 PT145
#> 579 44.10343 Black or African American Male PBO VIS3 PT145
#> 582 37.87445 Asian Female TRT VIS2 PT146
#> 583 48.31828 Asian Female TRT VIS3 PT146
#> 584 50.21520 Asian Female TRT VIS4 PT146
#> 585 41.94615 Asian Female PBO VIS1 PT147
#> 586 39.62690 Asian Female PBO VIS2 PT147
#> 587 46.69763 Asian Female PBO VIS3 PT147
#> 590 43.75255 Black or African American Male TRT VIS2 PT148
#> 591 47.38873 Black or African American Male TRT VIS3 PT148
#> 593 32.43412 Asian Female PBO VIS1 PT149
#> 594 43.07163 Asian Female PBO VIS2 PT149
#> 595 42.99551 Asian Female PBO VIS3 PT149
#> 596 53.82759 Asian Female PBO VIS4 PT149
#> 599 50.64802 White Male PBO VIS3 PT150
#> 600 63.44051 White Male PBO VIS4 PT150
#> 601 34.48949 Asian Female PBO VIS1 PT151
#> 602 40.08056 Asian Female PBO VIS2 PT151
#> 604 47.46553 Asian Female PBO VIS4 PT151
#> 606 37.11697 Asian Female TRT VIS2 PT152
#> 608 36.25120 Asian Female TRT VIS4 PT152
#> 609 29.20171 Black or African American Male PBO VIS1 PT153
#> 610 31.53773 Black or African American Male PBO VIS2 PT153
#> 611 42.35683 Black or African American Male PBO VIS3 PT153
#> 612 64.78352 Black or African American Male PBO VIS4 PT153
#> 613 32.72757 Black or African American Female PBO VIS1 PT154
#> 614 37.50022 Black or African American Female PBO VIS2 PT154
#> 616 57.03861 Black or African American Female PBO VIS4 PT154
#> 617 36.32475 Asian Male TRT VIS1 PT155
#> 619 41.46725 Asian Male TRT VIS3 PT155
#> 620 59.01411 Asian Male TRT VIS4 PT155
#> 621 30.14970 White Male PBO VIS1 PT156
#> 622 34.91740 White Male PBO VIS2 PT156
#> 623 52.13900 White Male PBO VIS3 PT156
#> 624 58.73839 White Male PBO VIS4 PT156
#> 625 35.83185 Black or African American Male TRT VIS1 PT157
#> 628 56.41409 Black or African American Male TRT VIS4 PT157
#> 630 43.55593 Black or African American Male TRT VIS2 PT158
#> 631 44.26320 Black or African American Male TRT VIS3 PT158
#> 632 59.25579 Black or African American Male TRT VIS4 PT158
#> 633 28.47314 Asian Female TRT VIS1 PT159
#> 634 47.47581 Asian Female TRT VIS2 PT159
#> 638 46.47483 Asian Male TRT VIS2 PT160
#> 639 51.22677 Asian Male TRT VIS3 PT160
#> 640 45.82777 Asian Male TRT VIS4 PT160
#> 642 39.06783 Black or African American Female PBO VIS2 PT161
#> 645 29.99542 Asian Male PBO VIS1 PT162
#> 648 54.17796 Asian Male PBO VIS4 PT162
#> 650 44.55743 White Male PBO VIS2 PT163
#> 652 62.59579 White Male PBO VIS4 PT163
#> 654 35.48396 Black or African American Female PBO VIS2 PT164
#> 655 44.07768 Black or African American Female PBO VIS3 PT164
#> 656 46.57837 Black or African American Female PBO VIS4 PT164
#> 657 47.67979 White Female TRT VIS1 PT165
#> 661 22.15439 Asian Male TRT VIS1 PT166
#> 665 34.27765 Black or African American Male PBO VIS1 PT167
#> 666 36.90059 Black or African American Male PBO VIS2 PT167
#> 668 40.54285 Black or African American Male PBO VIS4 PT167
#> 669 29.09494 Black or African American Female PBO VIS1 PT168
#> 670 37.21768 Black or African American Female PBO VIS2 PT168
#> 671 43.08491 Black or African American Female PBO VIS3 PT168
#> 673 27.12174 White Female PBO VIS1 PT169
#> 674 34.11916 White Female PBO VIS2 PT169
#> 678 40.80230 White Female TRT VIS2 PT170
#> 679 45.89269 White Female TRT VIS3 PT170
#> 680 43.69153 White Female TRT VIS4 PT170
#> 682 29.22869 Asian Female PBO VIS2 PT171
#> 684 55.68362 Asian Female PBO VIS4 PT171
#> 685 31.90698 Asian Female TRT VIS1 PT172
#> 686 37.31061 Asian Female TRT VIS2 PT172
#> 687 40.75546 Asian Female TRT VIS3 PT172
#> 689 42.19474 White Female TRT VIS1 PT173
#> 690 44.87228 White Female TRT VIS2 PT173
#> 691 47.55198 White Female TRT VIS3 PT173
#> 693 50.62894 Black or African American Female TRT VIS1 PT174
#> 694 45.47551 Black or African American Female TRT VIS2 PT174
#> 695 48.62168 Black or African American Female TRT VIS3 PT174
#> 697 29.66493 Black or African American Female PBO VIS1 PT175
#> 698 34.57406 Black or African American Female PBO VIS2 PT175
#> 700 38.11676 Black or African American Female PBO VIS4 PT175
#> 701 33.77204 Black or African American Male TRT VIS1 PT176
#> 702 34.26148 Black or African American Male TRT VIS2 PT176
#> 704 58.81037 Black or African American Male TRT VIS4 PT176
#> 707 39.88119 Black or African American Male PBO VIS3 PT177
#> 709 31.62708 Black or African American Male PBO VIS1 PT178
#> 712 48.22049 Black or African American Male PBO VIS4 PT178
#> 713 42.58829 White Male TRT VIS1 PT179
#> 715 49.33262 White Male TRT VIS3 PT179
#> 716 53.74331 White Male TRT VIS4 PT179
#> 717 29.71857 Asian Male PBO VIS1 PT180
#> 718 30.45651 Asian Male PBO VIS2 PT180
#> 719 38.29800 Asian Male PBO VIS3 PT180
#> 721 36.81040 Asian Female PBO VIS1 PT181
#> 723 42.35045 Asian Female PBO VIS3 PT181
#> 724 39.39860 Asian Female PBO VIS4 PT181
#> 727 49.73629 Black or African American Female TRT VIS3 PT182
#> 728 41.58082 Black or African American Female TRT VIS4 PT182
#> 729 43.58901 Black or African American Female TRT VIS1 PT183
#> 730 40.16762 Black or African American Female TRT VIS2 PT183
#> 734 41.08206 White Female TRT VIS2 PT184
#> 736 69.37409 White Female TRT VIS4 PT184
#> 738 41.27625 Black or African American Female PBO VIS2 PT185
#> 739 44.76138 Black or African American Female PBO VIS3 PT185
#> 740 39.69815 Black or African American Female PBO VIS4 PT185
#> 741 38.44296 White Male PBO VIS1 PT186
#> 742 48.20586 White Male PBO VIS2 PT186
#> 744 35.50735 White Male PBO VIS4 PT186
#> 745 32.08153 Black or African American Female PBO VIS1 PT187
#> 749 44.69256 Black or African American Female PBO VIS1 PT188
#> 751 42.18689 Black or African American Female PBO VIS3 PT188
#> 753 37.01741 Asian Female TRT VIS1 PT189
#> 754 38.26920 Asian Female TRT VIS2 PT189
#> 755 49.28806 Asian Female TRT VIS3 PT189
#> 757 40.45953 Black or African American Female TRT VIS1 PT190
#> 758 45.10337 Black or African American Female TRT VIS2 PT190
#> 759 45.58250 Black or African American Female TRT VIS3 PT190
#> 760 62.96989 Black or African American Female TRT VIS4 PT190
#> 761 30.78252 White Male TRT VIS1 PT191
#> 764 44.69667 White Male TRT VIS4 PT191
#> 765 32.72491 White Female TRT VIS1 PT192
#> 766 45.78702 White Female TRT VIS2 PT192
#> 767 48.74886 White Female TRT VIS3 PT192
#> 768 84.08449 White Female TRT VIS4 PT192
#> 770 30.19495 Asian Male PBO VIS2 PT193
#> 771 36.78573 Asian Male PBO VIS3 PT193
#> 772 61.03588 Asian Male PBO VIS4 PT193
#> 773 20.36749 Black or African American Male PBO VIS1 PT194
#> 774 35.22480 Black or African American Male PBO VIS2 PT194
#> 775 37.42847 Black or African American Male PBO VIS3 PT194
#> 776 30.20501 Black or African American Male PBO VIS4 PT194
#> 778 49.12862 White Female TRT VIS2 PT195
#> 779 47.31234 White Female TRT VIS3 PT195
#> 781 19.28388 Asian Male PBO VIS1 PT196
#> 782 30.00682 Asian Male PBO VIS2 PT196
#> 784 49.21768 Asian Male PBO VIS4 PT196
#> 788 40.13353 Black or African American Male PBO VIS4 PT197
#> 789 42.34534 Black or African American Male TRT VIS1 PT198
#> 790 52.32575 Black or African American Male TRT VIS2 PT198
#> 792 69.26254 Black or African American Male TRT VIS4 PT198
#> 797 35.70341 Black or African American Male PBO VIS1 PT200
#> 798 41.64454 Black or African American Male PBO VIS2 PT200
#> 800 54.25081 Black or African American Male PBO VIS4 PT200
model.frame(object, include = "subject_var")
#> RACE SEX ARMCD AVISIT USUBJID
#> 2 Black or African American Female TRT VIS2 PT1
#> 4 Black or African American Female TRT VIS4 PT1
#> 6 Asian Male PBO VIS2 PT2
#> 7 Asian Male PBO VIS3 PT2
#> 8 Asian Male PBO VIS4 PT2
#> 10 Black or African American Female PBO VIS2 PT3
#> 12 Black or African American Female PBO VIS4 PT3
#> 13 Asian Female TRT VIS1 PT4
#> 14 Asian Female TRT VIS2 PT4
#> 16 Asian Female TRT VIS4 PT4
#> 17 Black or African American Male PBO VIS1 PT5
#> 19 Black or African American Male PBO VIS3 PT5
#> 20 Black or African American Male PBO VIS4 PT5
#> 23 Black or African American Male PBO VIS3 PT6
#> 25 Asian Female PBO VIS1 PT7
#> 26 Asian Female PBO VIS2 PT7
#> 28 Asian Female PBO VIS4 PT7
#> 29 Black or African American Male PBO VIS1 PT8
#> 30 Black or African American Male PBO VIS2 PT8
#> 31 Black or African American Male PBO VIS3 PT8
#> 32 Black or African American Male PBO VIS4 PT8
#> 33 White Male TRT VIS1 PT9
#> 34 White Male TRT VIS2 PT9
#> 36 White Male TRT VIS4 PT9
#> 39 Black or African American Female PBO VIS3 PT10
#> 41 Asian Female TRT VIS1 PT11
#> 42 Asian Female TRT VIS2 PT11
#> 43 Asian Female TRT VIS3 PT11
#> 44 Asian Female TRT VIS4 PT11
#> 45 Asian Male PBO VIS1 PT12
#> 46 Asian Male PBO VIS2 PT12
#> 47 Asian Male PBO VIS3 PT12
#> 51 White Male TRT VIS3 PT13
#> 52 White Male TRT VIS4 PT13
#> 55 Black or African American Male PBO VIS3 PT14
#> 59 Asian Male PBO VIS3 PT15
#> 60 Asian Male PBO VIS4 PT15
#> 62 Asian Female PBO VIS2 PT16
#> 64 Asian Female PBO VIS4 PT16
#> 65 White Female PBO VIS1 PT17
#> 68 White Female PBO VIS4 PT17
#> 69 Asian Male TRT VIS1 PT18
#> 70 Asian Male TRT VIS2 PT18
#> 72 Asian Male TRT VIS4 PT18
#> 73 Asian Male TRT VIS1 PT19
#> 74 Asian Male TRT VIS2 PT19
#> 75 Asian Male TRT VIS3 PT19
#> 76 Asian Male TRT VIS4 PT19
#> 78 White Female TRT VIS2 PT20
#> 79 White Female TRT VIS3 PT20
#> 82 White Male TRT VIS2 PT21
#> 83 White Male TRT VIS3 PT21
#> 84 White Male TRT VIS4 PT21
#> 85 White Male TRT VIS1 PT22
#> 86 White Male TRT VIS2 PT22
#> 87 White Male TRT VIS3 PT22
#> 88 White Male TRT VIS4 PT22
#> 89 Black or African American Female PBO VIS1 PT23
#> 90 Black or African American Female PBO VIS2 PT23
#> 91 Black or African American Female PBO VIS3 PT23
#> 93 White Female TRT VIS1 PT24
#> 94 White Female TRT VIS2 PT24
#> 95 White Female TRT VIS3 PT24
#> 96 White Female TRT VIS4 PT24
#> 97 White Female TRT VIS1 PT25
#> 98 White Female TRT VIS2 PT25
#> 99 White Female TRT VIS3 PT25
#> 100 White Female TRT VIS4 PT25
#> 101 White Female TRT VIS1 PT26
#> 102 White Female TRT VIS2 PT26
#> 103 White Female TRT VIS3 PT26
#> 104 White Female TRT VIS4 PT26
#> 105 Black or African American Female TRT VIS1 PT27
#> 107 Black or African American Female TRT VIS3 PT27
#> 108 Black or African American Female TRT VIS4 PT27
#> 109 Black or African American Male TRT VIS1 PT28
#> 110 Black or African American Male TRT VIS2 PT28
#> 111 Black or African American Male TRT VIS3 PT28
#> 112 Black or African American Male TRT VIS4 PT28
#> 113 Black or African American Male TRT VIS1 PT29
#> 114 Black or African American Male TRT VIS2 PT29
#> 116 Black or African American Male TRT VIS4 PT29
#> 117 Asian Female TRT VIS1 PT30
#> 118 Asian Female TRT VIS2 PT30
#> 119 Asian Female TRT VIS3 PT30
#> 120 Asian Female TRT VIS4 PT30
#> 121 Asian Female PBO VIS1 PT31
#> 123 Asian Female PBO VIS3 PT31
#> 125 Black or African American Male TRT VIS1 PT32
#> 128 Black or African American Male TRT VIS4 PT32
#> 129 Black or African American Male TRT VIS1 PT33
#> 130 Black or African American Male TRT VIS2 PT33
#> 132 Black or African American Male TRT VIS4 PT33
#> 133 Asian Female PBO VIS1 PT34
#> 134 Asian Female PBO VIS2 PT34
#> 135 Asian Female PBO VIS3 PT34
#> 136 Asian Female PBO VIS4 PT34
#> 137 Black or African American Female TRT VIS1 PT35
#> 138 Black or African American Female TRT VIS2 PT35
#> 140 Black or African American Female TRT VIS4 PT35
#> 142 Asian Female TRT VIS2 PT36
#> 144 Asian Female TRT VIS4 PT36
#> 145 White Female PBO VIS1 PT37
#> 146 White Female PBO VIS2 PT37
#> 147 White Female PBO VIS3 PT37
#> 148 White Female PBO VIS4 PT37
#> 149 Asian Female PBO VIS1 PT38
#> 151 Asian Female PBO VIS3 PT38
#> 153 Asian Female PBO VIS1 PT39
#> 155 Asian Female PBO VIS3 PT39
#> 156 Asian Female PBO VIS4 PT39
#> 157 Black or African American Male PBO VIS1 PT40
#> 158 Black or African American Male PBO VIS2 PT40
#> 159 Black or African American Male PBO VIS3 PT40
#> 162 Black or African American Male PBO VIS2 PT41
#> 163 Black or African American Male PBO VIS3 PT41
#> 164 Black or African American Male PBO VIS4 PT41
#> 165 Black or African American Male PBO VIS1 PT42
#> 168 Black or African American Male PBO VIS4 PT42
#> 169 Black or African American Female TRT VIS1 PT43
#> 170 Black or African American Female TRT VIS2 PT43
#> 171 Black or African American Female TRT VIS3 PT43
#> 172 Black or African American Female TRT VIS4 PT43
#> 173 Black or African American Male TRT VIS1 PT44
#> 177 Asian Female PBO VIS1 PT45
#> 178 Asian Female PBO VIS2 PT45
#> 179 Asian Female PBO VIS3 PT45
#> 180 Asian Female PBO VIS4 PT45
#> 181 Asian Male PBO VIS1 PT46
#> 182 Asian Male PBO VIS2 PT46
#> 183 Asian Male PBO VIS3 PT46
#> 185 Asian Male TRT VIS1 PT47
#> 186 Asian Male TRT VIS2 PT47
#> 187 Asian Male TRT VIS3 PT47
#> 190 Black or African American Male PBO VIS2 PT48
#> 191 Black or African American Male PBO VIS3 PT48
#> 193 White Female PBO VIS1 PT49
#> 194 White Female PBO VIS2 PT49
#> 195 White Female PBO VIS3 PT49
#> 197 Asian Female TRT VIS1 PT50
#> 198 Asian Female TRT VIS2 PT50
#> 199 Asian Female TRT VIS3 PT50
#> 201 Black or African American Female TRT VIS1 PT51
#> 202 Black or African American Female TRT VIS2 PT51
#> 204 Black or African American Female TRT VIS4 PT51
#> 206 Black or African American Female TRT VIS2 PT52
#> 208 Black or African American Female TRT VIS4 PT52
#> 209 White Male PBO VIS1 PT53
#> 210 White Male PBO VIS2 PT53
#> 217 Asian Male TRT VIS1 PT55
#> 218 Asian Male TRT VIS2 PT55
#> 219 Asian Male TRT VIS3 PT55
#> 221 Black or African American Male PBO VIS1 PT56
#> 224 Black or African American Male PBO VIS4 PT56
#> 226 Black or African American Male TRT VIS2 PT57
#> 227 Black or African American Male TRT VIS3 PT57
#> 228 Black or African American Male TRT VIS4 PT57
#> 230 White Female TRT VIS2 PT58
#> 231 White Female TRT VIS3 PT58
#> 233 White Female TRT VIS1 PT59
#> 235 White Female TRT VIS3 PT59
#> 236 White Female TRT VIS4 PT59
#> 237 Black or African American Female TRT VIS1 PT60
#> 238 Black or African American Female TRT VIS2 PT60
#> 239 Black or African American Female TRT VIS3 PT60
#> 240 Black or African American Female TRT VIS4 PT60
#> 241 Asian Male TRT VIS1 PT61
#> 242 Asian Male TRT VIS2 PT61
#> 244 Asian Male TRT VIS4 PT61
#> 246 Asian Female PBO VIS2 PT62
#> 250 White Female PBO VIS2 PT63
#> 251 White Female PBO VIS3 PT63
#> 252 White Female PBO VIS4 PT63
#> 253 Black or African American Female PBO VIS1 PT64
#> 254 Black or African American Female PBO VIS2 PT64
#> 256 Black or African American Female PBO VIS4 PT64
#> 257 Asian Female TRT VIS1 PT65
#> 258 Asian Female TRT VIS2 PT65
#> 259 Asian Female TRT VIS3 PT65
#> 260 Asian Female TRT VIS4 PT65
#> 261 Asian Male TRT VIS1 PT66
#> 262 Asian Male TRT VIS2 PT66
#> 263 Asian Male TRT VIS3 PT66
#> 264 Asian Male TRT VIS4 PT66
#> 265 White Female TRT VIS1 PT67
#> 266 White Female TRT VIS2 PT67
#> 267 White Female TRT VIS3 PT67
#> 268 White Female TRT VIS4 PT67
#> 269 White Female TRT VIS1 PT68
#> 270 White Female TRT VIS2 PT68
#> 273 Black or African American Male PBO VIS1 PT69
#> 274 Black or African American Male PBO VIS2 PT69
#> 275 Black or African American Male PBO VIS3 PT69
#> 276 Black or African American Male PBO VIS4 PT69
#> 277 Black or African American Male PBO VIS1 PT70
#> 278 Black or African American Male PBO VIS2 PT70
#> 280 Black or African American Male PBO VIS4 PT70
#> 281 Asian Male TRT VIS1 PT71
#> 282 Asian Male TRT VIS2 PT71
#> 283 Asian Male TRT VIS3 PT71
#> 284 Asian Male TRT VIS4 PT71
#> 285 Asian Female TRT VIS1 PT72
#> 286 Asian Female TRT VIS2 PT72
#> 287 Asian Female TRT VIS3 PT72
#> 291 White Male PBO VIS3 PT73
#> 292 White Male PBO VIS4 PT73
#> 293 White Female PBO VIS1 PT74
#> 295 White Female PBO VIS3 PT74
#> 296 White Female PBO VIS4 PT74
#> 298 White Male PBO VIS2 PT75
#> 299 White Male PBO VIS3 PT75
#> 300 White Male PBO VIS4 PT75
#> 301 White Male TRT VIS1 PT76
#> 304 White Male TRT VIS4 PT76
#> 305 Asian Male TRT VIS1 PT77
#> 306 Asian Male TRT VIS2 PT77
#> 307 Asian Male TRT VIS3 PT77
#> 308 Asian Male TRT VIS4 PT77
#> 310 White Female TRT VIS2 PT78
#> 311 White Female TRT VIS3 PT78
#> 312 White Female TRT VIS4 PT78
#> 316 Asian Female PBO VIS4 PT79
#> 317 Asian Male TRT VIS1 PT80
#> 318 Asian Male TRT VIS2 PT80
#> 319 Asian Male TRT VIS3 PT80
#> 322 White Male PBO VIS2 PT81
#> 323 White Male PBO VIS3 PT81
#> 324 White Male PBO VIS4 PT81
#> 325 Black or African American Female PBO VIS1 PT82
#> 327 Black or African American Female PBO VIS3 PT82
#> 328 Black or African American Female PBO VIS4 PT82
#> 329 White Male TRT VIS1 PT83
#> 330 White Male TRT VIS2 PT83
#> 331 White Male TRT VIS3 PT83
#> 332 White Male TRT VIS4 PT83
#> 336 Asian Female PBO VIS4 PT84
#> 339 Asian Female PBO VIS3 PT85
#> 340 Asian Female PBO VIS4 PT85
#> 341 Black or African American Female TRT VIS1 PT86
#> 342 Black or African American Female TRT VIS2 PT86
#> 343 Black or African American Female TRT VIS3 PT86
#> 344 Black or African American Female TRT VIS4 PT86
#> 345 White Male PBO VIS1 PT87
#> 347 White Male PBO VIS3 PT87
#> 349 Black or African American Female PBO VIS1 PT88
#> 351 Black or African American Female PBO VIS3 PT88
#> 352 Black or African American Female PBO VIS4 PT88
#> 353 Black or African American Male PBO VIS1 PT89
#> 354 Black or African American Male PBO VIS2 PT89
#> 355 Black or African American Male PBO VIS3 PT89
#> 356 Black or African American Male PBO VIS4 PT89
#> 357 Asian Male PBO VIS1 PT90
#> 363 White Female TRT VIS3 PT91
#> 364 White Female TRT VIS4 PT91
#> 365 White Female TRT VIS1 PT92
#> 367 White Female TRT VIS3 PT92
#> 368 White Female TRT VIS4 PT92
#> 370 Black or African American Male PBO VIS2 PT93
#> 371 Black or African American Male PBO VIS3 PT93
#> 372 Black or African American Male PBO VIS4 PT93
#> 373 Asian Female PBO VIS1 PT94
#> 375 Asian Female PBO VIS3 PT94
#> 376 Asian Female PBO VIS4 PT94
#> 378 Asian Female PBO VIS2 PT95
#> 379 Asian Female PBO VIS3 PT95
#> 381 Black or African American Female PBO VIS1 PT96
#> 382 Black or African American Female PBO VIS2 PT96
#> 384 Black or African American Female PBO VIS4 PT96
#> 385 White Male TRT VIS1 PT97
#> 386 White Male TRT VIS2 PT97
#> 388 White Male TRT VIS4 PT97
#> 389 Asian Male PBO VIS1 PT98
#> 390 Asian Male PBO VIS2 PT98
#> 391 Asian Male PBO VIS3 PT98
#> 392 Asian Male PBO VIS4 PT98
#> 394 Black or African American Male PBO VIS2 PT99
#> 397 Black or African American Female PBO VIS1 PT100
#> 398 Black or African American Female PBO VIS2 PT100
#> 399 Black or African American Female PBO VIS3 PT100
#> 402 White Male TRT VIS2 PT101
#> 403 White Male TRT VIS3 PT101
#> 405 Asian Female PBO VIS1 PT102
#> 406 Asian Female PBO VIS2 PT102
#> 407 Asian Female PBO VIS3 PT102
#> 408 Asian Female PBO VIS4 PT102
#> 409 Asian Female PBO VIS1 PT103
#> 410 Asian Female PBO VIS2 PT103
#> 411 Asian Female PBO VIS3 PT103
#> 413 Black or African American Female PBO VIS1 PT104
#> 415 Black or African American Female PBO VIS3 PT104
#> 416 Black or African American Female PBO VIS4 PT104
#> 418 Black or African American Female TRT VIS2 PT105
#> 419 Black or African American Female TRT VIS3 PT105
#> 421 Asian Female TRT VIS1 PT106
#> 422 Asian Female TRT VIS2 PT106
#> 423 Asian Female TRT VIS3 PT106
#> 424 Asian Female TRT VIS4 PT106
#> 427 White Female PBO VIS3 PT107
#> 428 White Female PBO VIS4 PT107
#> 429 White Male TRT VIS1 PT108
#> 430 White Male TRT VIS2 PT108
#> 431 White Male TRT VIS3 PT108
#> 432 White Male TRT VIS4 PT108
#> 434 Black or African American Female PBO VIS2 PT109
#> 435 Black or African American Female PBO VIS3 PT109
#> 436 Black or African American Female PBO VIS4 PT109
#> 438 Asian Male TRT VIS2 PT110
#> 439 Asian Male TRT VIS3 PT110
#> 444 White Female PBO VIS4 PT111
#> 445 Black or African American Female PBO VIS1 PT112
#> 447 Black or African American Female PBO VIS3 PT112
#> 449 Asian Male PBO VIS1 PT113
#> 450 Asian Male PBO VIS2 PT113
#> 451 Asian Male PBO VIS3 PT113
#> 453 Black or African American Male PBO VIS1 PT114
#> 454 Black or African American Male PBO VIS2 PT114
#> 455 Black or African American Male PBO VIS3 PT114
#> 456 Black or African American Male PBO VIS4 PT114
#> 457 Asian Female PBO VIS1 PT115
#> 458 Asian Female PBO VIS2 PT115
#> 459 Asian Female PBO VIS3 PT115
#> 461 Black or African American Male TRT VIS1 PT116
#> 463 Black or African American Male TRT VIS3 PT116
#> 464 Black or African American Male TRT VIS4 PT116
#> 465 Black or African American Male PBO VIS1 PT117
#> 469 Asian Female PBO VIS1 PT118
#> 470 Asian Female PBO VIS2 PT118
#> 471 Asian Female PBO VIS3 PT118
#> 473 White Male TRT VIS1 PT119
#> 474 White Male TRT VIS2 PT119
#> 477 Asian Female PBO VIS1 PT120
#> 484 Black or African American Male TRT VIS4 PT121
#> 487 Asian Female PBO VIS3 PT122
#> 489 White Female PBO VIS1 PT123
#> 490 White Female PBO VIS2 PT123
#> 491 White Female PBO VIS3 PT123
#> 494 Asian Female TRT VIS2 PT124
#> 495 Asian Female TRT VIS3 PT124
#> 496 Asian Female TRT VIS4 PT124
#> 497 White Male PBO VIS1 PT125
#> 498 White Male PBO VIS2 PT125
#> 499 White Male PBO VIS3 PT125
#> 501 Asian Male TRT VIS1 PT126
#> 502 Asian Male TRT VIS2 PT126
#> 504 Asian Male TRT VIS4 PT126
#> 505 Black or African American Female TRT VIS1 PT127
#> 508 Black or African American Female TRT VIS4 PT127
#> 509 Asian Female TRT VIS1 PT128
#> 510 Asian Female TRT VIS2 PT128
#> 511 Asian Female TRT VIS3 PT128
#> 512 Asian Female TRT VIS4 PT128
#> 513 White Male TRT VIS1 PT129
#> 518 White Male PBO VIS2 PT130
#> 519 White Male PBO VIS3 PT130
#> 521 Asian Female PBO VIS1 PT131
#> 522 Asian Female PBO VIS2 PT131
#> 523 Asian Female PBO VIS3 PT131
#> 524 Asian Female PBO VIS4 PT131
#> 526 Asian Male PBO VIS2 PT132
#> 527 Asian Male PBO VIS3 PT132
#> 528 Asian Male PBO VIS4 PT132
#> 530 White Male PBO VIS2 PT133
#> 531 White Male PBO VIS3 PT133
#> 532 White Male PBO VIS4 PT133
#> 534 Black or African American Male PBO VIS2 PT134
#> 535 Black or African American Male PBO VIS3 PT134
#> 536 Black or African American Male PBO VIS4 PT134
#> 537 Black or African American Male PBO VIS1 PT135
#> 538 Black or African American Male PBO VIS2 PT135
#> 539 Black or African American Male PBO VIS3 PT135
#> 540 Black or African American Male PBO VIS4 PT135
#> 541 White Female TRT VIS1 PT136
#> 544 White Female TRT VIS4 PT136
#> 545 Asian Female TRT VIS1 PT137
#> 546 Asian Female TRT VIS2 PT137
#> 547 Asian Female TRT VIS3 PT137
#> 548 Asian Female TRT VIS4 PT137
#> 549 Asian Male TRT VIS1 PT138
#> 550 Asian Male TRT VIS2 PT138
#> 551 Asian Male TRT VIS3 PT138
#> 555 Asian Female TRT VIS3 PT139
#> 556 Asian Female TRT VIS4 PT139
#> 557 Black or African American Female PBO VIS1 PT140
#> 558 Black or African American Female PBO VIS2 PT140
#> 560 Black or African American Female PBO VIS4 PT140
#> 562 White Male TRT VIS2 PT141
#> 564 White Male TRT VIS4 PT141
#> 569 Black or African American Female TRT VIS1 PT143
#> 570 Black or African American Female TRT VIS2 PT143
#> 572 Black or African American Female TRT VIS4 PT143
#> 573 Asian Female PBO VIS1 PT144
#> 574 Asian Female PBO VIS2 PT144
#> 575 Asian Female PBO VIS3 PT144
#> 576 Asian Female PBO VIS4 PT144
#> 577 Black or African American Male PBO VIS1 PT145
#> 578 Black or African American Male PBO VIS2 PT145
#> 579 Black or African American Male PBO VIS3 PT145
#> 582 Asian Female TRT VIS2 PT146
#> 583 Asian Female TRT VIS3 PT146
#> 584 Asian Female TRT VIS4 PT146
#> 585 Asian Female PBO VIS1 PT147
#> 586 Asian Female PBO VIS2 PT147
#> 587 Asian Female PBO VIS3 PT147
#> 590 Black or African American Male TRT VIS2 PT148
#> 591 Black or African American Male TRT VIS3 PT148
#> 593 Asian Female PBO VIS1 PT149
#> 594 Asian Female PBO VIS2 PT149
#> 595 Asian Female PBO VIS3 PT149
#> 596 Asian Female PBO VIS4 PT149
#> 599 White Male PBO VIS3 PT150
#> 600 White Male PBO VIS4 PT150
#> 601 Asian Female PBO VIS1 PT151
#> 602 Asian Female PBO VIS2 PT151
#> 604 Asian Female PBO VIS4 PT151
#> 606 Asian Female TRT VIS2 PT152
#> 608 Asian Female TRT VIS4 PT152
#> 609 Black or African American Male PBO VIS1 PT153
#> 610 Black or African American Male PBO VIS2 PT153
#> 611 Black or African American Male PBO VIS3 PT153
#> 612 Black or African American Male PBO VIS4 PT153
#> 613 Black or African American Female PBO VIS1 PT154
#> 614 Black or African American Female PBO VIS2 PT154
#> 616 Black or African American Female PBO VIS4 PT154
#> 617 Asian Male TRT VIS1 PT155
#> 619 Asian Male TRT VIS3 PT155
#> 620 Asian Male TRT VIS4 PT155
#> 621 White Male PBO VIS1 PT156
#> 622 White Male PBO VIS2 PT156
#> 623 White Male PBO VIS3 PT156
#> 624 White Male PBO VIS4 PT156
#> 625 Black or African American Male TRT VIS1 PT157
#> 628 Black or African American Male TRT VIS4 PT157
#> 630 Black or African American Male TRT VIS2 PT158
#> 631 Black or African American Male TRT VIS3 PT158
#> 632 Black or African American Male TRT VIS4 PT158
#> 633 Asian Female TRT VIS1 PT159
#> 634 Asian Female TRT VIS2 PT159
#> 638 Asian Male TRT VIS2 PT160
#> 639 Asian Male TRT VIS3 PT160
#> 640 Asian Male TRT VIS4 PT160
#> 642 Black or African American Female PBO VIS2 PT161
#> 645 Asian Male PBO VIS1 PT162
#> 648 Asian Male PBO VIS4 PT162
#> 650 White Male PBO VIS2 PT163
#> 652 White Male PBO VIS4 PT163
#> 654 Black or African American Female PBO VIS2 PT164
#> 655 Black or African American Female PBO VIS3 PT164
#> 656 Black or African American Female PBO VIS4 PT164
#> 657 White Female TRT VIS1 PT165
#> 661 Asian Male TRT VIS1 PT166
#> 665 Black or African American Male PBO VIS1 PT167
#> 666 Black or African American Male PBO VIS2 PT167
#> 668 Black or African American Male PBO VIS4 PT167
#> 669 Black or African American Female PBO VIS1 PT168
#> 670 Black or African American Female PBO VIS2 PT168
#> 671 Black or African American Female PBO VIS3 PT168
#> 673 White Female PBO VIS1 PT169
#> 674 White Female PBO VIS2 PT169
#> 678 White Female TRT VIS2 PT170
#> 679 White Female TRT VIS3 PT170
#> 680 White Female TRT VIS4 PT170
#> 682 Asian Female PBO VIS2 PT171
#> 684 Asian Female PBO VIS4 PT171
#> 685 Asian Female TRT VIS1 PT172
#> 686 Asian Female TRT VIS2 PT172
#> 687 Asian Female TRT VIS3 PT172
#> 689 White Female TRT VIS1 PT173
#> 690 White Female TRT VIS2 PT173
#> 691 White Female TRT VIS3 PT173
#> 693 Black or African American Female TRT VIS1 PT174
#> 694 Black or African American Female TRT VIS2 PT174
#> 695 Black or African American Female TRT VIS3 PT174
#> 697 Black or African American Female PBO VIS1 PT175
#> 698 Black or African American Female PBO VIS2 PT175
#> 700 Black or African American Female PBO VIS4 PT175
#> 701 Black or African American Male TRT VIS1 PT176
#> 702 Black or African American Male TRT VIS2 PT176
#> 704 Black or African American Male TRT VIS4 PT176
#> 707 Black or African American Male PBO VIS3 PT177
#> 709 Black or African American Male PBO VIS1 PT178
#> 712 Black or African American Male PBO VIS4 PT178
#> 713 White Male TRT VIS1 PT179
#> 715 White Male TRT VIS3 PT179
#> 716 White Male TRT VIS4 PT179
#> 717 Asian Male PBO VIS1 PT180
#> 718 Asian Male PBO VIS2 PT180
#> 719 Asian Male PBO VIS3 PT180
#> 721 Asian Female PBO VIS1 PT181
#> 723 Asian Female PBO VIS3 PT181
#> 724 Asian Female PBO VIS4 PT181
#> 727 Black or African American Female TRT VIS3 PT182
#> 728 Black or African American Female TRT VIS4 PT182
#> 729 Black or African American Female TRT VIS1 PT183
#> 730 Black or African American Female TRT VIS2 PT183
#> 734 White Female TRT VIS2 PT184
#> 736 White Female TRT VIS4 PT184
#> 738 Black or African American Female PBO VIS2 PT185
#> 739 Black or African American Female PBO VIS3 PT185
#> 740 Black or African American Female PBO VIS4 PT185
#> 741 White Male PBO VIS1 PT186
#> 742 White Male PBO VIS2 PT186
#> 744 White Male PBO VIS4 PT186
#> 745 Black or African American Female PBO VIS1 PT187
#> 749 Black or African American Female PBO VIS1 PT188
#> 751 Black or African American Female PBO VIS3 PT188
#> 753 Asian Female TRT VIS1 PT189
#> 754 Asian Female TRT VIS2 PT189
#> 755 Asian Female TRT VIS3 PT189
#> 757 Black or African American Female TRT VIS1 PT190
#> 758 Black or African American Female TRT VIS2 PT190
#> 759 Black or African American Female TRT VIS3 PT190
#> 760 Black or African American Female TRT VIS4 PT190
#> 761 White Male TRT VIS1 PT191
#> 764 White Male TRT VIS4 PT191
#> 765 White Female TRT VIS1 PT192
#> 766 White Female TRT VIS2 PT192
#> 767 White Female TRT VIS3 PT192
#> 768 White Female TRT VIS4 PT192
#> 770 Asian Male PBO VIS2 PT193
#> 771 Asian Male PBO VIS3 PT193
#> 772 Asian Male PBO VIS4 PT193
#> 773 Black or African American Male PBO VIS1 PT194
#> 774 Black or African American Male PBO VIS2 PT194
#> 775 Black or African American Male PBO VIS3 PT194
#> 776 Black or African American Male PBO VIS4 PT194
#> 778 White Female TRT VIS2 PT195
#> 779 White Female TRT VIS3 PT195
#> 781 Asian Male PBO VIS1 PT196
#> 782 Asian Male PBO VIS2 PT196
#> 784 Asian Male PBO VIS4 PT196
#> 788 Black or African American Male PBO VIS4 PT197
#> 789 Black or African American Male TRT VIS1 PT198
#> 790 Black or African American Male TRT VIS2 PT198
#> 792 Black or African American Male TRT VIS4 PT198
#> 797 Black or African American Male PBO VIS1 PT200
#> 798 Black or African American Male PBO VIS2 PT200
#> 800 Black or African American Male PBO VIS4 PT200
# Model matrix:
model.matrix(object)
#> (Intercept) RACEBlack or African American RACEWhite SEXFemale ARMCDTRT
#> 2 1 1 0 1 1
#> 4 1 1 0 1 1
#> 6 1 0 0 0 0
#> 7 1 0 0 0 0
#> 8 1 0 0 0 0
#> 10 1 1 0 1 0
#> 12 1 1 0 1 0
#> 13 1 0 0 1 1
#> 14 1 0 0 1 1
#> 16 1 0 0 1 1
#> 17 1 1 0 0 0
#> 19 1 1 0 0 0
#> 20 1 1 0 0 0
#> 23 1 1 0 0 0
#> 25 1 0 0 1 0
#> 26 1 0 0 1 0
#> 28 1 0 0 1 0
#> 29 1 1 0 0 0
#> 30 1 1 0 0 0
#> 31 1 1 0 0 0
#> 32 1 1 0 0 0
#> 33 1 0 1 0 1
#> 34 1 0 1 0 1
#> 36 1 0 1 0 1
#> 39 1 1 0 1 0
#> 41 1 0 0 1 1
#> 42 1 0 0 1 1
#> 43 1 0 0 1 1
#> 44 1 0 0 1 1
#> 45 1 0 0 0 0
#> 46 1 0 0 0 0
#> 47 1 0 0 0 0
#> 51 1 0 1 0 1
#> 52 1 0 1 0 1
#> 55 1 1 0 0 0
#> 59 1 0 0 0 0
#> 60 1 0 0 0 0
#> 62 1 0 0 1 0
#> 64 1 0 0 1 0
#> 65 1 0 1 1 0
#> 68 1 0 1 1 0
#> 69 1 0 0 0 1
#> 70 1 0 0 0 1
#> 72 1 0 0 0 1
#> 73 1 0 0 0 1
#> 74 1 0 0 0 1
#> 75 1 0 0 0 1
#> 76 1 0 0 0 1
#> 78 1 0 1 1 1
#> 79 1 0 1 1 1
#> 82 1 0 1 0 1
#> 83 1 0 1 0 1
#> 84 1 0 1 0 1
#> 85 1 0 1 0 1
#> 86 1 0 1 0 1
#> 87 1 0 1 0 1
#> 88 1 0 1 0 1
#> 89 1 1 0 1 0
#> 90 1 1 0 1 0
#> 91 1 1 0 1 0
#> 93 1 0 1 1 1
#> 94 1 0 1 1 1
#> 95 1 0 1 1 1
#> 96 1 0 1 1 1
#> 97 1 0 1 1 1
#> 98 1 0 1 1 1
#> 99 1 0 1 1 1
#> 100 1 0 1 1 1
#> 101 1 0 1 1 1
#> 102 1 0 1 1 1
#> 103 1 0 1 1 1
#> 104 1 0 1 1 1
#> 105 1 1 0 1 1
#> 107 1 1 0 1 1
#> 108 1 1 0 1 1
#> 109 1 1 0 0 1
#> 110 1 1 0 0 1
#> 111 1 1 0 0 1
#> 112 1 1 0 0 1
#> 113 1 1 0 0 1
#> 114 1 1 0 0 1
#> 116 1 1 0 0 1
#> 117 1 0 0 1 1
#> 118 1 0 0 1 1
#> 119 1 0 0 1 1
#> 120 1 0 0 1 1
#> 121 1 0 0 1 0
#> 123 1 0 0 1 0
#> 125 1 1 0 0 1
#> 128 1 1 0 0 1
#> 129 1 1 0 0 1
#> 130 1 1 0 0 1
#> 132 1 1 0 0 1
#> 133 1 0 0 1 0
#> 134 1 0 0 1 0
#> 135 1 0 0 1 0
#> 136 1 0 0 1 0
#> 137 1 1 0 1 1
#> 138 1 1 0 1 1
#> 140 1 1 0 1 1
#> 142 1 0 0 1 1
#> 144 1 0 0 1 1
#> 145 1 0 1 1 0
#> 146 1 0 1 1 0
#> 147 1 0 1 1 0
#> 148 1 0 1 1 0
#> 149 1 0 0 1 0
#> 151 1 0 0 1 0
#> 153 1 0 0 1 0
#> 155 1 0 0 1 0
#> 156 1 0 0 1 0
#> 157 1 1 0 0 0
#> 158 1 1 0 0 0
#> 159 1 1 0 0 0
#> 162 1 1 0 0 0
#> 163 1 1 0 0 0
#> 164 1 1 0 0 0
#> 165 1 1 0 0 0
#> 168 1 1 0 0 0
#> 169 1 1 0 1 1
#> 170 1 1 0 1 1
#> 171 1 1 0 1 1
#> 172 1 1 0 1 1
#> 173 1 1 0 0 1
#> 177 1 0 0 1 0
#> 178 1 0 0 1 0
#> 179 1 0 0 1 0
#> 180 1 0 0 1 0
#> 181 1 0 0 0 0
#> 182 1 0 0 0 0
#> 183 1 0 0 0 0
#> 185 1 0 0 0 1
#> 186 1 0 0 0 1
#> 187 1 0 0 0 1
#> 190 1 1 0 0 0
#> 191 1 1 0 0 0
#> 193 1 0 1 1 0
#> 194 1 0 1 1 0
#> 195 1 0 1 1 0
#> 197 1 0 0 1 1
#> 198 1 0 0 1 1
#> 199 1 0 0 1 1
#> 201 1 1 0 1 1
#> 202 1 1 0 1 1
#> 204 1 1 0 1 1
#> 206 1 1 0 1 1
#> 208 1 1 0 1 1
#> 209 1 0 1 0 0
#> 210 1 0 1 0 0
#> 217 1 0 0 0 1
#> 218 1 0 0 0 1
#> 219 1 0 0 0 1
#> 221 1 1 0 0 0
#> 224 1 1 0 0 0
#> 226 1 1 0 0 1
#> 227 1 1 0 0 1
#> 228 1 1 0 0 1
#> 230 1 0 1 1 1
#> 231 1 0 1 1 1
#> 233 1 0 1 1 1
#> 235 1 0 1 1 1
#> 236 1 0 1 1 1
#> 237 1 1 0 1 1
#> 238 1 1 0 1 1
#> 239 1 1 0 1 1
#> 240 1 1 0 1 1
#> 241 1 0 0 0 1
#> 242 1 0 0 0 1
#> 244 1 0 0 0 1
#> 246 1 0 0 1 0
#> 250 1 0 1 1 0
#> 251 1 0 1 1 0
#> 252 1 0 1 1 0
#> 253 1 1 0 1 0
#> 254 1 1 0 1 0
#> 256 1 1 0 1 0
#> 257 1 0 0 1 1
#> 258 1 0 0 1 1
#> 259 1 0 0 1 1
#> 260 1 0 0 1 1
#> 261 1 0 0 0 1
#> 262 1 0 0 0 1
#> 263 1 0 0 0 1
#> 264 1 0 0 0 1
#> 265 1 0 1 1 1
#> 266 1 0 1 1 1
#> 267 1 0 1 1 1
#> 268 1 0 1 1 1
#> 269 1 0 1 1 1
#> 270 1 0 1 1 1
#> 273 1 1 0 0 0
#> 274 1 1 0 0 0
#> 275 1 1 0 0 0
#> 276 1 1 0 0 0
#> 277 1 1 0 0 0
#> 278 1 1 0 0 0
#> 280 1 1 0 0 0
#> 281 1 0 0 0 1
#> 282 1 0 0 0 1
#> 283 1 0 0 0 1
#> 284 1 0 0 0 1
#> 285 1 0 0 1 1
#> 286 1 0 0 1 1
#> 287 1 0 0 1 1
#> 291 1 0 1 0 0
#> 292 1 0 1 0 0
#> 293 1 0 1 1 0
#> 295 1 0 1 1 0
#> 296 1 0 1 1 0
#> 298 1 0 1 0 0
#> 299 1 0 1 0 0
#> 300 1 0 1 0 0
#> 301 1 0 1 0 1
#> 304 1 0 1 0 1
#> 305 1 0 0 0 1
#> 306 1 0 0 0 1
#> 307 1 0 0 0 1
#> 308 1 0 0 0 1
#> 310 1 0 1 1 1
#> 311 1 0 1 1 1
#> 312 1 0 1 1 1
#> 316 1 0 0 1 0
#> 317 1 0 0 0 1
#> 318 1 0 0 0 1
#> 319 1 0 0 0 1
#> 322 1 0 1 0 0
#> 323 1 0 1 0 0
#> 324 1 0 1 0 0
#> 325 1 1 0 1 0
#> 327 1 1 0 1 0
#> 328 1 1 0 1 0
#> 329 1 0 1 0 1
#> 330 1 0 1 0 1
#> 331 1 0 1 0 1
#> 332 1 0 1 0 1
#> 336 1 0 0 1 0
#> 339 1 0 0 1 0
#> 340 1 0 0 1 0
#> 341 1 1 0 1 1
#> 342 1 1 0 1 1
#> 343 1 1 0 1 1
#> 344 1 1 0 1 1
#> 345 1 0 1 0 0
#> 347 1 0 1 0 0
#> 349 1 1 0 1 0
#> 351 1 1 0 1 0
#> 352 1 1 0 1 0
#> 353 1 1 0 0 0
#> 354 1 1 0 0 0
#> 355 1 1 0 0 0
#> 356 1 1 0 0 0
#> 357 1 0 0 0 0
#> 363 1 0 1 1 1
#> 364 1 0 1 1 1
#> 365 1 0 1 1 1
#> 367 1 0 1 1 1
#> 368 1 0 1 1 1
#> 370 1 1 0 0 0
#> 371 1 1 0 0 0
#> 372 1 1 0 0 0
#> 373 1 0 0 1 0
#> 375 1 0 0 1 0
#> 376 1 0 0 1 0
#> 378 1 0 0 1 0
#> 379 1 0 0 1 0
#> 381 1 1 0 1 0
#> 382 1 1 0 1 0
#> 384 1 1 0 1 0
#> 385 1 0 1 0 1
#> 386 1 0 1 0 1
#> 388 1 0 1 0 1
#> 389 1 0 0 0 0
#> 390 1 0 0 0 0
#> 391 1 0 0 0 0
#> 392 1 0 0 0 0
#> 394 1 1 0 0 0
#> 397 1 1 0 1 0
#> 398 1 1 0 1 0
#> 399 1 1 0 1 0
#> 402 1 0 1 0 1
#> 403 1 0 1 0 1
#> 405 1 0 0 1 0
#> 406 1 0 0 1 0
#> 407 1 0 0 1 0
#> 408 1 0 0 1 0
#> 409 1 0 0 1 0
#> 410 1 0 0 1 0
#> 411 1 0 0 1 0
#> 413 1 1 0 1 0
#> 415 1 1 0 1 0
#> 416 1 1 0 1 0
#> 418 1 1 0 1 1
#> 419 1 1 0 1 1
#> 421 1 0 0 1 1
#> 422 1 0 0 1 1
#> 423 1 0 0 1 1
#> 424 1 0 0 1 1
#> 427 1 0 1 1 0
#> 428 1 0 1 1 0
#> 429 1 0 1 0 1
#> 430 1 0 1 0 1
#> 431 1 0 1 0 1
#> 432 1 0 1 0 1
#> 434 1 1 0 1 0
#> 435 1 1 0 1 0
#> 436 1 1 0 1 0
#> 438 1 0 0 0 1
#> 439 1 0 0 0 1
#> 444 1 0 1 1 0
#> 445 1 1 0 1 0
#> 447 1 1 0 1 0
#> 449 1 0 0 0 0
#> 450 1 0 0 0 0
#> 451 1 0 0 0 0
#> 453 1 1 0 0 0
#> 454 1 1 0 0 0
#> 455 1 1 0 0 0
#> 456 1 1 0 0 0
#> 457 1 0 0 1 0
#> 458 1 0 0 1 0
#> 459 1 0 0 1 0
#> 461 1 1 0 0 1
#> 463 1 1 0 0 1
#> 464 1 1 0 0 1
#> 465 1 1 0 0 0
#> 469 1 0 0 1 0
#> 470 1 0 0 1 0
#> 471 1 0 0 1 0
#> 473 1 0 1 0 1
#> 474 1 0 1 0 1
#> 477 1 0 0 1 0
#> 484 1 1 0 0 1
#> 487 1 0 0 1 0
#> 489 1 0 1 1 0
#> 490 1 0 1 1 0
#> 491 1 0 1 1 0
#> 494 1 0 0 1 1
#> 495 1 0 0 1 1
#> 496 1 0 0 1 1
#> 497 1 0 1 0 0
#> 498 1 0 1 0 0
#> 499 1 0 1 0 0
#> 501 1 0 0 0 1
#> 502 1 0 0 0 1
#> 504 1 0 0 0 1
#> 505 1 1 0 1 1
#> 508 1 1 0 1 1
#> 509 1 0 0 1 1
#> 510 1 0 0 1 1
#> 511 1 0 0 1 1
#> 512 1 0 0 1 1
#> 513 1 0 1 0 1
#> 518 1 0 1 0 0
#> 519 1 0 1 0 0
#> 521 1 0 0 1 0
#> 522 1 0 0 1 0
#> 523 1 0 0 1 0
#> 524 1 0 0 1 0
#> 526 1 0 0 0 0
#> 527 1 0 0 0 0
#> 528 1 0 0 0 0
#> 530 1 0 1 0 0
#> 531 1 0 1 0 0
#> 532 1 0 1 0 0
#> 534 1 1 0 0 0
#> 535 1 1 0 0 0
#> 536 1 1 0 0 0
#> 537 1 1 0 0 0
#> 538 1 1 0 0 0
#> 539 1 1 0 0 0
#> 540 1 1 0 0 0
#> 541 1 0 1 1 1
#> 544 1 0 1 1 1
#> 545 1 0 0 1 1
#> 546 1 0 0 1 1
#> 547 1 0 0 1 1
#> 548 1 0 0 1 1
#> 549 1 0 0 0 1
#> 550 1 0 0 0 1
#> 551 1 0 0 0 1
#> 555 1 0 0 1 1
#> 556 1 0 0 1 1
#> 557 1 1 0 1 0
#> 558 1 1 0 1 0
#> 560 1 1 0 1 0
#> 562 1 0 1 0 1
#> 564 1 0 1 0 1
#> 569 1 1 0 1 1
#> 570 1 1 0 1 1
#> 572 1 1 0 1 1
#> 573 1 0 0 1 0
#> 574 1 0 0 1 0
#> 575 1 0 0 1 0
#> 576 1 0 0 1 0
#> 577 1 1 0 0 0
#> 578 1 1 0 0 0
#> 579 1 1 0 0 0
#> 582 1 0 0 1 1
#> 583 1 0 0 1 1
#> 584 1 0 0 1 1
#> 585 1 0 0 1 0
#> 586 1 0 0 1 0
#> 587 1 0 0 1 0
#> 590 1 1 0 0 1
#> 591 1 1 0 0 1
#> 593 1 0 0 1 0
#> 594 1 0 0 1 0
#> 595 1 0 0 1 0
#> 596 1 0 0 1 0
#> 599 1 0 1 0 0
#> 600 1 0 1 0 0
#> 601 1 0 0 1 0
#> 602 1 0 0 1 0
#> 604 1 0 0 1 0
#> 606 1 0 0 1 1
#> 608 1 0 0 1 1
#> 609 1 1 0 0 0
#> 610 1 1 0 0 0
#> 611 1 1 0 0 0
#> 612 1 1 0 0 0
#> 613 1 1 0 1 0
#> 614 1 1 0 1 0
#> 616 1 1 0 1 0
#> 617 1 0 0 0 1
#> 619 1 0 0 0 1
#> 620 1 0 0 0 1
#> 621 1 0 1 0 0
#> 622 1 0 1 0 0
#> 623 1 0 1 0 0
#> 624 1 0 1 0 0
#> 625 1 1 0 0 1
#> 628 1 1 0 0 1
#> 630 1 1 0 0 1
#> 631 1 1 0 0 1
#> 632 1 1 0 0 1
#> 633 1 0 0 1 1
#> 634 1 0 0 1 1
#> 638 1 0 0 0 1
#> 639 1 0 0 0 1
#> 640 1 0 0 0 1
#> 642 1 1 0 1 0
#> 645 1 0 0 0 0
#> 648 1 0 0 0 0
#> 650 1 0 1 0 0
#> 652 1 0 1 0 0
#> 654 1 1 0 1 0
#> 655 1 1 0 1 0
#> 656 1 1 0 1 0
#> 657 1 0 1 1 1
#> 661 1 0 0 0 1
#> 665 1 1 0 0 0
#> 666 1 1 0 0 0
#> 668 1 1 0 0 0
#> 669 1 1 0 1 0
#> 670 1 1 0 1 0
#> 671 1 1 0 1 0
#> 673 1 0 1 1 0
#> 674 1 0 1 1 0
#> 678 1 0 1 1 1
#> 679 1 0 1 1 1
#> 680 1 0 1 1 1
#> 682 1 0 0 1 0
#> 684 1 0 0 1 0
#> 685 1 0 0 1 1
#> 686 1 0 0 1 1
#> 687 1 0 0 1 1
#> 689 1 0 1 1 1
#> 690 1 0 1 1 1
#> 691 1 0 1 1 1
#> 693 1 1 0 1 1
#> 694 1 1 0 1 1
#> 695 1 1 0 1 1
#> 697 1 1 0 1 0
#> 698 1 1 0 1 0
#> 700 1 1 0 1 0
#> 701 1 1 0 0 1
#> 702 1 1 0 0 1
#> 704 1 1 0 0 1
#> 707 1 1 0 0 0
#> 709 1 1 0 0 0
#> 712 1 1 0 0 0
#> 713 1 0 1 0 1
#> 715 1 0 1 0 1
#> 716 1 0 1 0 1
#> 717 1 0 0 0 0
#> 718 1 0 0 0 0
#> 719 1 0 0 0 0
#> 721 1 0 0 1 0
#> 723 1 0 0 1 0
#> 724 1 0 0 1 0
#> 727 1 1 0 1 1
#> 728 1 1 0 1 1
#> 729 1 1 0 1 1
#> 730 1 1 0 1 1
#> 734 1 0 1 1 1
#> 736 1 0 1 1 1
#> 738 1 1 0 1 0
#> 739 1 1 0 1 0
#> 740 1 1 0 1 0
#> 741 1 0 1 0 0
#> 742 1 0 1 0 0
#> 744 1 0 1 0 0
#> 745 1 1 0 1 0
#> 749 1 1 0 1 0
#> 751 1 1 0 1 0
#> 753 1 0 0 1 1
#> 754 1 0 0 1 1
#> 755 1 0 0 1 1
#> 757 1 1 0 1 1
#> 758 1 1 0 1 1
#> 759 1 1 0 1 1
#> 760 1 1 0 1 1
#> 761 1 0 1 0 1
#> 764 1 0 1 0 1
#> 765 1 0 1 1 1
#> 766 1 0 1 1 1
#> 767 1 0 1 1 1
#> 768 1 0 1 1 1
#> 770 1 0 0 0 0
#> 771 1 0 0 0 0
#> 772 1 0 0 0 0
#> 773 1 1 0 0 0
#> 774 1 1 0 0 0
#> 775 1 1 0 0 0
#> 776 1 1 0 0 0
#> 778 1 0 1 1 1
#> 779 1 0 1 1 1
#> 781 1 0 0 0 0
#> 782 1 0 0 0 0
#> 784 1 0 0 0 0
#> 788 1 1 0 0 0
#> 789 1 1 0 0 1
#> 790 1 1 0 0 1
#> 792 1 1 0 0 1
#> 797 1 1 0 0 0
#> 798 1 1 0 0 0
#> 800 1 1 0 0 0
#> AVISITVIS2 AVISITVIS3 AVISITVIS4 ARMCDTRT:AVISITVIS2 ARMCDTRT:AVISITVIS3
#> 2 1 0 0 1 0
#> 4 0 0 1 0 0
#> 6 1 0 0 0 0
#> 7 0 1 0 0 0
#> 8 0 0 1 0 0
#> 10 1 0 0 0 0
#> 12 0 0 1 0 0
#> 13 0 0 0 0 0
#> 14 1 0 0 1 0
#> 16 0 0 1 0 0
#> 17 0 0 0 0 0
#> 19 0 1 0 0 0
#> 20 0 0 1 0 0
#> 23 0 1 0 0 0
#> 25 0 0 0 0 0
#> 26 1 0 0 0 0
#> 28 0 0 1 0 0
#> 29 0 0 0 0 0
#> 30 1 0 0 0 0
#> 31 0 1 0 0 0
#> 32 0 0 1 0 0
#> 33 0 0 0 0 0
#> 34 1 0 0 1 0
#> 36 0 0 1 0 0
#> 39 0 1 0 0 0
#> 41 0 0 0 0 0
#> 42 1 0 0 1 0
#> 43 0 1 0 0 1
#> 44 0 0 1 0 0
#> 45 0 0 0 0 0
#> 46 1 0 0 0 0
#> 47 0 1 0 0 0
#> 51 0 1 0 0 1
#> 52 0 0 1 0 0
#> 55 0 1 0 0 0
#> 59 0 1 0 0 0
#> 60 0 0 1 0 0
#> 62 1 0 0 0 0
#> 64 0 0 1 0 0
#> 65 0 0 0 0 0
#> 68 0 0 1 0 0
#> 69 0 0 0 0 0
#> 70 1 0 0 1 0
#> 72 0 0 1 0 0
#> 73 0 0 0 0 0
#> 74 1 0 0 1 0
#> 75 0 1 0 0 1
#> 76 0 0 1 0 0
#> 78 1 0 0 1 0
#> 79 0 1 0 0 1
#> 82 1 0 0 1 0
#> 83 0 1 0 0 1
#> 84 0 0 1 0 0
#> 85 0 0 0 0 0
#> 86 1 0 0 1 0
#> 87 0 1 0 0 1
#> 88 0 0 1 0 0
#> 89 0 0 0 0 0
#> 90 1 0 0 0 0
#> 91 0 1 0 0 0
#> 93 0 0 0 0 0
#> 94 1 0 0 1 0
#> 95 0 1 0 0 1
#> 96 0 0 1 0 0
#> 97 0 0 0 0 0
#> 98 1 0 0 1 0
#> 99 0 1 0 0 1
#> 100 0 0 1 0 0
#> 101 0 0 0 0 0
#> 102 1 0 0 1 0
#> 103 0 1 0 0 1
#> 104 0 0 1 0 0
#> 105 0 0 0 0 0
#> 107 0 1 0 0 1
#> 108 0 0 1 0 0
#> 109 0 0 0 0 0
#> 110 1 0 0 1 0
#> 111 0 1 0 0 1
#> 112 0 0 1 0 0
#> 113 0 0 0 0 0
#> 114 1 0 0 1 0
#> 116 0 0 1 0 0
#> 117 0 0 0 0 0
#> 118 1 0 0 1 0
#> 119 0 1 0 0 1
#> 120 0 0 1 0 0
#> 121 0 0 0 0 0
#> 123 0 1 0 0 0
#> 125 0 0 0 0 0
#> 128 0 0 1 0 0
#> 129 0 0 0 0 0
#> 130 1 0 0 1 0
#> 132 0 0 1 0 0
#> 133 0 0 0 0 0
#> 134 1 0 0 0 0
#> 135 0 1 0 0 0
#> 136 0 0 1 0 0
#> 137 0 0 0 0 0
#> 138 1 0 0 1 0
#> 140 0 0 1 0 0
#> 142 1 0 0 1 0
#> 144 0 0 1 0 0
#> 145 0 0 0 0 0
#> 146 1 0 0 0 0
#> 147 0 1 0 0 0
#> 148 0 0 1 0 0
#> 149 0 0 0 0 0
#> 151 0 1 0 0 0
#> 153 0 0 0 0 0
#> 155 0 1 0 0 0
#> 156 0 0 1 0 0
#> 157 0 0 0 0 0
#> 158 1 0 0 0 0
#> 159 0 1 0 0 0
#> 162 1 0 0 0 0
#> 163 0 1 0 0 0
#> 164 0 0 1 0 0
#> 165 0 0 0 0 0
#> 168 0 0 1 0 0
#> 169 0 0 0 0 0
#> 170 1 0 0 1 0
#> 171 0 1 0 0 1
#> 172 0 0 1 0 0
#> 173 0 0 0 0 0
#> 177 0 0 0 0 0
#> 178 1 0 0 0 0
#> 179 0 1 0 0 0
#> 180 0 0 1 0 0
#> 181 0 0 0 0 0
#> 182 1 0 0 0 0
#> 183 0 1 0 0 0
#> 185 0 0 0 0 0
#> 186 1 0 0 1 0
#> 187 0 1 0 0 1
#> 190 1 0 0 0 0
#> 191 0 1 0 0 0
#> 193 0 0 0 0 0
#> 194 1 0 0 0 0
#> 195 0 1 0 0 0
#> 197 0 0 0 0 0
#> 198 1 0 0 1 0
#> 199 0 1 0 0 1
#> 201 0 0 0 0 0
#> 202 1 0 0 1 0
#> 204 0 0 1 0 0
#> 206 1 0 0 1 0
#> 208 0 0 1 0 0
#> 209 0 0 0 0 0
#> 210 1 0 0 0 0
#> 217 0 0 0 0 0
#> 218 1 0 0 1 0
#> 219 0 1 0 0 1
#> 221 0 0 0 0 0
#> 224 0 0 1 0 0
#> 226 1 0 0 1 0
#> 227 0 1 0 0 1
#> 228 0 0 1 0 0
#> 230 1 0 0 1 0
#> 231 0 1 0 0 1
#> 233 0 0 0 0 0
#> 235 0 1 0 0 1
#> 236 0 0 1 0 0
#> 237 0 0 0 0 0
#> 238 1 0 0 1 0
#> 239 0 1 0 0 1
#> 240 0 0 1 0 0
#> 241 0 0 0 0 0
#> 242 1 0 0 1 0
#> 244 0 0 1 0 0
#> 246 1 0 0 0 0
#> 250 1 0 0 0 0
#> 251 0 1 0 0 0
#> 252 0 0 1 0 0
#> 253 0 0 0 0 0
#> 254 1 0 0 0 0
#> 256 0 0 1 0 0
#> 257 0 0 0 0 0
#> 258 1 0 0 1 0
#> 259 0 1 0 0 1
#> 260 0 0 1 0 0
#> 261 0 0 0 0 0
#> 262 1 0 0 1 0
#> 263 0 1 0 0 1
#> 264 0 0 1 0 0
#> 265 0 0 0 0 0
#> 266 1 0 0 1 0
#> 267 0 1 0 0 1
#> 268 0 0 1 0 0
#> 269 0 0 0 0 0
#> 270 1 0 0 1 0
#> 273 0 0 0 0 0
#> 274 1 0 0 0 0
#> 275 0 1 0 0 0
#> 276 0 0 1 0 0
#> 277 0 0 0 0 0
#> 278 1 0 0 0 0
#> 280 0 0 1 0 0
#> 281 0 0 0 0 0
#> 282 1 0 0 1 0
#> 283 0 1 0 0 1
#> 284 0 0 1 0 0
#> 285 0 0 0 0 0
#> 286 1 0 0 1 0
#> 287 0 1 0 0 1
#> 291 0 1 0 0 0
#> 292 0 0 1 0 0
#> 293 0 0 0 0 0
#> 295 0 1 0 0 0
#> 296 0 0 1 0 0
#> 298 1 0 0 0 0
#> 299 0 1 0 0 0
#> 300 0 0 1 0 0
#> 301 0 0 0 0 0
#> 304 0 0 1 0 0
#> 305 0 0 0 0 0
#> 306 1 0 0 1 0
#> 307 0 1 0 0 1
#> 308 0 0 1 0 0
#> 310 1 0 0 1 0
#> 311 0 1 0 0 1
#> 312 0 0 1 0 0
#> 316 0 0 1 0 0
#> 317 0 0 0 0 0
#> 318 1 0 0 1 0
#> 319 0 1 0 0 1
#> 322 1 0 0 0 0
#> 323 0 1 0 0 0
#> 324 0 0 1 0 0
#> 325 0 0 0 0 0
#> 327 0 1 0 0 0
#> 328 0 0 1 0 0
#> 329 0 0 0 0 0
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#> 332 0 0 1 0 0
#> 336 0 0 1 0 0
#> 339 0 1 0 0 0
#> 340 0 0 1 0 0
#> 341 0 0 0 0 0
#> 342 1 0 0 1 0
#> 343 0 1 0 0 1
#> 344 0 0 1 0 0
#> 345 0 0 0 0 0
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#> 353 0 0 0 0 0
#> 354 1 0 0 0 0
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#> 363 0 1 0 0 1
#> 364 0 0 1 0 0
#> 365 0 0 0 0 0
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#> 368 0 0 1 0 0
#> 370 1 0 0 0 0
#> 371 0 1 0 0 0
#> 372 0 0 1 0 0
#> 373 0 0 0 0 0
#> 375 0 1 0 0 0
#> 376 0 0 1 0 0
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#> 379 0 1 0 0 0
#> 381 0 0 0 0 0
#> 382 1 0 0 0 0
#> 384 0 0 1 0 0
#> 385 0 0 0 0 0
#> 386 1 0 0 1 0
#> 388 0 0 1 0 0
#> 389 0 0 0 0 0
#> 390 1 0 0 0 0
#> 391 0 1 0 0 0
#> 392 0 0 1 0 0
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#> 397 0 0 0 0 0
#> 398 1 0 0 0 0
#> 399 0 1 0 0 0
#> 402 1 0 0 1 0
#> 403 0 1 0 0 1
#> 405 0 0 0 0 0
#> 406 1 0 0 0 0
#> 407 0 1 0 0 0
#> 408 0 0 1 0 0
#> 409 0 0 0 0 0
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#> 411 0 1 0 0 0
#> 413 0 0 0 0 0
#> 415 0 1 0 0 0
#> 416 0 0 1 0 0
#> 418 1 0 0 1 0
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#> 421 0 0 0 0 0
#> 422 1 0 0 1 0
#> 423 0 1 0 0 1
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#> 427 0 1 0 0 0
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#> 431 0 1 0 0 1
#> 432 0 0 1 0 0
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#> 444 0 0 1 0 0
#> 445 0 0 0 0 0
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#> 450 1 0 0 0 0
#> 451 0 1 0 0 0
#> 453 0 0 0 0 0
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#> 455 0 1 0 0 0
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#> 457 0 0 0 0 0
#> 458 1 0 0 0 0
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#> 461 0 0 0 0 0
#> 463 0 1 0 0 1
#> 464 0 0 1 0 0
#> 465 0 0 0 0 0
#> 469 0 0 0 0 0
#> 470 1 0 0 0 0
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#> 477 0 0 0 0 0
#> 484 0 0 1 0 0
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#> 490 1 0 0 0 0
#> 491 0 1 0 0 0
#> 494 1 0 0 1 0
#> 495 0 1 0 0 1
#> 496 0 0 1 0 0
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#> 499 0 1 0 0 0
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#> 502 1 0 0 1 0
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#> 510 1 0 0 1 0
#> 511 0 1 0 0 1
#> 512 0 0 1 0 0
#> 513 0 0 0 0 0
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#> 519 0 1 0 0 0
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#> 522 1 0 0 0 0
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#> 530 1 0 0 0 0
#> 531 0 1 0 0 0
#> 532 0 0 1 0 0
#> 534 1 0 0 0 0
#> 535 0 1 0 0 0
#> 536 0 0 1 0 0
#> 537 0 0 0 0 0
#> 538 1 0 0 0 0
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#> 540 0 0 1 0 0
#> 541 0 0 0 0 0
#> 544 0 0 1 0 0
#> 545 0 0 0 0 0
#> 546 1 0 0 1 0
#> 547 0 1 0 0 1
#> 548 0 0 1 0 0
#> 549 0 0 0 0 0
#> 550 1 0 0 1 0
#> 551 0 1 0 0 1
#> 555 0 1 0 0 1
#> 556 0 0 1 0 0
#> 557 0 0 0 0 0
#> 558 1 0 0 0 0
#> 560 0 0 1 0 0
#> 562 1 0 0 1 0
#> 564 0 0 1 0 0
#> 569 0 0 0 0 0
#> 570 1 0 0 1 0
#> 572 0 0 1 0 0
#> 573 0 0 0 0 0
#> 574 1 0 0 0 0
#> 575 0 1 0 0 0
#> 576 0 0 1 0 0
#> 577 0 0 0 0 0
#> 578 1 0 0 0 0
#> 579 0 1 0 0 0
#> 582 1 0 0 1 0
#> 583 0 1 0 0 1
#> 584 0 0 1 0 0
#> 585 0 0 0 0 0
#> 586 1 0 0 0 0
#> 587 0 1 0 0 0
#> 590 1 0 0 1 0
#> 591 0 1 0 0 1
#> 593 0 0 0 0 0
#> 594 1 0 0 0 0
#> 595 0 1 0 0 0
#> 596 0 0 1 0 0
#> 599 0 1 0 0 0
#> 600 0 0 1 0 0
#> 601 0 0 0 0 0
#> 602 1 0 0 0 0
#> 604 0 0 1 0 0
#> 606 1 0 0 1 0
#> 608 0 0 1 0 0
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#> 611 0 1 0 0 0
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#> 613 0 0 0 0 0
#> 614 1 0 0 0 0
#> 616 0 0 1 0 0
#> 617 0 0 0 0 0
#> 619 0 1 0 0 1
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#> 621 0 0 0 0 0
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#> 625 0 0 0 0 0
#> 628 0 0 1 0 0
#> 630 1 0 0 1 0
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#> 632 0 0 1 0 0
#> 633 0 0 0 0 0
#> 634 1 0 0 1 0
#> 638 1 0 0 1 0
#> 639 0 1 0 0 1
#> 640 0 0 1 0 0
#> 642 1 0 0 0 0
#> 645 0 0 0 0 0
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#> 652 0 0 1 0 0
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#> 656 0 0 1 0 0
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#> 673 0 0 0 0 0
#> 674 1 0 0 0 0
#> 678 1 0 0 1 0
#> 679 0 1 0 0 1
#> 680 0 0 1 0 0
#> 682 1 0 0 0 0
#> 684 0 0 1 0 0
#> 685 0 0 0 0 0
#> 686 1 0 0 1 0
#> 687 0 1 0 0 1
#> 689 0 0 0 0 0
#> 690 1 0 0 1 0
#> 691 0 1 0 0 1
#> 693 0 0 0 0 0
#> 694 1 0 0 1 0
#> 695 0 1 0 0 1
#> 697 0 0 0 0 0
#> 698 1 0 0 0 0
#> 700 0 0 1 0 0
#> 701 0 0 0 0 0
#> 702 1 0 0 1 0
#> 704 0 0 1 0 0
#> 707 0 1 0 0 0
#> 709 0 0 0 0 0
#> 712 0 0 1 0 0
#> 713 0 0 0 0 0
#> 715 0 1 0 0 1
#> 716 0 0 1 0 0
#> 717 0 0 0 0 0
#> 718 1 0 0 0 0
#> 719 0 1 0 0 0
#> 721 0 0 0 0 0
#> 723 0 1 0 0 0
#> 724 0 0 1 0 0
#> 727 0 1 0 0 1
#> 728 0 0 1 0 0
#> 729 0 0 0 0 0
#> 730 1 0 0 1 0
#> 734 1 0 0 1 0
#> 736 0 0 1 0 0
#> 738 1 0 0 0 0
#> 739 0 1 0 0 0
#> 740 0 0 1 0 0
#> 741 0 0 0 0 0
#> 742 1 0 0 0 0
#> 744 0 0 1 0 0
#> 745 0 0 0 0 0
#> 749 0 0 0 0 0
#> 751 0 1 0 0 0
#> 753 0 0 0 0 0
#> 754 1 0 0 1 0
#> 755 0 1 0 0 1
#> 757 0 0 0 0 0
#> 758 1 0 0 1 0
#> 759 0 1 0 0 1
#> 760 0 0 1 0 0
#> 761 0 0 0 0 0
#> 764 0 0 1 0 0
#> 765 0 0 0 0 0
#> 766 1 0 0 1 0
#> 767 0 1 0 0 1
#> 768 0 0 1 0 0
#> 770 1 0 0 0 0
#> 771 0 1 0 0 0
#> 772 0 0 1 0 0
#> 773 0 0 0 0 0
#> 774 1 0 0 0 0
#> 775 0 1 0 0 0
#> 776 0 0 1 0 0
#> 778 1 0 0 1 0
#> 779 0 1 0 0 1
#> 781 0 0 0 0 0
#> 782 1 0 0 0 0
#> 784 0 0 1 0 0
#> 788 0 0 1 0 0
#> 789 0 0 0 0 0
#> 790 1 0 0 1 0
#> 792 0 0 1 0 0
#> 797 0 0 0 0 0
#> 798 1 0 0 0 0
#> 800 0 0 1 0 0
#> ARMCDTRT:AVISITVIS4
#> 2 0
#> 4 1
#> 6 0
#> 7 0
#> 8 0
#> 10 0
#> 12 0
#> 13 0
#> 14 0
#> 16 1
#> 17 0
#> 19 0
#> 20 0
#> 23 0
#> 25 0
#> 26 0
#> 28 0
#> 29 0
#> 30 0
#> 31 0
#> 32 0
#> 33 0
#> 34 0
#> 36 1
#> 39 0
#> 41 0
#> 42 0
#> 43 0
#> 44 1
#> 45 0
#> 46 0
#> 47 0
#> 51 0
#> 52 1
#> 55 0
#> 59 0
#> 60 0
#> 62 0
#> 64 0
#> 65 0
#> 68 0
#> 69 0
#> 70 0
#> 72 1
#> 73 0
#> 74 0
#> 75 0
#> 76 1
#> 78 0
#> 79 0
#> 82 0
#> 83 0
#> 84 1
#> 85 0
#> 86 0
#> 87 0
#> 88 1
#> 89 0
#> 90 0
#> 91 0
#> 93 0
#> 94 0
#> 95 0
#> 96 1
#> 97 0
#> 98 0
#> 99 0
#> 100 1
#> 101 0
#> 102 0
#> 103 0
#> 104 1
#> 105 0
#> 107 0
#> 108 1
#> 109 0
#> 110 0
#> 111 0
#> 112 1
#> 113 0
#> 114 0
#> 116 1
#> 117 0
#> 118 0
#> 119 0
#> 120 1
#> 121 0
#> 123 0
#> 125 0
#> 128 1
#> 129 0
#> 130 0
#> 132 1
#> 133 0
#> 134 0
#> 135 0
#> 136 0
#> 137 0
#> 138 0
#> 140 1
#> 142 0
#> 144 1
#> 145 0
#> 146 0
#> 147 0
#> 148 0
#> 149 0
#> 151 0
#> 153 0
#> 155 0
#> 156 0
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#> 158 0
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#> 162 0
#> 163 0
#> 164 0
#> 165 0
#> 168 0
#> 169 0
#> 170 0
#> 171 0
#> 172 1
#> 173 0
#> 177 0
#> 178 0
#> 179 0
#> 180 0
#> 181 0
#> 182 0
#> 183 0
#> 185 0
#> 186 0
#> 187 0
#> 190 0
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#> 194 0
#> 195 0
#> 197 0
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#> 199 0
#> 201 0
#> 202 0
#> 204 1
#> 206 0
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#> 210 0
#> 217 0
#> 218 0
#> 219 0
#> 221 0
#> 224 0
#> 226 0
#> 227 0
#> 228 1
#> 230 0
#> 231 0
#> 233 0
#> 235 0
#> 236 1
#> 237 0
#> 238 0
#> 239 0
#> 240 1
#> 241 0
#> 242 0
#> 244 1
#> 246 0
#> 250 0
#> 251 0
#> 252 0
#> 253 0
#> 254 0
#> 256 0
#> 257 0
#> 258 0
#> 259 0
#> 260 1
#> 261 0
#> 262 0
#> 263 0
#> 264 1
#> 265 0
#> 266 0
#> 267 0
#> 268 1
#> 269 0
#> 270 0
#> 273 0
#> 274 0
#> 275 0
#> 276 0
#> 277 0
#> 278 0
#> 280 0
#> 281 0
#> 282 0
#> 283 0
#> 284 1
#> 285 0
#> 286 0
#> 287 0
#> 291 0
#> 292 0
#> 293 0
#> 295 0
#> 296 0
#> 298 0
#> 299 0
#> 300 0
#> 301 0
#> 304 1
#> 305 0
#> 306 0
#> 307 0
#> 308 1
#> 310 0
#> 311 0
#> 312 1
#> 316 0
#> 317 0
#> 318 0
#> 319 0
#> 322 0
#> 323 0
#> 324 0
#> 325 0
#> 327 0
#> 328 0
#> 329 0
#> 330 0
#> 331 0
#> 332 1
#> 336 0
#> 339 0
#> 340 0
#> 341 0
#> 342 0
#> 343 0
#> 344 1
#> 345 0
#> 347 0
#> 349 0
#> 351 0
#> 352 0
#> 353 0
#> 354 0
#> 355 0
#> 356 0
#> 357 0
#> 363 0
#> 364 1
#> 365 0
#> 367 0
#> 368 1
#> 370 0
#> 371 0
#> 372 0
#> 373 0
#> 375 0
#> 376 0
#> 378 0
#> 379 0
#> 381 0
#> 382 0
#> 384 0
#> 385 0
#> 386 0
#> 388 1
#> 389 0
#> 390 0
#> 391 0
#> 392 0
#> 394 0
#> 397 0
#> 398 0
#> 399 0
#> 402 0
#> 403 0
#> 405 0
#> 406 0
#> 407 0
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#> 409 0
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#> 411 0
#> 413 0
#> 415 0
#> 416 0
#> 418 0
#> 419 0
#> 421 0
#> 422 0
#> 423 0
#> 424 1
#> 427 0
#> 428 0
#> 429 0
#> 430 0
#> 431 0
#> 432 1
#> 434 0
#> 435 0
#> 436 0
#> 438 0
#> 439 0
#> 444 0
#> 445 0
#> 447 0
#> 449 0
#> 450 0
#> 451 0
#> 453 0
#> 454 0
#> 455 0
#> 456 0
#> 457 0
#> 458 0
#> 459 0
#> 461 0
#> 463 0
#> 464 1
#> 465 0
#> 469 0
#> 470 0
#> 471 0
#> 473 0
#> 474 0
#> 477 0
#> 484 1
#> 487 0
#> 489 0
#> 490 0
#> 491 0
#> 494 0
#> 495 0
#> 496 1
#> 497 0
#> 498 0
#> 499 0
#> 501 0
#> 502 0
#> 504 1
#> 505 0
#> 508 1
#> 509 0
#> 510 0
#> 511 0
#> 512 1
#> 513 0
#> 518 0
#> 519 0
#> 521 0
#> 522 0
#> 523 0
#> 524 0
#> 526 0
#> 527 0
#> 528 0
#> 530 0
#> 531 0
#> 532 0
#> 534 0
#> 535 0
#> 536 0
#> 537 0
#> 538 0
#> 539 0
#> 540 0
#> 541 0
#> 544 1
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#> 546 0
#> 547 0
#> 548 1
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#> 550 0
#> 551 0
#> 555 0
#> 556 1
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#> 558 0
#> 560 0
#> 562 0
#> 564 1
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#> 570 0
#> 572 1
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#> 574 0
#> 575 0
#> 576 0
#> 577 0
#> 578 0
#> 579 0
#> 582 0
#> 583 0
#> 584 1
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#> 586 0
#> 587 0
#> 590 0
#> 591 0
#> 593 0
#> 594 0
#> 595 0
#> 596 0
#> 599 0
#> 600 0
#> 601 0
#> 602 0
#> 604 0
#> 606 0
#> 608 1
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#> 610 0
#> 611 0
#> 612 0
#> 613 0
#> 614 0
#> 616 0
#> 617 0
#> 619 0
#> 620 1
#> 621 0
#> 622 0
#> 623 0
#> 624 0
#> 625 0
#> 628 1
#> 630 0
#> 631 0
#> 632 1
#> 633 0
#> 634 0
#> 638 0
#> 639 0
#> 640 1
#> 642 0
#> 645 0
#> 648 0
#> 650 0
#> 652 0
#> 654 0
#> 655 0
#> 656 0
#> 657 0
#> 661 0
#> 665 0
#> 666 0
#> 668 0
#> 669 0
#> 670 0
#> 671 0
#> 673 0
#> 674 0
#> 678 0
#> 679 0
#> 680 1
#> 682 0
#> 684 0
#> 685 0
#> 686 0
#> 687 0
#> 689 0
#> 690 0
#> 691 0
#> 693 0
#> 694 0
#> 695 0
#> 697 0
#> 698 0
#> 700 0
#> 701 0
#> 702 0
#> 704 1
#> 707 0
#> 709 0
#> 712 0
#> 713 0
#> 715 0
#> 716 1
#> 717 0
#> 718 0
#> 719 0
#> 721 0
#> 723 0
#> 724 0
#> 727 0
#> 728 1
#> 729 0
#> 730 0
#> 734 0
#> 736 1
#> 738 0
#> 739 0
#> 740 0
#> 741 0
#> 742 0
#> 744 0
#> 745 0
#> 749 0
#> 751 0
#> 753 0
#> 754 0
#> 755 0
#> 757 0
#> 758 0
#> 759 0
#> 760 1
#> 761 0
#> 764 1
#> 765 0
#> 766 0
#> 767 0
#> 768 1
#> 770 0
#> 771 0
#> 772 0
#> 773 0
#> 774 0
#> 775 0
#> 776 0
#> 778 0
#> 779 0
#> 781 0
#> 782 0
#> 784 0
#> 788 0
#> 789 0
#> 790 0
#> 792 1
#> 797 0
#> 798 0
#> 800 0
# terms:
terms(object)
#> FEV1 ~ RACE + SEX + ARMCD + AVISIT + ARMCD:AVISIT
#> attr(,"variables")
#> list(FEV1, RACE, SEX, ARMCD, AVISIT)
#> attr(,"factors")
#> RACE SEX ARMCD AVISIT ARMCD:AVISIT
#> FEV1 0 0 0 0 0
#> RACE 1 0 0 0 0
#> SEX 0 1 0 0 0
#> ARMCD 0 0 1 0 1
#> AVISIT 0 0 0 1 1
#> attr(,"term.labels")
#> [1] "RACE" "SEX" "ARMCD" "AVISIT" "ARMCD:AVISIT"
#> attr(,"order")
#> [1] 1 1 1 1 2
#> attr(,"intercept")
#> [1] 1
#> attr(,"response")
#> [1] 1
#> attr(,".Environment")
#> <environment: 0x55f0d8738120>
terms(object, include = "subject_var")
#> ~RACE + SEX + ARMCD + AVISIT + USUBJID + ARMCD:AVISIT
#> attr(,"variables")
#> list(RACE, SEX, ARMCD, AVISIT, USUBJID)
#> attr(,"factors")
#> RACE SEX ARMCD AVISIT USUBJID ARMCD:AVISIT
#> RACE 1 0 0 0 0 0
#> SEX 0 1 0 0 0 0
#> ARMCD 0 0 1 0 0 1
#> AVISIT 0 0 0 1 0 1
#> USUBJID 0 0 0 0 1 0
#> attr(,"term.labels")
#> [1] "RACE" "SEX" "ARMCD" "AVISIT" "USUBJID"
#> [6] "ARMCD:AVISIT"
#> attr(,"order")
#> [1] 1 1 1 1 1 2
#> attr(,"intercept")
#> [1] 1
#> attr(,"response")
#> [1] 0
#> attr(,".Environment")
#> <environment: 0x55f0d8738120>
# 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: 0x55f0d8738120>
# 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
# residuals:
residuals(object, type = "response")
#> [1] -1.234879652 -31.602602517 -4.161845076 -4.240694906 2.976720902
#> [6] -1.486633993 -10.523496721 -0.985476011 -5.929054895 3.894654785
#> [11] 0.005884300 4.143525922 -5.650335904 -3.625861923 0.826964231
#> [16] -3.033654140 2.122875942 -0.077764040 -1.236764407 2.898887820
#> [21] 5.666894712 6.973709346 1.649933932 2.223736062 1.999981925
#> [26] 9.449780374 -0.697302530 -0.797571139 -4.121957765 9.568283755
#> [31] 7.148615054 -1.008039398 3.474215960 0.203016560 -0.741716375
#> [36] -6.462963308 -6.753458982 -0.047005589 1.515200267 -14.092698990
#> [41] -10.947245143 -1.951225988 -5.706079812 -9.307052363 -2.403398748
#> [46] 7.086671267 -2.850398916 16.073985815 2.634589860 3.803878882
#> [51] 11.652502799 -0.134982175 4.531571586 5.789979573 6.916172244
#> [56] -8.335823810 -2.446122835 -8.765445294 -1.487993507 0.630113879
#> [61] -10.923600038 -9.812114797 5.259684070 -4.094159929 -8.824895907
#> [66] -13.157401791 0.877589312 -0.339592573 8.595725270 3.936442210
#> [71] 1.552353395 13.791819037 -14.336580048 0.027233096 0.336487818
#> [76] 1.612454504 3.714131743 6.358341554 6.469275659 1.146036288
#> [81] -6.481239981 -15.420209264 10.564048465 1.873040490 -0.604472161
#> [86] 11.276535944 -3.846981011 4.205682602 -2.888867642 -10.092066250
#> [91] -8.954675960 -9.131288225 -10.160328309 8.348966008 -3.324891902
#> [96] -6.821197849 -0.252287406 -0.230464002 -1.407973607 -2.003675274
#> [101] 4.967728097 -10.820604863 -2.685460226 -1.400767701 -5.913374081
#> [106] 5.965692909 7.081065238 5.753275316 6.224314102 1.714833437
#> [111] -4.753946846 -2.150649979 -1.304036547 -1.697593065 4.231714405
#> [116] 7.523067828 -2.009615920 6.756933384 -5.242275398 -6.597847500
#> [121] 1.364617396 1.759820544 15.973845801 -0.461493245 2.787801686
#> [126] 0.484952966 -3.870461666 12.311298679 -11.232317044 -4.481649361
#> [131] -0.220037106 -12.363619506 1.709197627 -6.875616999 5.976752937
#> [136] -0.656597193 7.283701781 -2.922519989 6.370716054 -5.058284328
#> [141] -9.236842335 -4.345245889 -9.622488058 -6.654774813 -12.022180587
#> [146] 1.887357480 -6.370718385 4.328882117 3.485715939 5.595034537
#> [151] 9.409222502 2.234484592 -2.968076876 0.569772634 0.236447831
#> [156] 1.328254786 0.485660327 8.823363223 0.276419270 -2.984758150
#> [161] -0.711357559 2.929205179 3.904409284 -1.545435261 4.840564762
#> [166] 4.044772166 -1.721893559 7.189004781 1.582814876 -6.023735519
#> [171] 9.469873359 3.411370689 12.312877955 -0.415604291 -7.826304065
#> [176] -2.588748144 4.878824125 -2.386492192 0.275251132 15.403304358
#> [181] -1.117311743 -5.778945571 -4.284700514 -0.658853762 -1.604989482
#> [186] -8.628885781 -4.503110575 -4.125143859 1.692779577 -0.289986567
#> [191] -1.324600198 7.581755477 -1.962979124 -12.646575009 -5.000450448
#> [196] 0.168281529 -2.531874945 -1.621285472 5.569746459 1.486223742
#> [201] 15.758167838 11.723532098 1.222430146 2.140888226 -2.930455637
#> [206] -7.358901274 1.549014295 4.296486376 4.408791522 2.197562650
#> [211] -8.375741822 4.953239770 -1.144775488 -1.781395739 -3.146498706
#> [216] 6.783928757 1.098312860 -22.160474609 -2.816167884 -3.716081684
#> [221] 8.774205403 -2.178968843 0.782958690 -0.005585283 -2.923802312
#> [226] -1.430045436 -3.266421082 -7.413799193 8.803384810 3.193392733
#> [231] 6.552308299 -3.576957870 -2.900210364 0.851863016 -4.147762312
#> [236] 7.742038570 -1.501447881 10.267382270 5.455583260 -6.641733868
#> [241] -7.367427023 10.801038359 -7.567612905 2.531801030 -3.893743195
#> [246] 0.623788663 9.698229358 3.060268038 5.913536244 -11.374576033
#> [251] 6.770578888 -4.806976192 1.005172447 -7.759025307 2.809952061
#> [256] 5.765703310 -2.046337220 3.454956181 1.797059893 -6.820263102
#> [261] 2.852094314 2.232372715 -3.397204643 6.883657677 -1.853470422
#> [266] 0.858119403 -2.080961909 -5.325272886 8.348411771 -1.049269637
#> [271] -7.961356400 -10.048198957 -7.611069668 -0.927034289 -8.037769563
#> [276] -0.395791510 1.964185716 1.846717422 -2.319131312 -1.960386265
#> [281] 4.813456009 4.453877656 7.759026726 1.075918408 8.735930253
#> [286] 0.931062886 -6.492053277 3.905731110 6.103802886 -1.553315409
#> [291] -0.364087540 6.347166038 3.008394981 -5.651857454 0.406319494
#> [296] 1.155221279 -9.081862494 -4.569513438 17.559986122 2.202330352
#> [301] -1.269170427 -0.367685386 -3.931511724 3.117376232 -2.997817660
#> [306] -15.997444012 -2.144194495 2.087262596 -10.213796246 -0.460389924
#> [311] -2.282396185 1.510231160 6.144987953 -1.051913357 -3.165847388
#> [316] 2.362323843 0.673403098 -0.194314949 9.826663341 6.250933707
#> [321] -0.235076130 2.461097100 -1.767393574 -9.083814120 -9.512139613
#> [326] 0.332052131 2.907510403 6.797230637 4.517753563 6.860770097
#> [331] -0.535968343 8.144395143 8.315853379 10.472748395 -1.241441160
#> [336] 1.208712197 4.720909904 -2.811983812 -3.180543134 5.616929260
#> [341] -3.699626586 -1.645226149 0.073590117 5.932694905 13.347774651
#> [346] -0.599486961 0.586742528 1.009572681 -0.943207940 2.177417986
#> [351] 3.098085788 -3.479836872 0.282540942 4.915937135 -3.702232983
#> [356] -5.607957192 -3.714729352 -17.040753161 -3.528768581 0.541082039
#> [361] 8.076779586 -6.191258080 0.412993243 5.018532666 1.732496518
#> [366] 4.890613044 -3.824823997 -0.487435234 2.480592663 2.305343529
#> [371] -26.246442718 -5.774618924 0.493027338 -12.146505999 -7.174675073
#> [376] -2.154139764 -7.657419221 21.074111704 6.040615383 8.467296562
#> [381] -10.336888517 -4.958491116 -3.742055183 0.987848599 2.215631522
#> [386] -0.851264659 -0.630616202 -9.028257544 -7.573424167 -12.742297395
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#> [316] 0.7525263117 0.2374861472 0.0485866424 1.5430891919 0.5963358156
#> [321] -0.4165254171 0.3864681432 -0.5474483920 -1.0479711477 -1.4936992670
#> [326] 0.0521424248 0.6021738509 1.7554016312 0.7094266338 1.1347868613
#> [331] -0.0841635588 0.8331590995 2.1544925682 1.6445444703 -1.0704845256
#> [336] 0.0422949184 0.9158362154 -0.8652747275 -0.4634570399 0.8820311160
#> [341] -1.2290194130 -0.5469180840 0.0115559178 1.2749988739 1.3196411685
#> [346] -0.0941379407 0.0821894630 0.1585340454 -0.2809634145 0.5605975850
#> [351] 0.3210435388 -0.5464417046 0.0548117275 1.2785517452 -0.5813647525
#> [356] -0.9268450187 -0.8100221758 -1.6228924603 -0.6845659265 0.2383717814
#> [361] 0.9373665455 -1.2010774371 0.2778870068 0.6983516870 0.3360968725
#> [366] 1.2321538333 -0.4502233484 -0.0765423638 0.5728145982 0.5933717589
#> [371] -2.7595058681 -0.9067932563 0.2513154764 -1.9073760316 -0.6179707379
#> [376] -0.1399576256 -0.3481875728 3.3092854483 -0.3109331235 1.5769403726
#> [381] -2.6781075222 -0.4429096259 -0.5876180661 0.4999842463 0.3313270198
#> [386] -0.1651416824 -0.0404624874 -1.4177148596 -0.9429902703 -0.9663561862
#> [391] -0.6466191658 -2.2535063005 0.4563135780 0.1453231304 -0.1182882332
#> [396] 1.6290602024 0.3146280392 -0.3493814754 1.0416358515 0.0136480171
#> [401] 1.7026248170 -0.0356641510 1.0414162785 0.5572875838 0.3550610267
#> [406] 0.2089424426 1.4368057614 0.2836747953 0.6772658681 1.0065016707
#> [411] 1.2001797239 0.5316990211 0.6336526750 -0.0190973637 -0.4963304733
#> [416] -1.4052713714 -0.4877801835 -0.9729185834 0.0800046880 1.9984809435
#> [421] 0.0146878993 -0.0014265194 0.9781840852 0.2784228533 -0.7805456237
#> [426] 0.8431370787 -0.9847933905 -0.8886963475 1.6803385316 1.0750066150
#> [431] -0.0393142744 0.4961341049 0.5191451935 -0.4573708258 0.6990303540
#> [436] -1.0057233670 2.1745715012 1.3823092213 1.6431382695 -0.6682482269
#> [441] 0.3092682477 -0.1228064748 0.9013385029 0.6395657029 1.0544417016
#> [446] -0.3859865656 0.3428001042 -0.0580379891 1.1240987437 -1.9467612774
#> [451] 0.3093001178 -0.2042447739 -0.7712826657 -0.5557474232 0.2159453336
#> [456] 0.1312795882 -1.5114788550 -0.8743767375 -0.8762197629 -0.9952456694
#> [461] -1.1590765928 -1.3025718628 1.1807930021 -0.4665046434 -0.2828278537
#> [466] -0.8862018395 0.2627760037 -0.2246585728 -0.7350270917 2.2330846670
#> [471] -0.1680653380 0.2272482221 -0.4662419908 -0.3983863095 -0.8770483090
#> [476] -0.3627669389 -1.2516292128 0.8971122637 -0.7173723569 -0.1069220469
#> [481] 0.1134832246 0.3757775355 -0.2128705850 -0.3102625447 -0.1662801672
#> [486] -1.0328202284 -0.6530835138 0.8961528576 0.0541645528 -0.9043953545
#> [491] 0.9533184156 -1.0979696605 1.1275981322 -0.7743775123 -0.8219478749
#> [496] 1.4861078517 0.7376918609 0.3628190010 -0.9383643181 0.3175034734
#> [501] 1.3442690156 -1.8287202394 -0.0867610000 1.8935600524 -0.6001433545
#> [506] 0.3359892081 -0.4675070243 1.2196363492 0.6361736837 0.5308643245
#> [511] -0.2883264347 0.9624697278 -1.4780914615 -0.8452842468 -1.2242792315
#> [516] 0.6984536826 -0.1638332534 3.1504729615 -1.0518676907 -0.9852678720
#> [521] 1.7339047855 -1.8750237951 0.4999242945 -1.0247546509 -1.4427773430
#> [526] 0.7390501284 -0.8521673979 -1.8048482025 -0.3303034285 0.7739312929
#> [531] -0.7394480434 0.9835059653 1.9907888113 1.4970706001 0.5331882492
#> [536] 0.7105372605 0.5615316312