Nothing
tests/testthat/_snaps/survreg.md
In JointAI: Joint Analysis and Imputation of Incomplete Data
data_list remains the same
Code
lapply(models, "[[", "data_list")
Output
$m0a
$m0a$M_lvlone
futime status != "censored" (Intercept)
1 400 1 1
3 5169 0 1
12 1012 1 1
16 1925 1 1
23 1505 1 1
29 2503 1 1
35 2501 0 1
42 2466 1 1
50 2400 1 1
57 51 1 1
58 3762 1 1
70 304 1 1
72 4247 0 1
84 1217 1 1
91 3584 1 1
102 4345 0 1
115 769 1 1
118 132 1 1
119 4901 0 1
134 1356 1 1
138 3657 1 1
150 673 1 1
153 264 1 1
155 4079 1 1
168 4796 0 1
180 1444 1 1
186 77 1 1
187 549 1 1
190 5074 1 1
200 321 1 1
203 3839 1 1
215 5192 0 1
231 3170 1 1
241 4602 0 1
255 2847 1 1
259 4281 0 1
270 223 1 1
272 3244 1 1
282 2297 1 1
290 5136 0 1
305 1350 1 1
309 5122 0 1
325 5225 0 1
340 3428 1 1
351 4694 0 1
360 2256 1 1
368 3245 0 1
375 5096 0 1
384 708 1 1
388 2598 1 1
397 3853 1 1
407 2386 1 1
416 1000 1 1
419 1434 1 1
424 1360 1 1
430 1847 1 1
436 3282 1 1
447 5128 0 1
463 2224 1 1
470 5034 0 1
483 4925 0 1
497 3090 1 1
507 859 1 1
510 1487 1 1
516 4842 0 1
522 4191 1 1
535 2769 1 1
545 4708 0 1
559 1170 1 1
563 3683 1 1
576 4865 0 1
587 4853 0 1
593 4859 0 1
608 1827 1 1
612 1191 1 1
617 71 1 1
618 326 1 1
620 1690 1 1
624 4376 0 1
635 890 1 1
639 2540 1 1
649 3574 1 1
659 4719 0 1
674 4701 0 1
677 3358 1 1
688 1657 1 1
689 198 1 1
691 3076 1 1
695 1741 1 1
699 2689 1 1
708 460 1 1
711 389 1 1
712 4583 0 1
727 750 1 1
730 137 1 1
731 4520 0 1
745 620 1 1
749 4492 0 1
763 4489 0 1
776 552 1 1
780 4250 0 1
792 3770 0 1
804 110 1 1
805 3086 1 1
815 3092 1 1
825 3222 1 1
830 4058 0 1
841 2583 1 1
849 3173 0 1
859 2044 1 1
866 2350 1 1
874 3445 1 1
885 951 1 1
890 3395 1 1
901 4091 0 1
913 4015 0 1
924 1083 1 1
929 2288 1 1
936 515 1 1
938 2033 1 1
946 191 1 1
947 3966 0 1
957 971 1 1
959 3903 1 1
960 2468 1 1
969 824 1 1
974 3924 0 1
985 1037 1 1
990 3908 0 1
1002 1411 1 1
1008 850 1 1
1012 3613 0 1
1018 2796 1 1
1027 3818 0 1
1039 3819 0 1
1049 3767 0 1
1058 3659 0 1
1070 1297 1 1
1076 2357 1 1
1082 3728 0 1
1094 3719 0 1
1097 2419 1 1
1106 786 1 1
1109 945 1 1
1113 3645 0 1
1117 3086 1 1
1124 3382 1 1
1129 1427 1 1
1135 762 1 1
1138 3560 0 1
1147 3539 0 1
1158 1152 1 1
1161 3532 0 1
1171 140 1 1
1172 3516 0 1
1175 853 1 1
1179 3504 0 1
1190 2475 1 1
1199 1536 1 1
1203 3441 0 1
1211 3466 0 1
1222 186 1 1
1223 2055 1 1
1226 276 1 1
1227 1076 1 1
1232 3390 0 1
1240 1684 1 1
1246 3384 0 1
1256 1212 1 1
1261 3361 0 1
1262 3243 0 1
1272 2970 0 1
1279 3326 0 1
1288 3313 0 1
1298 3293 0 1
1308 1492 1 1
1314 3278 0 1
1315 3249 0 1
1321 3242 0 1
1324 3232 0 1
1334 3225 0 1
1335 3224 0 1
1340 2241 1 1
1348 974 1 1
1353 2882 1 1
1358 1576 1 1
1362 733 1 1
1366 2635 0 1
1374 3125 0 1
1379 3173 0 1
1383 216 1 1
1384 3112 0 1
1394 797 1 1
1398 3118 0 1
1403 2999 0 1
1404 2555 1 1
1412 3034 0 1
1421 3025 0 1
1429 2991 0 1
1432 2932 1 1
1443 2963 0 1
1453 2941 0 1
1455 2890 0 1
1464 2090 1 1
1472 2081 1 1
1477 2924 0 1
1485 2840 0 1
1492 904 1 1
1495 2888 0 1
1497 2893 0 1
1507 2865 0 1
1515 2845 0 1
1521 2189 0 1
1523 1786 1 1
1527 1080 1 1
1531 2336 0 1
1538 790 1 1
1542 2839 0 1
1552 2826 0 1
1558 1235 1 1
1563 2719 0 1
1571 597 1 1
1575 334 1 1
1576 2614 0 1
1584 2691 0 1
1593 2647 0 1
1602 999 1 1
1606 2636 0 1
1610 348 1 1
1613 2648 0 1
1617 1165 1 1
1621 2620 0 1
1625 2601 0 1
1626 2445 0 1
1628 2302 1 1
1630 2577 0 1
1632 1947 1 1
1634 1874 1 1
1637 694 1 1
1640 2500 0 1
1648 837 1 1
1652 2128 1 1
1660 930 1 1
1662 1690 1 1
1666 2459 0 1
1668 1435 1 1
1674 940 1 1
1678 2454 0 1
1686 2452 0 1
1689 2327 1 1
1691 2307 0 1
1692 2439 0 1
1695 2434 0 1
1703 737 1 1
1707 2405 0 1
1713 2370 0 1
1719 2283 0 1
1722 2371 0 1
1730 2284 0 1
1735 1674 1 1
1736 2348 0 1
1742 1850 1 1
1745 1303 1 1
1750 1542 1 1
1754 1084 1 1
1758 2287 0 1
1765 179 1 1
1766 1191 1 1
1768 1898 1 1
1775 2010 1 1
1778 2238 0 1
1786 2194 0 1
1792 1649 1 1
1795 1447 1 1
1800 2020 0 1
1804 2147 0 1
1806 2105 0 1
1811 1996 1 1
1816 2102 0 1
1823 2081 0 1
1830 41 1 1
1831 1673 1 1
1835 2095 0 1
1839 2087 0 1
1846 2070 0 1
1847 2077 0 1
1849 1552 0 1
1853 1067 1 1
1855 799 1 1
1859 2032 0 1
1866 901 1 1
1871 1785 1 1
1874 1989 0 1
1876 1971 0 1
1883 875 1 1
1885 1990 0 1
1888 533 1 1
1891 1969 0 1
1894 1962 0 1
1895 207 1 1
1897 1969 0 1
1900 1940 0 1
1905 1597 1 1
1909 1899 0 1
1910 1885 0 1
1915 1885 0 1
1917 1818 0 1
1922 1822 0 1
1927 1663 0 1
1932 1608 0 1
1937 1508 0 1
1941 1457 0 1
$m0a$mu_reg_surv
[1] 0
$m0a$tau_reg_surv
[1] 0.001
$m0a$cens_Srv_ftm_stts_cn
1 3 12 16 23 29 35 42 50 57 58 70 72 84 91 102
0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1
115 118 119 134 138 150 153 155 168 180 186 187 190 200 203 215
0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1
231 241 255 259 270 272 282 290 305 309 325 340 351 360 368 375
0 1 0 1 0 0 0 1 0 1 1 0 1 0 1 1
384 388 397 407 416 419 424 430 436 447 463 470 483 497 507 510
0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0
516 522 535 545 559 563 576 587 593 608 612 617 618 620 624 635
1 0 0 1 0 0 1 1 1 0 0 0 0 0 1 0
639 649 659 674 677 688 689 691 695 699 708 711 712 727 730 731
0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1
745 749 763 776 780 792 804 805 815 825 830 841 849 859 866 874
0 1 1 0 1 1 0 0 0 0 1 0 1 0 0 0
885 890 901 913 924 929 936 938 946 947 957 959 960 969 974 985
0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0
990 1002 1008 1012 1018 1027 1039 1049 1058 1070 1076 1082 1094 1097 1106 1109
1 0 0 1 0 1 1 1 1 0 0 1 1 0 0 0
1113 1117 1124 1129 1135 1138 1147 1158 1161 1171 1172 1175 1179 1190 1199 1203
1 0 0 0 0 1 1 0 1 0 1 0 1 0 0 1
1211 1222 1223 1226 1227 1232 1240 1246 1256 1261 1262 1272 1279 1288 1298 1308
1 0 0 0 0 1 0 1 0 1 1 1 1 1 1 0
1314 1315 1321 1324 1334 1335 1340 1348 1353 1358 1362 1366 1374 1379 1383 1384
1 1 1 1 1 1 0 0 0 0 0 1 1 1 0 1
1394 1398 1403 1404 1412 1421 1429 1432 1443 1453 1455 1464 1472 1477 1485 1492
0 1 1 0 1 1 1 0 1 1 1 0 0 1 1 0
1495 1497 1507 1515 1521 1523 1527 1531 1538 1542 1552 1558 1563 1571 1575 1576
1 1 1 1 1 0 0 1 0 1 1 0 1 0 0 1
1584 1593 1602 1606 1610 1613 1617 1621 1625 1626 1628 1630 1632 1634 1637 1640
1 1 0 1 0 1 0 1 1 1 0 1 0 0 0 1
1648 1652 1660 1662 1666 1668 1674 1678 1686 1689 1691 1692 1695 1703 1707 1713
0 0 0 0 1 0 0 1 1 0 1 1 1 0 1 1
1719 1722 1730 1735 1736 1742 1745 1750 1754 1758 1765 1766 1768 1775 1778 1786
1 1 1 0 1 0 0 0 0 1 0 0 0 0 1 1
1792 1795 1800 1804 1806 1811 1816 1823 1830 1831 1835 1839 1846 1847 1849 1853
0 0 1 1 1 0 1 1 0 0 1 1 1 1 1 0
1855 1859 1866 1871 1874 1876 1883 1885 1888 1891 1894 1895 1897 1900 1905 1909
0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 1
1910 1915 1917 1922 1927 1932 1937 1941
1 1 1 1 1 1 1 1
$m0a$Srv_ftm_stts_cn
[1] 400 NA 1012 1925 1505 2503 NA 2466 2400 51 3762 304 NA 1217 3584
[16] NA 769 132 NA 1356 3657 673 264 4079 NA 1444 77 549 5074 321
[31] 3839 NA 3170 NA 2847 NA 223 3244 2297 NA 1350 NA NA 3428 NA
[46] 2256 NA NA 708 2598 3853 2386 1000 1434 1360 1847 3282 NA 2224 NA
[61] NA 3090 859 1487 NA 4191 2769 NA 1170 3683 NA NA NA 1827 1191
[76] 71 326 1690 NA 890 2540 3574 NA NA 3358 1657 198 3076 1741 2689
[91] 460 389 NA 750 137 NA 620 NA NA 552 NA NA 110 3086 3092
[106] 3222 NA 2583 NA 2044 2350 3445 951 3395 NA NA 1083 2288 515 2033
[121] 191 NA 971 3903 2468 824 NA 1037 NA 1411 850 NA 2796 NA NA
[136] NA NA 1297 2357 NA NA 2419 786 945 NA 3086 3382 1427 762 NA
[151] NA 1152 NA 140 NA 853 NA 2475 1536 NA NA 186 2055 276 1076
[166] NA 1684 NA 1212 NA NA NA NA NA NA 1492 NA NA NA NA
[181] NA NA 2241 974 2882 1576 733 NA NA NA 216 NA 797 NA NA
[196] 2555 NA NA NA 2932 NA NA NA 2090 2081 NA NA 904 NA NA
[211] NA NA NA 1786 1080 NA 790 NA NA 1235 NA 597 334 NA NA
[226] NA 999 NA 348 NA 1165 NA NA NA 2302 NA 1947 1874 694 NA
[241] 837 2128 930 1690 NA 1435 940 NA NA 2327 NA NA NA 737 NA
[256] NA NA NA NA 1674 NA 1850 1303 1542 1084 NA 179 1191 1898 2010
[271] NA NA 1649 1447 NA NA NA 1996 NA NA 41 1673 NA NA NA
[286] NA NA 1067 799 NA 901 1785 NA NA 875 NA 533 NA NA 207
[301] NA NA 1597 NA NA NA NA NA NA NA NA NA
$m1a
$m1a$M_lvlone
futime status != "censored" (Intercept) age sexfemale
1 400 1 1 58.76523 1
3 5169 0 1 56.44627 1
12 1012 1 1 70.07255 0
16 1925 1 1 54.74059 1
23 1505 1 1 38.10541 1
29 2503 1 1 66.25873 1
35 2501 0 1 55.53457 1
42 2466 1 1 53.05681 1
50 2400 1 1 42.50787 1
57 51 1 1 70.55989 1
58 3762 1 1 53.71389 1
70 304 1 1 59.13758 1
72 4247 0 1 45.68925 1
84 1217 1 1 56.22177 0
91 3584 1 1 64.64613 1
102 4345 0 1 40.44353 1
115 769 1 1 52.18344 1
118 132 1 1 53.93018 1
119 4901 0 1 49.56057 1
134 1356 1 1 59.95346 1
138 3657 1 1 64.18891 0
150 673 1 1 56.27652 1
153 264 1 1 55.96715 1
155 4079 1 1 44.52019 0
168 4796 0 1 45.07324 1
180 1444 1 1 52.02464 1
186 77 1 1 54.43943 1
187 549 1 1 44.94730 1
190 5074 1 1 63.87680 1
200 321 1 1 41.38535 1
203 3839 1 1 41.55236 1
215 5192 0 1 53.99589 1
231 3170 1 1 51.28268 1
241 4602 0 1 52.06023 1
255 2847 1 1 48.61875 1
259 4281 0 1 56.41068 1
270 223 1 1 61.72758 1
272 3244 1 1 36.62697 1
282 2297 1 1 55.39220 1
290 5136 0 1 46.66940 1
305 1350 1 1 33.63450 1
309 5122 0 1 33.69473 1
325 5225 0 1 48.87064 1
340 3428 1 1 37.58248 1
351 4694 0 1 41.79329 1
360 2256 1 1 45.79877 1
368 3245 0 1 47.42779 1
375 5096 0 1 49.13621 0
384 708 1 1 61.15264 1
388 2598 1 1 53.50856 1
397 3853 1 1 52.08761 1
407 2386 1 1 50.54073 0
416 1000 1 1 67.40862 1
419 1434 1 1 39.19781 1
424 1360 1 1 65.76318 0
430 1847 1 1 33.61807 1
436 3282 1 1 53.57153 1
447 5128 0 1 44.56947 0
463 2224 1 1 40.39425 1
470 5034 0 1 58.38193 1
483 4925 0 1 43.89870 0
497 3090 1 1 60.70637 1
507 859 1 1 46.62834 1
510 1487 1 1 62.90760 1
516 4842 0 1 40.20260 1
522 4191 1 1 46.45311 0
535 2769 1 1 51.28816 1
545 4708 0 1 32.61328 1
559 1170 1 1 49.33881 1
563 3683 1 1 56.39973 1
576 4865 0 1 48.84600 1
587 4853 0 1 32.49281 1
593 4859 0 1 38.49418 1
608 1827 1 1 51.92060 1
612 1191 1 1 43.51814 1
617 71 1 1 51.94251 1
618 326 1 1 49.82615 1
620 1690 1 1 47.94524 1
624 4376 0 1 46.51608 1
635 890 1 1 67.41136 0
639 2540 1 1 63.26352 1
649 3574 1 1 67.31006 1
659 4719 0 1 56.01369 1
674 4701 0 1 55.83025 1
677 3358 1 1 47.21697 1
688 1657 1 1 52.75838 1
689 198 1 1 37.27858 1
691 3076 1 1 41.39357 1
695 1741 1 1 52.44353 1
699 2689 1 1 33.47570 0
708 460 1 1 45.60712 1
711 389 1 1 76.70910 1
712 4583 0 1 36.53388 1
727 750 1 1 53.91650 1
730 137 1 1 46.39014 1
731 4520 0 1 48.84600 1
745 620 1 1 71.89322 0
749 4492 0 1 28.88433 1
763 4489 0 1 48.46817 0
776 552 1 1 51.46886 0
780 4250 0 1 44.95003 1
792 3770 0 1 56.56947 1
804 110 1 1 48.96372 1
805 3086 1 1 43.01711 1
815 3092 1 1 34.03970 1
825 3222 1 1 68.50924 1
830 4058 0 1 62.52156 1
841 2583 1 1 50.35729 1
849 3173 0 1 44.06297 1
859 2044 1 1 38.91034 1
866 2350 1 1 41.15264 1
874 3445 1 1 55.45791 1
885 951 1 1 51.23340 1
890 3395 1 1 52.82683 0
901 4091 0 1 42.63929 1
913 4015 0 1 61.07050 1
924 1083 1 1 49.65640 1
929 2288 1 1 48.85421 1
936 515 1 1 54.25599 1
938 2033 1 1 35.15127 0
946 191 1 1 67.90691 0
947 3966 0 1 55.43600 1
957 971 1 1 45.82067 1
959 3903 1 1 52.88980 0
960 2468 1 1 47.18138 1
969 824 1 1 53.59890 1
974 3924 0 1 44.10404 1
985 1037 1 1 41.94935 1
990 3908 0 1 63.61396 1
1002 1411 1 1 44.22724 1
1008 850 1 1 62.00137 1
1012 3613 0 1 40.55305 1
1018 2796 1 1 62.64476 0
1027 3818 0 1 42.33539 1
1039 3819 0 1 42.96783 1
1049 3767 0 1 55.96167 1
1058 3659 0 1 62.86105 1
1070 1297 1 1 51.24983 0
1076 2357 1 1 46.76249 1
1082 3728 0 1 54.07529 1
1094 3719 0 1 47.03628 1
1097 2419 1 1 55.72621 1
1106 786 1 1 46.10267 1
1109 945 1 1 52.28747 1
1113 3645 0 1 51.20055 1
1117 3086 1 1 33.86448 1
1124 3382 1 1 75.01164 1
1129 1427 1 1 30.86379 1
1135 762 1 1 61.80424 0
1138 3560 0 1 34.98700 1
1147 3539 0 1 55.04175 1
1158 1152 1 1 69.94114 0
1161 3532 0 1 49.60438 1
1171 140 1 1 69.37714 0
1172 3516 0 1 43.55647 1
1175 853 1 1 59.40862 1
1179 3504 0 1 48.75838 1
1190 2475 1 1 36.49281 1
1199 1536 1 1 45.76044 0
1203 3441 0 1 57.37166 1
1211 3466 0 1 42.74333 1
1222 186 1 1 58.81725 1
1223 2055 1 1 53.49760 1
1226 276 1 1 43.41410 1
1227 1076 1 1 53.30595 0
1232 3390 0 1 41.35524 1
1240 1684 1 1 60.95825 0
1246 3384 0 1 47.75359 1
1256 1212 1 1 35.49076 1
1261 3361 0 1 48.66256 1
1262 3243 0 1 52.66804 1
1272 2970 0 1 49.86995 1
1279 3326 0 1 30.27515 1
1288 3313 0 1 55.56742 1
1298 3293 0 1 52.15332 1
1308 1492 1 1 41.60986 1
1314 3278 0 1 55.45243 1
1315 3249 0 1 70.00411 1
1321 3242 0 1 43.94251 1
1324 3232 0 1 42.56810 1
1334 3225 0 1 44.56947 1
1335 3224 0 1 56.94456 1
1340 2241 1 1 40.26010 1
1348 974 1 1 37.60712 1
1353 2882 1 1 48.36140 1
1358 1576 1 1 70.83641 1
1362 733 1 1 35.79192 1
1366 2635 0 1 62.62286 1
1374 3125 0 1 50.64750 1
1379 3173 0 1 54.52704 1
1383 216 1 1 52.69268 1
1384 3112 0 1 52.72005 1
1394 797 1 1 56.77207 1
1398 3118 0 1 44.39699 1
1403 2999 0 1 29.55510 1
1404 2555 1 1 57.04038 1
1412 3034 0 1 44.62697 1
1421 3025 0 1 35.79740 1
1429 2991 0 1 40.71732 1
1432 2932 1 1 32.23272 1
1443 2963 0 1 41.09240 1
1453 2941 0 1 61.63997 1
1455 2890 0 1 37.05681 1
1464 2090 1 1 62.57906 1
1472 2081 1 1 48.97741 1
1477 2924 0 1 61.99042 1
1485 2840 0 1 72.77207 1
1492 904 1 1 61.29500 1
1495 2888 0 1 52.62423 1
1497 2893 0 1 49.76318 0
1507 2865 0 1 52.91444 1
1515 2845 0 1 47.26352 1
1521 2189 0 1 50.20397 1
1523 1786 1 1 69.34702 1
1527 1080 1 1 41.16906 1
1531 2336 0 1 59.16496 1
1538 790 1 1 36.07940 1
1542 2839 0 1 34.59548 1
1552 2826 0 1 42.71321 1
1558 1235 1 1 63.63039 1
1563 2719 0 1 56.62971 1
1571 597 1 1 46.26420 1
1575 334 1 1 61.24298 1
1576 2614 0 1 38.62012 1
1584 2691 0 1 38.77070 1
1593 2647 0 1 56.69541 1
1602 999 1 1 58.95140 0
1606 2636 0 1 36.92266 1
1610 348 1 1 62.41478 1
1613 2648 0 1 34.60917 1
1617 1165 1 1 58.33539 1
1621 2620 0 1 50.18207 1
1625 2601 0 1 42.68583 1
1626 2445 0 1 34.37919 1
1628 2302 1 1 33.18275 1
1630 2577 0 1 38.38193 1
1632 1947 1 1 59.76181 1
1634 1874 1 1 66.41205 1
1637 694 1 1 46.78987 1
1640 2500 0 1 56.07940 1
1648 837 1 1 41.37440 1
1652 2128 1 1 64.57221 1
1660 930 1 1 67.48802 1
1662 1690 1 1 44.82957 1
1666 2459 0 1 45.77139 1
1668 1435 1 1 32.95003 1
1674 940 1 1 41.22108 1
1678 2454 0 1 55.41684 1
1686 2452 0 1 47.98084 1
1689 2327 1 1 40.79124 1
1691 2307 0 1 56.97467 1
1692 2439 0 1 68.46270 1
1695 2434 0 1 78.43943 0
1703 737 1 1 39.85763 1
1707 2405 0 1 35.31006 1
1713 2370 0 1 31.44422 1
1719 2283 0 1 58.26420 1
1722 2371 0 1 51.48802 1
1730 2284 0 1 59.96988 1
1735 1674 1 1 74.52430 0
1736 2348 0 1 52.36413 1
1742 1850 1 1 42.78713 1
1745 1303 1 1 34.87474 1
1750 1542 1 1 44.13963 1
1754 1084 1 1 46.38193 1
1758 2287 0 1 56.30938 1
1765 179 1 1 70.90760 1
1766 1191 1 1 55.39493 1
1768 1898 1 1 45.08419 1
1775 2010 1 1 26.27789 1
1778 2238 0 1 50.47228 1
1786 2194 0 1 38.39836 1
1792 1649 1 1 47.41958 1
1795 1447 1 1 47.98084 1
1800 2020 0 1 38.31622 1
1804 2147 0 1 50.10815 1
1806 2105 0 1 35.08830 1
1811 1996 1 1 32.50376 1
1816 2102 0 1 56.15332 1
1823 2081 0 1 46.15469 1
1830 41 1 1 65.88364 1
1831 1673 1 1 33.94387 1
1835 2095 0 1 62.86105 1
1839 2087 0 1 48.56400 1
1846 2070 0 1 46.34908 1
1847 2077 0 1 38.85284 1
1849 1552 0 1 58.64750 1
1853 1067 1 1 48.93634 1
1855 799 1 1 67.57290 0
1859 2032 0 1 65.98494 1
1866 901 1 1 40.90075 1
1871 1785 1 1 50.24504 0
1874 1989 0 1 57.19644 1
1876 1971 0 1 60.53662 0
1883 875 1 1 35.35113 0
1885 1990 0 1 31.38125 1
1888 533 1 1 55.98631 0
1891 1969 0 1 52.72553 1
1894 1962 0 1 38.09172 1
1895 207 1 1 58.17112 1
1897 1969 0 1 45.21013 1
1900 1940 0 1 37.79877 1
1905 1597 1 1 60.65982 1
1909 1899 0 1 35.53457 1
1910 1885 0 1 43.06639 1
1915 1885 0 1 56.39151 1
1917 1818 0 1 30.57358 1
1922 1822 0 1 61.18275 1
1927 1663 0 1 58.29979 1
1932 1608 0 1 62.33265 1
1937 1508 0 1 37.99863 1
1941 1457 0 1 33.15264 1
$m1a$spM_lvlone
center scale
futime 2341.64103 1301.27242
status != "censored" NA NA
(Intercept) NA NA
age 50.01901 10.58126
sexfemale NA NA
$m1a$mu_reg_surv
[1] 0
$m1a$tau_reg_surv
[1] 0.001
$m1a$cens_Srv_ftm_stts_cn
1 3 12 16 23 29 35 42 50 57 58 70 72 84 91 102
0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1
115 118 119 134 138 150 153 155 168 180 186 187 190 200 203 215
0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1
231 241 255 259 270 272 282 290 305 309 325 340 351 360 368 375
0 1 0 1 0 0 0 1 0 1 1 0 1 0 1 1
384 388 397 407 416 419 424 430 436 447 463 470 483 497 507 510
0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0
516 522 535 545 559 563 576 587 593 608 612 617 618 620 624 635
1 0 0 1 0 0 1 1 1 0 0 0 0 0 1 0
639 649 659 674 677 688 689 691 695 699 708 711 712 727 730 731
0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1
745 749 763 776 780 792 804 805 815 825 830 841 849 859 866 874
0 1 1 0 1 1 0 0 0 0 1 0 1 0 0 0
885 890 901 913 924 929 936 938 946 947 957 959 960 969 974 985
0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0
990 1002 1008 1012 1018 1027 1039 1049 1058 1070 1076 1082 1094 1097 1106 1109
1 0 0 1 0 1 1 1 1 0 0 1 1 0 0 0
1113 1117 1124 1129 1135 1138 1147 1158 1161 1171 1172 1175 1179 1190 1199 1203
1 0 0 0 0 1 1 0 1 0 1 0 1 0 0 1
1211 1222 1223 1226 1227 1232 1240 1246 1256 1261 1262 1272 1279 1288 1298 1308
1 0 0 0 0 1 0 1 0 1 1 1 1 1 1 0
1314 1315 1321 1324 1334 1335 1340 1348 1353 1358 1362 1366 1374 1379 1383 1384
1 1 1 1 1 1 0 0 0 0 0 1 1 1 0 1
1394 1398 1403 1404 1412 1421 1429 1432 1443 1453 1455 1464 1472 1477 1485 1492
0 1 1 0 1 1 1 0 1 1 1 0 0 1 1 0
1495 1497 1507 1515 1521 1523 1527 1531 1538 1542 1552 1558 1563 1571 1575 1576
1 1 1 1 1 0 0 1 0 1 1 0 1 0 0 1
1584 1593 1602 1606 1610 1613 1617 1621 1625 1626 1628 1630 1632 1634 1637 1640
1 1 0 1 0 1 0 1 1 1 0 1 0 0 0 1
1648 1652 1660 1662 1666 1668 1674 1678 1686 1689 1691 1692 1695 1703 1707 1713
0 0 0 0 1 0 0 1 1 0 1 1 1 0 1 1
1719 1722 1730 1735 1736 1742 1745 1750 1754 1758 1765 1766 1768 1775 1778 1786
1 1 1 0 1 0 0 0 0 1 0 0 0 0 1 1
1792 1795 1800 1804 1806 1811 1816 1823 1830 1831 1835 1839 1846 1847 1849 1853
0 0 1 1 1 0 1 1 0 0 1 1 1 1 1 0
1855 1859 1866 1871 1874 1876 1883 1885 1888 1891 1894 1895 1897 1900 1905 1909
0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 1
1910 1915 1917 1922 1927 1932 1937 1941
1 1 1 1 1 1 1 1
$m1a$Srv_ftm_stts_cn
[1] 400 NA 1012 1925 1505 2503 NA 2466 2400 51 3762 304 NA 1217 3584
[16] NA 769 132 NA 1356 3657 673 264 4079 NA 1444 77 549 5074 321
[31] 3839 NA 3170 NA 2847 NA 223 3244 2297 NA 1350 NA NA 3428 NA
[46] 2256 NA NA 708 2598 3853 2386 1000 1434 1360 1847 3282 NA 2224 NA
[61] NA 3090 859 1487 NA 4191 2769 NA 1170 3683 NA NA NA 1827 1191
[76] 71 326 1690 NA 890 2540 3574 NA NA 3358 1657 198 3076 1741 2689
[91] 460 389 NA 750 137 NA 620 NA NA 552 NA NA 110 3086 3092
[106] 3222 NA 2583 NA 2044 2350 3445 951 3395 NA NA 1083 2288 515 2033
[121] 191 NA 971 3903 2468 824 NA 1037 NA 1411 850 NA 2796 NA NA
[136] NA NA 1297 2357 NA NA 2419 786 945 NA 3086 3382 1427 762 NA
[151] NA 1152 NA 140 NA 853 NA 2475 1536 NA NA 186 2055 276 1076
[166] NA 1684 NA 1212 NA NA NA NA NA NA 1492 NA NA NA NA
[181] NA NA 2241 974 2882 1576 733 NA NA NA 216 NA 797 NA NA
[196] 2555 NA NA NA 2932 NA NA NA 2090 2081 NA NA 904 NA NA
[211] NA NA NA 1786 1080 NA 790 NA NA 1235 NA 597 334 NA NA
[226] NA 999 NA 348 NA 1165 NA NA NA 2302 NA 1947 1874 694 NA
[241] 837 2128 930 1690 NA 1435 940 NA NA 2327 NA NA NA 737 NA
[256] NA NA NA NA 1674 NA 1850 1303 1542 1084 NA 179 1191 1898 2010
[271] NA NA 1649 1447 NA NA NA 1996 NA NA 41 1673 NA NA NA
[286] NA NA 1067 799 NA 901 1785 NA NA 875 NA 533 NA NA 207
[301] NA NA 1597 NA NA NA NA NA NA NA NA NA
$m1b
$m1b$M_lvlone
futime I(status != "censored") (Intercept) age sexfemale
1 400 1 1 58.76523 1
3 5169 0 1 56.44627 1
12 1012 1 1 70.07255 0
16 1925 1 1 54.74059 1
23 1505 1 1 38.10541 1
29 2503 1 1 66.25873 1
35 2501 0 1 55.53457 1
42 2466 1 1 53.05681 1
50 2400 1 1 42.50787 1
57 51 1 1 70.55989 1
58 3762 1 1 53.71389 1
70 304 1 1 59.13758 1
72 4247 0 1 45.68925 1
84 1217 1 1 56.22177 0
91 3584 1 1 64.64613 1
102 4345 0 1 40.44353 1
115 769 1 1 52.18344 1
118 132 1 1 53.93018 1
119 4901 0 1 49.56057 1
134 1356 1 1 59.95346 1
138 3657 1 1 64.18891 0
150 673 1 1 56.27652 1
153 264 1 1 55.96715 1
155 4079 1 1 44.52019 0
168 4796 0 1 45.07324 1
180 1444 1 1 52.02464 1
186 77 1 1 54.43943 1
187 549 1 1 44.94730 1
190 5074 1 1 63.87680 1
200 321 1 1 41.38535 1
203 3839 1 1 41.55236 1
215 5192 0 1 53.99589 1
231 3170 1 1 51.28268 1
241 4602 0 1 52.06023 1
255 2847 1 1 48.61875 1
259 4281 0 1 56.41068 1
270 223 1 1 61.72758 1
272 3244 1 1 36.62697 1
282 2297 1 1 55.39220 1
290 5136 0 1 46.66940 1
305 1350 1 1 33.63450 1
309 5122 0 1 33.69473 1
325 5225 0 1 48.87064 1
340 3428 1 1 37.58248 1
351 4694 0 1 41.79329 1
360 2256 1 1 45.79877 1
368 3245 0 1 47.42779 1
375 5096 0 1 49.13621 0
384 708 1 1 61.15264 1
388 2598 1 1 53.50856 1
397 3853 1 1 52.08761 1
407 2386 1 1 50.54073 0
416 1000 1 1 67.40862 1
419 1434 1 1 39.19781 1
424 1360 1 1 65.76318 0
430 1847 1 1 33.61807 1
436 3282 1 1 53.57153 1
447 5128 0 1 44.56947 0
463 2224 1 1 40.39425 1
470 5034 0 1 58.38193 1
483 4925 0 1 43.89870 0
497 3090 1 1 60.70637 1
507 859 1 1 46.62834 1
510 1487 1 1 62.90760 1
516 4842 0 1 40.20260 1
522 4191 1 1 46.45311 0
535 2769 1 1 51.28816 1
545 4708 0 1 32.61328 1
559 1170 1 1 49.33881 1
563 3683 1 1 56.39973 1
576 4865 0 1 48.84600 1
587 4853 0 1 32.49281 1
593 4859 0 1 38.49418 1
608 1827 1 1 51.92060 1
612 1191 1 1 43.51814 1
617 71 1 1 51.94251 1
618 326 1 1 49.82615 1
620 1690 1 1 47.94524 1
624 4376 0 1 46.51608 1
635 890 1 1 67.41136 0
639 2540 1 1 63.26352 1
649 3574 1 1 67.31006 1
659 4719 0 1 56.01369 1
674 4701 0 1 55.83025 1
677 3358 1 1 47.21697 1
688 1657 1 1 52.75838 1
689 198 1 1 37.27858 1
691 3076 1 1 41.39357 1
695 1741 1 1 52.44353 1
699 2689 1 1 33.47570 0
708 460 1 1 45.60712 1
711 389 1 1 76.70910 1
712 4583 0 1 36.53388 1
727 750 1 1 53.91650 1
730 137 1 1 46.39014 1
731 4520 0 1 48.84600 1
745 620 1 1 71.89322 0
749 4492 0 1 28.88433 1
763 4489 0 1 48.46817 0
776 552 1 1 51.46886 0
780 4250 0 1 44.95003 1
792 3770 0 1 56.56947 1
804 110 1 1 48.96372 1
805 3086 1 1 43.01711 1
815 3092 1 1 34.03970 1
825 3222 1 1 68.50924 1
830 4058 0 1 62.52156 1
841 2583 1 1 50.35729 1
849 3173 0 1 44.06297 1
859 2044 1 1 38.91034 1
866 2350 1 1 41.15264 1
874 3445 1 1 55.45791 1
885 951 1 1 51.23340 1
890 3395 1 1 52.82683 0
901 4091 0 1 42.63929 1
913 4015 0 1 61.07050 1
924 1083 1 1 49.65640 1
929 2288 1 1 48.85421 1
936 515 1 1 54.25599 1
938 2033 1 1 35.15127 0
946 191 1 1 67.90691 0
947 3966 0 1 55.43600 1
957 971 1 1 45.82067 1
959 3903 1 1 52.88980 0
960 2468 1 1 47.18138 1
969 824 1 1 53.59890 1
974 3924 0 1 44.10404 1
985 1037 1 1 41.94935 1
990 3908 0 1 63.61396 1
1002 1411 1 1 44.22724 1
1008 850 1 1 62.00137 1
1012 3613 0 1 40.55305 1
1018 2796 1 1 62.64476 0
1027 3818 0 1 42.33539 1
1039 3819 0 1 42.96783 1
1049 3767 0 1 55.96167 1
1058 3659 0 1 62.86105 1
1070 1297 1 1 51.24983 0
1076 2357 1 1 46.76249 1
1082 3728 0 1 54.07529 1
1094 3719 0 1 47.03628 1
1097 2419 1 1 55.72621 1
1106 786 1 1 46.10267 1
1109 945 1 1 52.28747 1
1113 3645 0 1 51.20055 1
1117 3086 1 1 33.86448 1
1124 3382 1 1 75.01164 1
1129 1427 1 1 30.86379 1
1135 762 1 1 61.80424 0
1138 3560 0 1 34.98700 1
1147 3539 0 1 55.04175 1
1158 1152 1 1 69.94114 0
1161 3532 0 1 49.60438 1
1171 140 1 1 69.37714 0
1172 3516 0 1 43.55647 1
1175 853 1 1 59.40862 1
1179 3504 0 1 48.75838 1
1190 2475 1 1 36.49281 1
1199 1536 1 1 45.76044 0
1203 3441 0 1 57.37166 1
1211 3466 0 1 42.74333 1
1222 186 1 1 58.81725 1
1223 2055 1 1 53.49760 1
1226 276 1 1 43.41410 1
1227 1076 1 1 53.30595 0
1232 3390 0 1 41.35524 1
1240 1684 1 1 60.95825 0
1246 3384 0 1 47.75359 1
1256 1212 1 1 35.49076 1
1261 3361 0 1 48.66256 1
1262 3243 0 1 52.66804 1
1272 2970 0 1 49.86995 1
1279 3326 0 1 30.27515 1
1288 3313 0 1 55.56742 1
1298 3293 0 1 52.15332 1
1308 1492 1 1 41.60986 1
1314 3278 0 1 55.45243 1
1315 3249 0 1 70.00411 1
1321 3242 0 1 43.94251 1
1324 3232 0 1 42.56810 1
1334 3225 0 1 44.56947 1
1335 3224 0 1 56.94456 1
1340 2241 1 1 40.26010 1
1348 974 1 1 37.60712 1
1353 2882 1 1 48.36140 1
1358 1576 1 1 70.83641 1
1362 733 1 1 35.79192 1
1366 2635 0 1 62.62286 1
1374 3125 0 1 50.64750 1
1379 3173 0 1 54.52704 1
1383 216 1 1 52.69268 1
1384 3112 0 1 52.72005 1
1394 797 1 1 56.77207 1
1398 3118 0 1 44.39699 1
1403 2999 0 1 29.55510 1
1404 2555 1 1 57.04038 1
1412 3034 0 1 44.62697 1
1421 3025 0 1 35.79740 1
1429 2991 0 1 40.71732 1
1432 2932 1 1 32.23272 1
1443 2963 0 1 41.09240 1
1453 2941 0 1 61.63997 1
1455 2890 0 1 37.05681 1
1464 2090 1 1 62.57906 1
1472 2081 1 1 48.97741 1
1477 2924 0 1 61.99042 1
1485 2840 0 1 72.77207 1
1492 904 1 1 61.29500 1
1495 2888 0 1 52.62423 1
1497 2893 0 1 49.76318 0
1507 2865 0 1 52.91444 1
1515 2845 0 1 47.26352 1
1521 2189 0 1 50.20397 1
1523 1786 1 1 69.34702 1
1527 1080 1 1 41.16906 1
1531 2336 0 1 59.16496 1
1538 790 1 1 36.07940 1
1542 2839 0 1 34.59548 1
1552 2826 0 1 42.71321 1
1558 1235 1 1 63.63039 1
1563 2719 0 1 56.62971 1
1571 597 1 1 46.26420 1
1575 334 1 1 61.24298 1
1576 2614 0 1 38.62012 1
1584 2691 0 1 38.77070 1
1593 2647 0 1 56.69541 1
1602 999 1 1 58.95140 0
1606 2636 0 1 36.92266 1
1610 348 1 1 62.41478 1
1613 2648 0 1 34.60917 1
1617 1165 1 1 58.33539 1
1621 2620 0 1 50.18207 1
1625 2601 0 1 42.68583 1
1626 2445 0 1 34.37919 1
1628 2302 1 1 33.18275 1
1630 2577 0 1 38.38193 1
1632 1947 1 1 59.76181 1
1634 1874 1 1 66.41205 1
1637 694 1 1 46.78987 1
1640 2500 0 1 56.07940 1
1648 837 1 1 41.37440 1
1652 2128 1 1 64.57221 1
1660 930 1 1 67.48802 1
1662 1690 1 1 44.82957 1
1666 2459 0 1 45.77139 1
1668 1435 1 1 32.95003 1
1674 940 1 1 41.22108 1
1678 2454 0 1 55.41684 1
1686 2452 0 1 47.98084 1
1689 2327 1 1 40.79124 1
1691 2307 0 1 56.97467 1
1692 2439 0 1 68.46270 1
1695 2434 0 1 78.43943 0
1703 737 1 1 39.85763 1
1707 2405 0 1 35.31006 1
1713 2370 0 1 31.44422 1
1719 2283 0 1 58.26420 1
1722 2371 0 1 51.48802 1
1730 2284 0 1 59.96988 1
1735 1674 1 1 74.52430 0
1736 2348 0 1 52.36413 1
1742 1850 1 1 42.78713 1
1745 1303 1 1 34.87474 1
1750 1542 1 1 44.13963 1
1754 1084 1 1 46.38193 1
1758 2287 0 1 56.30938 1
1765 179 1 1 70.90760 1
1766 1191 1 1 55.39493 1
1768 1898 1 1 45.08419 1
1775 2010 1 1 26.27789 1
1778 2238 0 1 50.47228 1
1786 2194 0 1 38.39836 1
1792 1649 1 1 47.41958 1
1795 1447 1 1 47.98084 1
1800 2020 0 1 38.31622 1
1804 2147 0 1 50.10815 1
1806 2105 0 1 35.08830 1
1811 1996 1 1 32.50376 1
1816 2102 0 1 56.15332 1
1823 2081 0 1 46.15469 1
1830 41 1 1 65.88364 1
1831 1673 1 1 33.94387 1
1835 2095 0 1 62.86105 1
1839 2087 0 1 48.56400 1
1846 2070 0 1 46.34908 1
1847 2077 0 1 38.85284 1
1849 1552 0 1 58.64750 1
1853 1067 1 1 48.93634 1
1855 799 1 1 67.57290 0
1859 2032 0 1 65.98494 1
1866 901 1 1 40.90075 1
1871 1785 1 1 50.24504 0
1874 1989 0 1 57.19644 1
1876 1971 0 1 60.53662 0
1883 875 1 1 35.35113 0
1885 1990 0 1 31.38125 1
1888 533 1 1 55.98631 0
1891 1969 0 1 52.72553 1
1894 1962 0 1 38.09172 1
1895 207 1 1 58.17112 1
1897 1969 0 1 45.21013 1
1900 1940 0 1 37.79877 1
1905 1597 1 1 60.65982 1
1909 1899 0 1 35.53457 1
1910 1885 0 1 43.06639 1
1915 1885 0 1 56.39151 1
1917 1818 0 1 30.57358 1
1922 1822 0 1 61.18275 1
1927 1663 0 1 58.29979 1
1932 1608 0 1 62.33265 1
1937 1508 0 1 37.99863 1
1941 1457 0 1 33.15264 1
$m1b$spM_lvlone
center scale
futime 2341.64103 1301.27242
I(status != "censored") NA NA
(Intercept) NA NA
age 50.01901 10.58126
sexfemale NA NA
$m1b$mu_reg_surv
[1] 0
$m1b$tau_reg_surv
[1] 0.001
$m1b$cens_Srv_ftm_stts_cn
1 3 12 16 23 29 35 42 50 57 58 70 72 84 91 102
0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1
115 118 119 134 138 150 153 155 168 180 186 187 190 200 203 215
0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1
231 241 255 259 270 272 282 290 305 309 325 340 351 360 368 375
0 1 0 1 0 0 0 1 0 1 1 0 1 0 1 1
384 388 397 407 416 419 424 430 436 447 463 470 483 497 507 510
0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0
516 522 535 545 559 563 576 587 593 608 612 617 618 620 624 635
1 0 0 1 0 0 1 1 1 0 0 0 0 0 1 0
639 649 659 674 677 688 689 691 695 699 708 711 712 727 730 731
0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1
745 749 763 776 780 792 804 805 815 825 830 841 849 859 866 874
0 1 1 0 1 1 0 0 0 0 1 0 1 0 0 0
885 890 901 913 924 929 936 938 946 947 957 959 960 969 974 985
0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0
990 1002 1008 1012 1018 1027 1039 1049 1058 1070 1076 1082 1094 1097 1106 1109
1 0 0 1 0 1 1 1 1 0 0 1 1 0 0 0
1113 1117 1124 1129 1135 1138 1147 1158 1161 1171 1172 1175 1179 1190 1199 1203
1 0 0 0 0 1 1 0 1 0 1 0 1 0 0 1
1211 1222 1223 1226 1227 1232 1240 1246 1256 1261 1262 1272 1279 1288 1298 1308
1 0 0 0 0 1 0 1 0 1 1 1 1 1 1 0
1314 1315 1321 1324 1334 1335 1340 1348 1353 1358 1362 1366 1374 1379 1383 1384
1 1 1 1 1 1 0 0 0 0 0 1 1 1 0 1
1394 1398 1403 1404 1412 1421 1429 1432 1443 1453 1455 1464 1472 1477 1485 1492
0 1 1 0 1 1 1 0 1 1 1 0 0 1 1 0
1495 1497 1507 1515 1521 1523 1527 1531 1538 1542 1552 1558 1563 1571 1575 1576
1 1 1 1 1 0 0 1 0 1 1 0 1 0 0 1
1584 1593 1602 1606 1610 1613 1617 1621 1625 1626 1628 1630 1632 1634 1637 1640
1 1 0 1 0 1 0 1 1 1 0 1 0 0 0 1
1648 1652 1660 1662 1666 1668 1674 1678 1686 1689 1691 1692 1695 1703 1707 1713
0 0 0 0 1 0 0 1 1 0 1 1 1 0 1 1
1719 1722 1730 1735 1736 1742 1745 1750 1754 1758 1765 1766 1768 1775 1778 1786
1 1 1 0 1 0 0 0 0 1 0 0 0 0 1 1
1792 1795 1800 1804 1806 1811 1816 1823 1830 1831 1835 1839 1846 1847 1849 1853
0 0 1 1 1 0 1 1 0 0 1 1 1 1 1 0
1855 1859 1866 1871 1874 1876 1883 1885 1888 1891 1894 1895 1897 1900 1905 1909
0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 1
1910 1915 1917 1922 1927 1932 1937 1941
1 1 1 1 1 1 1 1
$m1b$Srv_ftm_stts_cn
[1] 400 NA 1012 1925 1505 2503 NA 2466 2400 51 3762 304 NA 1217 3584
[16] NA 769 132 NA 1356 3657 673 264 4079 NA 1444 77 549 5074 321
[31] 3839 NA 3170 NA 2847 NA 223 3244 2297 NA 1350 NA NA 3428 NA
[46] 2256 NA NA 708 2598 3853 2386 1000 1434 1360 1847 3282 NA 2224 NA
[61] NA 3090 859 1487 NA 4191 2769 NA 1170 3683 NA NA NA 1827 1191
[76] 71 326 1690 NA 890 2540 3574 NA NA 3358 1657 198 3076 1741 2689
[91] 460 389 NA 750 137 NA 620 NA NA 552 NA NA 110 3086 3092
[106] 3222 NA 2583 NA 2044 2350 3445 951 3395 NA NA 1083 2288 515 2033
[121] 191 NA 971 3903 2468 824 NA 1037 NA 1411 850 NA 2796 NA NA
[136] NA NA 1297 2357 NA NA 2419 786 945 NA 3086 3382 1427 762 NA
[151] NA 1152 NA 140 NA 853 NA 2475 1536 NA NA 186 2055 276 1076
[166] NA 1684 NA 1212 NA NA NA NA NA NA 1492 NA NA NA NA
[181] NA NA 2241 974 2882 1576 733 NA NA NA 216 NA 797 NA NA
[196] 2555 NA NA NA 2932 NA NA NA 2090 2081 NA NA 904 NA NA
[211] NA NA NA 1786 1080 NA 790 NA NA 1235 NA 597 334 NA NA
[226] NA 999 NA 348 NA 1165 NA NA NA 2302 NA 1947 1874 694 NA
[241] 837 2128 930 1690 NA 1435 940 NA NA 2327 NA NA NA 737 NA
[256] NA NA NA NA 1674 NA 1850 1303 1542 1084 NA 179 1191 1898 2010
[271] NA NA 1649 1447 NA NA NA 1996 NA NA 41 1673 NA NA NA
[286] NA NA 1067 799 NA 901 1785 NA NA 875 NA 533 NA NA 207
[301] NA NA 1597 NA NA NA NA NA NA NA NA NA
$m2a
$m2a$M_lvlone
futime status != "censored" copper (Intercept)
1 400 1 156 1
3 5169 0 54 1
12 1012 1 210 1
16 1925 1 64 1
23 1505 1 143 1
29 2503 1 50 1
35 2501 0 52 1
42 2466 1 52 1
50 2400 1 79 1
57 51 1 140 1
58 3762 1 46 1
70 304 1 94 1
72 4247 0 40 1
84 1217 1 43 1
91 3584 1 173 1
102 4345 0 28 1
115 769 1 159 1
118 132 1 588 1
119 4901 0 39 1
134 1356 1 140 1
138 3657 1 41 1
150 673 1 464 1
153 264 1 558 1
155 4079 1 124 1
168 4796 0 40 1
180 1444 1 53 1
186 77 1 221 1
187 549 1 209 1
190 5074 1 24 1
200 321 1 172 1
203 3839 1 114 1
215 5192 0 101 1
231 3170 1 82 1
241 4602 0 37 1
255 2847 1 201 1
259 4281 0 18 1
270 223 1 150 1
272 3244 1 102 1
282 2297 1 52 1
290 5136 0 105 1
305 1350 1 96 1
309 5122 0 122 1
325 5225 0 36 1
340 3428 1 131 1
351 4694 0 19 1
360 2256 1 161 1
368 3245 0 68 1
375 5096 0 281 1
384 708 1 58 1
388 2598 1 43 1
397 3853 1 54 1
407 2386 1 158 1
416 1000 1 94 1
419 1434 1 262 1
424 1360 1 121 1
430 1847 1 88 1
436 3282 1 231 1
447 5128 0 73 1
463 2224 1 49 1
470 5034 0 82 1
483 4925 0 28 1
497 3090 1 58 1
507 859 1 95 1
510 1487 1 52 1
516 4842 0 74 1
522 4191 1 105 1
535 2769 1 84 1
545 4708 0 58 1
559 1170 1 159 1
563 3683 1 20 1
576 4865 0 79 1
587 4853 0 51 1
593 4859 0 17 1
608 1827 1 280 1
612 1191 1 207 1
617 71 1 111 1
618 326 1 199 1
620 1690 1 75 1
624 4376 0 13 1
635 890 1 269 1
639 2540 1 34 1
649 3574 1 154 1
659 4719 0 48 1
674 4701 0 29 1
677 3358 1 58 1
688 1657 1 75 1
689 198 1 75 1
691 3076 1 37 1
695 1741 1 50 1
699 2689 1 94 1
708 460 1 110 1
711 389 1 36 1
712 4583 0 73 1
727 750 1 178 1
730 137 1 182 1
731 4520 0 33 1
745 620 1 62 1
749 4492 0 77 1
763 4489 0 148 1
776 552 1 145 1
780 4250 0 31 1
792 3770 0 172 1
804 110 1 57 1
805 3086 1 20 1
815 3092 1 38 1
825 3222 1 50 1
830 4058 0 10 1
841 2583 1 14 1
849 3173 0 53 1
859 2044 1 74 1
866 2350 1 77 1
874 3445 1 89 1
885 951 1 103 1
890 3395 1 208 1
901 4091 0 81 1
913 4015 0 74 1
924 1083 1 111 1
929 2288 1 67 1
936 515 1 129 1
938 2033 1 444 1
946 191 1 73 1
947 3966 0 29 1
957 971 1 18 1
959 3903 1 25 1
960 2468 1 75 1
969 824 1 NA 1
974 3924 0 9 1
985 1037 1 42 1
990 3908 0 30 1
1002 1411 1 205 1
1008 850 1 74 1
1012 3613 0 60 1
1018 2796 1 13 1
1027 3818 0 35 1
1039 3819 0 123 1
1049 3767 0 27 1
1058 3659 0 79 1
1070 1297 1 262 1
1076 2357 1 159 1
1082 3728 0 38 1
1094 3719 0 59 1
1097 2419 1 20 1
1106 786 1 86 1
1109 945 1 152 1
1113 3645 0 96 1
1117 3086 1 58 1
1124 3382 1 32 1
1129 1427 1 247 1
1135 762 1 290 1
1138 3560 0 57 1
1147 3539 0 57 1
1158 1152 1 243 1
1161 3532 0 68 1
1171 140 1 225 1
1172 3516 0 57 1
1175 853 1 219 1
1179 3504 0 34 1
1190 2475 1 32 1
1199 1536 1 217 1
1203 3441 0 13 1
1211 3466 0 4 1
1222 186 1 91 1
1223 2055 1 20 1
1226 276 1 161 1
1227 1076 1 80 1
1232 3390 0 84 1
1240 1684 1 200 1
1246 3384 0 44 1
1256 1212 1 67 1
1261 3361 0 32 1
1262 3243 0 31 1
1272 2970 0 65 1
1279 3326 0 76 1
1288 3313 0 63 1
1298 3293 0 152 1
1308 1492 1 77 1
1314 3278 0 49 1
1315 3249 0 51 1
1321 3242 0 39 1
1324 3232 0 44 1
1334 3225 0 63 1
1335 3224 0 161 1
1340 2241 1 44 1
1348 974 1 358 1
1353 2882 1 42 1
1358 1576 1 51 1
1362 733 1 251 1
1366 2635 0 41 1
1374 3125 0 52 1
1379 3173 0 24 1
1383 216 1 233 1
1384 3112 0 67 1
1394 797 1 267 1
1398 3118 0 50 1
1403 2999 0 76 1
1404 2555 1 13 1
1412 3034 0 25 1
1421 3025 0 38 1
1429 2991 0 52 1
1432 2932 1 112 1
1443 2963 0 26 1
1453 2941 0 15 1
1455 2890 0 227 1
1464 2090 1 23 1
1472 2081 1 108 1
1477 2924 0 12 1
1485 2840 0 14 1
1492 904 1 58 1
1495 2888 0 11 1
1497 2893 0 81 1
1507 2865 0 27 1
1515 2845 0 11 1
1521 2189 0 9 1
1523 1786 1 34 1
1527 1080 1 141 1
1531 2336 0 20 1
1538 790 1 65 1
1542 2839 0 29 1
1552 2826 0 67 1
1558 1235 1 96 1
1563 2719 0 72 1
1571 597 1 227 1
1575 334 1 123 1
1576 2614 0 67 1
1584 2691 0 108 1
1593 2647 0 39 1
1602 999 1 172 1
1606 2636 0 41 1
1610 348 1 200 1
1613 2648 0 51 1
1617 1165 1 115 1
1621 2620 0 47 1
1625 2601 0 412 1
1626 2445 0 78 1
1628 2302 1 71 1
1630 2577 0 67 1
1632 1947 1 105 1
1634 1874 1 NA 1
1637 694 1 231 1
1640 2500 0 24 1
1648 837 1 308 1
1652 2128 1 86 1
1660 930 1 139 1
1662 1690 1 121 1
1666 2459 0 48 1
1668 1435 1 63 1
1674 940 1 89 1
1678 2454 0 39 1
1686 2452 0 35 1
1689 2327 1 23 1
1691 2307 0 40 1
1692 2439 0 116 1
1695 2434 0 380 1
1703 737 1 121 1
1707 2405 0 35 1
1713 2370 0 48 1
1719 2283 0 36 1
1722 2371 0 80 1
1730 2284 0 42 1
1735 1674 1 188 1
1736 2348 0 129 1
1742 1850 1 65 1
1745 1303 1 45 1
1750 1542 1 188 1
1754 1084 1 121 1
1758 2287 0 52 1
1765 179 1 138 1
1766 1191 1 16 1
1768 1898 1 143 1
1775 2010 1 102 1
1778 2238 0 94 1
1786 2194 0 22 1
1792 1649 1 75 1
1795 1447 1 136 1
1800 2020 0 77 1
1804 2147 0 67 1
1806 2105 0 44 1
1811 1996 1 130 1
1816 2102 0 38 1
1823 2081 0 69 1
1830 41 1 220 1
1831 1673 1 97 1
1835 2095 0 103 1
1839 2087 0 75 1
1846 2070 0 65 1
1847 2077 0 70 1
1849 1552 0 155 1
1853 1067 1 107 1
1855 799 1 177 1
1859 2032 0 33 1
1866 901 1 123 1
1871 1785 1 84 1
1874 1989 0 196 1
1876 1971 0 88 1
1883 875 1 102 1
1885 1990 0 62 1
1888 533 1 100 1
1891 1969 0 73 1
1894 1962 0 90 1
1895 207 1 234 1
1897 1969 0 50 1
1900 1940 0 43 1
1905 1597 1 108 1
1909 1899 0 22 1
1910 1885 0 73 1
1915 1885 0 31 1
1917 1818 0 52 1
1922 1822 0 24 1
1927 1663 0 41 1
1932 1608 0 39 1
1937 1508 0 69 1
1941 1457 0 186 1
$m2a$spM_lvlone
center scale
futime 2341.64103 1301.27242
status != "censored" NA NA
copper 97.64839 85.61392
(Intercept) NA NA
$m2a$mu_reg_norm
[1] 0
$m2a$tau_reg_norm
[1] 1e-04
$m2a$shape_tau_norm
[1] 0.01
$m2a$rate_tau_norm
[1] 0.01
$m2a$mu_reg_surv
[1] 0
$m2a$tau_reg_surv
[1] 0.001
$m2a$cens_Srv_ftm_stts_cn
1 3 12 16 23 29 35 42 50 57 58 70 72 84 91 102
0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1
115 118 119 134 138 150 153 155 168 180 186 187 190 200 203 215
0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1
231 241 255 259 270 272 282 290 305 309 325 340 351 360 368 375
0 1 0 1 0 0 0 1 0 1 1 0 1 0 1 1
384 388 397 407 416 419 424 430 436 447 463 470 483 497 507 510
0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0
516 522 535 545 559 563 576 587 593 608 612 617 618 620 624 635
1 0 0 1 0 0 1 1 1 0 0 0 0 0 1 0
639 649 659 674 677 688 689 691 695 699 708 711 712 727 730 731
0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1
745 749 763 776 780 792 804 805 815 825 830 841 849 859 866 874
0 1 1 0 1 1 0 0 0 0 1 0 1 0 0 0
885 890 901 913 924 929 936 938 946 947 957 959 960 969 974 985
0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0
990 1002 1008 1012 1018 1027 1039 1049 1058 1070 1076 1082 1094 1097 1106 1109
1 0 0 1 0 1 1 1 1 0 0 1 1 0 0 0
1113 1117 1124 1129 1135 1138 1147 1158 1161 1171 1172 1175 1179 1190 1199 1203
1 0 0 0 0 1 1 0 1 0 1 0 1 0 0 1
1211 1222 1223 1226 1227 1232 1240 1246 1256 1261 1262 1272 1279 1288 1298 1308
1 0 0 0 0 1 0 1 0 1 1 1 1 1 1 0
1314 1315 1321 1324 1334 1335 1340 1348 1353 1358 1362 1366 1374 1379 1383 1384
1 1 1 1 1 1 0 0 0 0 0 1 1 1 0 1
1394 1398 1403 1404 1412 1421 1429 1432 1443 1453 1455 1464 1472 1477 1485 1492
0 1 1 0 1 1 1 0 1 1 1 0 0 1 1 0
1495 1497 1507 1515 1521 1523 1527 1531 1538 1542 1552 1558 1563 1571 1575 1576
1 1 1 1 1 0 0 1 0 1 1 0 1 0 0 1
1584 1593 1602 1606 1610 1613 1617 1621 1625 1626 1628 1630 1632 1634 1637 1640
1 1 0 1 0 1 0 1 1 1 0 1 0 0 0 1
1648 1652 1660 1662 1666 1668 1674 1678 1686 1689 1691 1692 1695 1703 1707 1713
0 0 0 0 1 0 0 1 1 0 1 1 1 0 1 1
1719 1722 1730 1735 1736 1742 1745 1750 1754 1758 1765 1766 1768 1775 1778 1786
1 1 1 0 1 0 0 0 0 1 0 0 0 0 1 1
1792 1795 1800 1804 1806 1811 1816 1823 1830 1831 1835 1839 1846 1847 1849 1853
0 0 1 1 1 0 1 1 0 0 1 1 1 1 1 0
1855 1859 1866 1871 1874 1876 1883 1885 1888 1891 1894 1895 1897 1900 1905 1909
0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 1
1910 1915 1917 1922 1927 1932 1937 1941
1 1 1 1 1 1 1 1
$m2a$Srv_ftm_stts_cn
[1] 400 NA 1012 1925 1505 2503 NA 2466 2400 51 3762 304 NA 1217 3584
[16] NA 769 132 NA 1356 3657 673 264 4079 NA 1444 77 549 5074 321
[31] 3839 NA 3170 NA 2847 NA 223 3244 2297 NA 1350 NA NA 3428 NA
[46] 2256 NA NA 708 2598 3853 2386 1000 1434 1360 1847 3282 NA 2224 NA
[61] NA 3090 859 1487 NA 4191 2769 NA 1170 3683 NA NA NA 1827 1191
[76] 71 326 1690 NA 890 2540 3574 NA NA 3358 1657 198 3076 1741 2689
[91] 460 389 NA 750 137 NA 620 NA NA 552 NA NA 110 3086 3092
[106] 3222 NA 2583 NA 2044 2350 3445 951 3395 NA NA 1083 2288 515 2033
[121] 191 NA 971 3903 2468 824 NA 1037 NA 1411 850 NA 2796 NA NA
[136] NA NA 1297 2357 NA NA 2419 786 945 NA 3086 3382 1427 762 NA
[151] NA 1152 NA 140 NA 853 NA 2475 1536 NA NA 186 2055 276 1076
[166] NA 1684 NA 1212 NA NA NA NA NA NA 1492 NA NA NA NA
[181] NA NA 2241 974 2882 1576 733 NA NA NA 216 NA 797 NA NA
[196] 2555 NA NA NA 2932 NA NA NA 2090 2081 NA NA 904 NA NA
[211] NA NA NA 1786 1080 NA 790 NA NA 1235 NA 597 334 NA NA
[226] NA 999 NA 348 NA 1165 NA NA NA 2302 NA 1947 1874 694 NA
[241] 837 2128 930 1690 NA 1435 940 NA NA 2327 NA NA NA 737 NA
[256] NA NA NA NA 1674 NA 1850 1303 1542 1084 NA 179 1191 1898 2010
[271] NA NA 1649 1447 NA NA NA 1996 NA NA 41 1673 NA NA NA
[286] NA NA 1067 799 NA 901 1785 NA NA 875 NA 533 NA NA 207
[301] NA NA 1597 NA NA NA NA NA NA NA NA NA
$m3a
$m3a$M_lvlone
futime status != "censored" trig copper (Intercept) sexfemale age
1 400 1 172 156 1 1 58.76523
3 5169 0 88 54 1 1 56.44627
12 1012 1 55 210 1 0 70.07255
16 1925 1 92 64 1 1 54.74059
23 1505 1 72 143 1 1 38.10541
29 2503 1 63 50 1 1 66.25873
35 2501 0 213 52 1 1 55.53457
42 2466 1 189 52 1 1 53.05681
50 2400 1 88 79 1 1 42.50787
57 51 1 143 140 1 1 70.55989
58 3762 1 79 46 1 1 53.71389
70 304 1 95 94 1 1 59.13758
72 4247 0 130 40 1 1 45.68925
84 1217 1 NA 43 1 0 56.22177
91 3584 1 96 173 1 1 64.64613
102 4345 0 58 28 1 1 40.44353
115 769 1 128 159 1 1 52.18344
118 132 1 200 588 1 1 53.93018
119 4901 0 123 39 1 1 49.56057
134 1356 1 135 140 1 1 59.95346
138 3657 1 83 41 1 0 64.18891
150 673 1 55 464 1 1 56.27652
153 264 1 191 558 1 1 55.96715
155 4079 1 230 124 1 0 44.52019
168 4796 0 66 40 1 1 45.07324
180 1444 1 166 53 1 1 52.02464
186 77 1 168 221 1 1 54.43943
187 549 1 195 209 1 1 44.94730
190 5074 1 86 24 1 1 63.87680
200 321 1 158 172 1 1 41.38535
203 3839 1 101 114 1 1 41.55236
215 5192 0 158 101 1 1 53.99589
231 3170 1 113 82 1 1 51.28268
241 4602 0 64 37 1 1 52.06023
255 2847 1 151 201 1 1 48.61875
259 4281 0 96 18 1 1 56.41068
270 223 1 118 150 1 1 61.72758
272 3244 1 87 102 1 1 36.62697
282 2297 1 111 52 1 1 55.39220
290 5136 0 NA 105 1 1 46.66940
305 1350 1 NA 96 1 1 33.63450
309 5122 0 NA 122 1 1 33.69473
325 5225 0 89 36 1 1 48.87064
340 3428 1 50 131 1 1 37.58248
351 4694 0 NA 19 1 1 41.79329
360 2256 1 165 161 1 1 45.79877
368 3245 0 71 68 1 1 47.42779
375 5096 0 178 281 1 0 49.13621
384 708 1 NA 58 1 1 61.15264
388 2598 1 73 43 1 1 53.50856
397 3853 1 143 54 1 1 52.08761
407 2386 1 93 158 1 0 50.54073
416 1000 1 NA 94 1 1 67.40862
419 1434 1 125 262 1 1 39.19781
424 1360 1 219 121 1 0 65.76318
430 1847 1 319 88 1 1 33.61807
436 3282 1 94 231 1 1 53.57153
447 5128 0 118 73 1 0 44.56947
463 2224 1 124 49 1 1 40.39425
470 5034 0 58 82 1 1 58.38193
483 4925 0 188 28 1 0 43.89870
497 3090 1 112 58 1 1 60.70637
507 859 1 133 95 1 1 46.62834
510 1487 1 188 52 1 1 62.90760
516 4842 0 108 74 1 1 40.20260
522 4191 1 171 105 1 0 46.45311
535 2769 1 185 84 1 1 51.28816
545 4708 0 46 58 1 1 32.61328
559 1170 1 184 159 1 1 49.33881
563 3683 1 NA 20 1 1 56.39973
576 4865 0 76 79 1 1 48.84600
587 4853 0 80 51 1 1 32.49281
593 4859 0 56 17 1 1 38.49418
608 1827 1 309 280 1 1 51.92060
612 1191 1 598 207 1 1 43.51814
617 71 1 243 111 1 1 51.94251
618 326 1 91 199 1 1 49.82615
620 1690 1 104 75 1 1 47.94524
624 4376 0 70 13 1 1 46.51608
635 890 1 91 269 1 0 67.41136
639 2540 1 318 34 1 1 63.26352
649 3574 1 272 154 1 1 67.31006
659 4719 0 100 48 1 1 56.01369
674 4701 0 74 29 1 1 55.83025
677 3358 1 84 58 1 1 47.21697
688 1657 1 174 75 1 1 52.75838
689 198 1 72 75 1 1 37.27858
691 3076 1 83 37 1 1 41.39357
695 1741 1 98 50 1 1 52.44353
699 2689 1 155 94 1 0 33.47570
708 460 1 56 110 1 1 45.60712
711 389 1 70 36 1 1 76.70910
712 4583 0 68 73 1 1 36.53388
727 750 1 69 178 1 1 53.91650
730 137 1 NA 182 1 1 46.39014
731 4520 0 NA 33 1 1 48.84600
745 620 1 91 62 1 0 71.89322
749 4492 0 101 77 1 1 28.88433
763 4489 0 118 148 1 0 48.46817
776 552 1 122 145 1 0 51.46886
780 4250 0 166 31 1 1 44.95003
792 3770 0 84 172 1 1 56.56947
804 110 1 102 57 1 1 48.96372
805 3086 1 53 20 1 1 43.01711
815 3092 1 102 38 1 1 34.03970
825 3222 1 NA 50 1 1 68.50924
830 4058 0 77 10 1 1 62.52156
841 2583 1 84 14 1 1 50.35729
849 3173 0 52 53 1 1 44.06297
859 2044 1 109 74 1 1 38.91034
866 2350 1 78 77 1 1 41.15264
874 3445 1 90 89 1 1 55.45791
885 951 1 100 103 1 1 51.23340
890 3395 1 78 208 1 0 52.82683
901 4091 0 156 81 1 1 42.63929
913 4015 0 382 74 1 1 61.07050
924 1083 1 149 111 1 1 49.65640
929 2288 1 93 67 1 1 48.85421
936 515 1 114 129 1 1 54.25599
938 2033 1 210 444 1 0 35.15127
946 191 1 49 73 1 0 67.90691
947 3966 0 85 29 1 1 55.43600
957 971 1 NA 18 1 1 45.82067
959 3903 1 107 25 1 0 52.88980
960 2468 1 137 75 1 1 47.18138
969 824 1 68 NA 1 1 53.59890
974 3924 0 95 9 1 1 44.10404
985 1037 1 NA 42 1 1 41.94935
990 3908 0 232 30 1 1 63.61396
1002 1411 1 97 205 1 1 44.22724
1008 850 1 104 74 1 1 62.00137
1012 3613 0 175 60 1 1 40.55305
1018 2796 1 99 13 1 0 62.64476
1027 3818 0 105 35 1 1 42.33539
1039 3819 0 121 123 1 1 42.96783
1049 3767 0 69 27 1 1 55.96167
1058 3659 0 152 79 1 1 62.86105
1070 1297 1 218 262 1 0 51.24983
1076 2357 1 134 159 1 1 46.76249
1082 3728 0 146 38 1 1 54.07529
1094 3719 0 56 59 1 1 47.03628
1097 2419 1 88 20 1 1 55.72621
1106 786 1 103 86 1 1 46.10267
1109 945 1 171 152 1 1 52.28747
1113 3645 0 106 96 1 1 51.20055
1117 3086 1 NA 58 1 1 33.86448
1124 3382 1 114 32 1 1 75.01164
1129 1427 1 280 247 1 1 30.86379
1135 762 1 112 290 1 0 61.80424
1138 3560 0 NA 57 1 1 34.98700
1147 3539 0 174 57 1 1 55.04175
1158 1152 1 126 243 1 0 69.94114
1161 3532 0 68 68 1 1 49.60438
1171 140 1 75 225 1 0 69.37714
1172 3516 0 80 57 1 1 43.55647
1175 853 1 205 219 1 1 59.40862
1179 3504 0 206 34 1 1 48.75838
1190 2475 1 118 32 1 1 36.49281
1199 1536 1 140 217 1 0 45.76044
1203 3441 0 119 13 1 1 57.37166
1211 3466 0 180 4 1 1 42.74333
1222 186 1 101 91 1 1 58.81725
1223 2055 1 68 20 1 1 53.49760
1226 276 1 NA 161 1 1 43.41410
1227 1076 1 142 80 1 0 53.30595
1232 3390 0 108 84 1 1 41.35524
1240 1684 1 154 200 1 0 60.95825
1246 3384 0 110 44 1 1 47.75359
1256 1212 1 137 67 1 1 35.49076
1261 3361 0 52 32 1 1 48.66256
1262 3243 0 NA 31 1 1 52.66804
1272 2970 0 126 65 1 1 49.86995
1279 3326 0 86 76 1 1 30.27515
1288 3313 0 NA 63 1 1 55.56742
1298 3293 0 168 152 1 1 52.15332
1308 1492 1 NA 77 1 1 41.60986
1314 3278 0 133 49 1 1 55.45243
1315 3249 0 NA 51 1 1 70.00411
1321 3242 0 133 39 1 1 43.94251
1324 3232 0 76 44 1 1 42.56810
1334 3225 0 106 63 1 1 44.56947
1335 3224 0 NA 161 1 1 56.94456
1340 2241 1 145 44 1 1 40.26010
1348 974 1 140 358 1 1 37.60712
1353 2882 1 94 42 1 1 48.36140
1358 1576 1 62 51 1 1 70.83641
1362 733 1 137 251 1 1 35.79192
1366 2635 0 98 41 1 1 62.62286
1374 3125 0 164 52 1 1 50.64750
1379 3173 0 NA 24 1 1 54.52704
1383 216 1 432 233 1 1 52.69268
1384 3112 0 101 67 1 1 52.72005
1394 797 1 157 267 1 1 56.77207
1398 3118 0 56 50 1 1 44.39699
1403 2999 0 90 76 1 1 29.55510
1404 2555 1 62 13 1 1 57.04038
1412 3034 0 102 25 1 1 44.62697
1421 3025 0 90 38 1 1 35.79740
1429 2991 0 194 52 1 1 40.71732
1432 2932 1 114 112 1 1 32.23272
1443 2963 0 67 26 1 1 41.09240
1453 2941 0 89 15 1 1 61.63997
1455 2890 0 57 227 1 1 37.05681
1464 2090 1 58 23 1 1 62.57906
1472 2081 1 NA 108 1 1 48.97741
1477 2924 0 68 12 1 1 61.99042
1485 2840 0 NA 14 1 1 72.77207
1492 904 1 131 58 1 1 61.29500
1495 2888 0 58 11 1 1 52.62423
1497 2893 0 90 81 1 0 49.76318
1507 2865 0 NA 27 1 1 52.91444
1515 2845 0 99 11 1 1 47.26352
1521 2189 0 55 9 1 1 50.20397
1523 1786 1 224 34 1 1 69.34702
1527 1080 1 157 141 1 1 41.16906
1531 2336 0 NA 20 1 1 59.16496
1538 790 1 210 65 1 1 36.07940
1542 2839 0 NA 29 1 1 34.59548
1552 2826 0 85 67 1 1 42.71321
1558 1235 1 113 96 1 1 63.63039
1563 2719 0 154 72 1 1 56.62971
1571 597 1 135 227 1 1 46.26420
1575 334 1 155 123 1 1 61.24298
1576 2614 0 101 67 1 1 38.62012
1584 2691 0 135 108 1 1 38.77070
1593 2647 0 104 39 1 1 56.69541
1602 999 1 63 172 1 0 58.95140
1606 2636 0 74 41 1 1 36.92266
1610 348 1 118 200 1 1 62.41478
1613 2648 0 44 51 1 1 34.60917
1617 1165 1 90 115 1 1 58.33539
1621 2620 0 87 47 1 1 50.18207
1625 2601 0 87 412 1 1 42.68583
1626 2445 0 177 78 1 1 34.37919
1628 2302 1 111 71 1 1 33.18275
1630 2577 0 106 67 1 1 38.38193
1632 1947 1 108 105 1 1 59.76181
1634 1874 1 81 NA 1 1 66.41205
1637 694 1 99 231 1 1 46.78987
1640 2500 0 139 24 1 1 56.07940
1648 837 1 322 308 1 1 41.37440
1652 2128 1 152 86 1 1 64.57221
1660 930 1 139 139 1 1 67.48802
1662 1690 1 231 121 1 1 44.82957
1666 2459 0 214 48 1 1 45.77139
1668 1435 1 33 63 1 1 32.95003
1674 940 1 139 89 1 1 41.22108
1678 2454 0 146 39 1 1 55.41684
1686 2452 0 71 35 1 1 47.98084
1689 2327 1 85 23 1 1 40.79124
1691 2307 0 120 40 1 1 56.97467
1692 2439 0 90 116 1 1 68.46270
1695 2434 0 154 380 1 0 78.43943
1703 737 1 91 121 1 1 39.85763
1707 2405 0 118 35 1 1 35.31006
1713 2370 0 108 48 1 1 31.44422
1719 2283 0 146 36 1 1 58.26420
1722 2371 0 55 80 1 1 51.48802
1730 2284 0 126 42 1 1 59.96988
1735 1674 1 128 188 1 0 74.52430
1736 2348 0 NA 129 1 1 52.36413
1742 1850 1 209 65 1 1 42.78713
1745 1303 1 77 45 1 1 34.87474
1750 1542 1 130 188 1 1 44.13963
1754 1084 1 146 121 1 1 46.38193
1758 2287 0 260 52 1 1 56.30938
1765 179 1 91 138 1 1 70.90760
1766 1191 1 179 16 1 1 55.39493
1768 1898 1 44 143 1 1 45.08419
1775 2010 1 117 102 1 1 26.27789
1778 2238 0 177 94 1 1 50.47228
1786 2194 0 85 22 1 1 38.39836
1792 1649 1 116 75 1 1 47.41958
1795 1447 1 NA 136 1 1 47.98084
1800 2020 0 114 77 1 1 38.31622
1804 2147 0 108 67 1 1 50.10815
1806 2105 0 111 44 1 1 35.08830
1811 1996 1 193 130 1 1 32.50376
1816 2102 0 56 38 1 1 56.15332
1823 2081 0 140 69 1 1 46.15469
1830 41 1 229 220 1 1 65.88364
1831 1673 1 169 97 1 1 33.94387
1835 2095 0 113 103 1 1 62.86105
1839 2087 0 195 75 1 1 48.56400
1846 2070 0 80 65 1 1 46.34908
1847 2077 0 118 70 1 1 38.85284
1849 1552 0 151 155 1 1 58.64750
1853 1067 1 242 107 1 1 48.93634
1855 799 1 242 177 1 0 67.57290
1859 2032 0 69 33 1 1 65.98494
1866 901 1 84 123 1 1 40.90075
1871 1785 1 153 84 1 0 50.24504
1874 1989 0 78 196 1 1 57.19644
1876 1971 0 133 88 1 0 60.53662
1883 875 1 194 102 1 0 35.35113
1885 1990 0 95 62 1 1 31.38125
1888 533 1 91 100 1 0 55.98631
1891 1969 0 93 73 1 1 52.72553
1894 1962 0 127 90 1 1 38.09172
1895 207 1 NA 234 1 1 58.17112
1897 1969 0 103 50 1 1 45.21013
1900 1940 0 104 43 1 1 37.79877
1905 1597 1 55 108 1 1 60.65982
1909 1899 0 75 22 1 1 35.53457
1910 1885 0 144 73 1 1 43.06639
1915 1885 0 64 31 1 1 56.39151
1917 1818 0 59 52 1 1 30.57358
1922 1822 0 113 24 1 1 61.18275
1927 1663 0 82 41 1 1 58.29979
1932 1608 0 100 39 1 1 62.33265
1937 1508 0 88 69 1 1 37.99863
1941 1457 0 149 186 1 1 33.15264
abs(age - copper) log(trig)
1 NA NA
3 NA NA
12 NA NA
16 NA NA
23 NA NA
29 NA NA
35 NA NA
42 NA NA
50 NA NA
57 NA NA
58 NA NA
70 NA NA
72 NA NA
84 NA NA
91 NA NA
102 NA NA
115 NA NA
118 NA NA
119 NA NA
134 NA NA
138 NA NA
150 NA NA
153 NA NA
155 NA NA
168 NA NA
180 NA NA
186 NA NA
187 NA NA
190 NA NA
200 NA NA
203 NA NA
215 NA NA
231 NA NA
241 NA NA
255 NA NA
259 NA NA
270 NA NA
272 NA NA
282 NA NA
290 NA NA
305 NA NA
309 NA NA
325 NA NA
340 NA NA
351 NA NA
360 NA NA
368 NA NA
375 NA NA
384 NA NA
388 NA NA
397 NA NA
407 NA NA
416 NA NA
419 NA NA
424 NA NA
430 NA NA
436 NA NA
447 NA NA
463 NA NA
470 NA NA
483 NA NA
497 NA NA
507 NA NA
510 NA NA
516 NA NA
522 NA NA
535 NA NA
545 NA NA
559 NA NA
563 NA NA
576 NA NA
587 NA NA
593 NA NA
608 NA NA
612 NA NA
617 NA NA
618 NA NA
620 NA NA
624 NA NA
635 NA NA
639 NA NA
649 NA NA
659 NA NA
674 NA NA
677 NA NA
688 NA NA
689 NA NA
691 NA NA
695 NA NA
699 NA NA
708 NA NA
711 NA NA
712 NA NA
727 NA NA
730 NA NA
731 NA NA
745 NA NA
749 NA NA
763 NA NA
776 NA NA
780 NA NA
792 NA NA
804 NA NA
805 NA NA
815 NA NA
825 NA NA
830 NA NA
841 NA NA
849 NA NA
859 NA NA
866 NA NA
874 NA NA
885 NA NA
890 NA NA
901 NA NA
913 NA NA
924 NA NA
929 NA NA
936 NA NA
938 NA NA
946 NA NA
947 NA NA
957 NA NA
959 NA NA
960 NA NA
969 NA NA
974 NA NA
985 NA NA
990 NA NA
1002 NA NA
1008 NA NA
1012 NA NA
1018 NA NA
1027 NA NA
1039 NA NA
1049 NA NA
1058 NA NA
1070 NA NA
1076 NA NA
1082 NA NA
1094 NA NA
1097 NA NA
1106 NA NA
1109 NA NA
1113 NA NA
1117 NA NA
1124 NA NA
1129 NA NA
1135 NA NA
1138 NA NA
1147 NA NA
1158 NA NA
1161 NA NA
1171 NA NA
1172 NA NA
1175 NA NA
1179 NA NA
1190 NA NA
1199 NA NA
1203 NA NA
1211 NA NA
1222 NA NA
1223 NA NA
1226 NA NA
1227 NA NA
1232 NA NA
1240 NA NA
1246 NA NA
1256 NA NA
1261 NA NA
1262 NA NA
1272 NA NA
1279 NA NA
1288 NA NA
1298 NA NA
1308 NA NA
1314 NA NA
1315 NA NA
1321 NA NA
1324 NA NA
1334 NA NA
1335 NA NA
1340 NA NA
1348 NA NA
1353 NA NA
1358 NA NA
1362 NA NA
1366 NA NA
1374 NA NA
1379 NA NA
1383 NA NA
1384 NA NA
1394 NA NA
1398 NA NA
1403 NA NA
1404 NA NA
1412 NA NA
1421 NA NA
1429 NA NA
1432 NA NA
1443 NA NA
1453 NA NA
1455 NA NA
1464 NA NA
1472 NA NA
1477 NA NA
1485 NA NA
1492 NA NA
1495 NA NA
1497 NA NA
1507 NA NA
1515 NA NA
1521 NA NA
1523 NA NA
1527 NA NA
1531 NA NA
1538 NA NA
1542 NA NA
1552 NA NA
1558 NA NA
1563 NA NA
1571 NA NA
1575 NA NA
1576 NA NA
1584 NA NA
1593 NA NA
1602 NA NA
1606 NA NA
1610 NA NA
1613 NA NA
1617 NA NA
1621 NA NA
1625 NA NA
1626 NA NA
1628 NA NA
1630 NA NA
1632 NA NA
1634 NA NA
1637 NA NA
1640 NA NA
1648 NA NA
1652 NA NA
1660 NA NA
1662 NA NA
1666 NA NA
1668 NA NA
1674 NA NA
1678 NA NA
1686 NA NA
1689 NA NA
1691 NA NA
1692 NA NA
1695 NA NA
1703 NA NA
1707 NA NA
1713 NA NA
1719 NA NA
1722 NA NA
1730 NA NA
1735 NA NA
1736 NA NA
1742 NA NA
1745 NA NA
1750 NA NA
1754 NA NA
1758 NA NA
1765 NA NA
1766 NA NA
1768 NA NA
1775 NA NA
1778 NA NA
1786 NA NA
1792 NA NA
1795 NA NA
1800 NA NA
1804 NA NA
1806 NA NA
1811 NA NA
1816 NA NA
1823 NA NA
1830 NA NA
1831 NA NA
1835 NA NA
1839 NA NA
1846 NA NA
1847 NA NA
1849 NA NA
1853 NA NA
1855 NA NA
1859 NA NA
1866 NA NA
1871 NA NA
1874 NA NA
1876 NA NA
1883 NA NA
1885 NA NA
1888 NA NA
1891 NA NA
1894 NA NA
1895 NA NA
1897 NA NA
1900 NA NA
1905 NA NA
1909 NA NA
1910 NA NA
1915 NA NA
1917 NA NA
1922 NA NA
1927 NA NA
1932 NA NA
1937 NA NA
1941 NA NA
$m3a$spM_lvlone
center scale
futime 2341.641026 1301.272424
status != "censored" NA NA
trig 124.702128 65.148639
copper 97.648387 85.613920
(Intercept) NA NA
sexfemale NA NA
age 50.019007 10.581261
abs(age - copper) 61.603707 76.189619
log(trig) 4.720406 0.447384
$m3a$mu_reg_norm
[1] 0
$m3a$tau_reg_norm
[1] 1e-04
$m3a$shape_tau_norm
[1] 0.01
$m3a$rate_tau_norm
[1] 0.01
$m3a$mu_reg_surv
[1] 0
$m3a$tau_reg_surv
[1] 0.001
$m3a$cens_Srv_ftm_stts_cn
1 3 12 16 23 29 35 42 50 57 58 70 72 84 91 102
0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1
115 118 119 134 138 150 153 155 168 180 186 187 190 200 203 215
0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1
231 241 255 259 270 272 282 290 305 309 325 340 351 360 368 375
0 1 0 1 0 0 0 1 0 1 1 0 1 0 1 1
384 388 397 407 416 419 424 430 436 447 463 470 483 497 507 510
0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0
516 522 535 545 559 563 576 587 593 608 612 617 618 620 624 635
1 0 0 1 0 0 1 1 1 0 0 0 0 0 1 0
639 649 659 674 677 688 689 691 695 699 708 711 712 727 730 731
0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1
745 749 763 776 780 792 804 805 815 825 830 841 849 859 866 874
0 1 1 0 1 1 0 0 0 0 1 0 1 0 0 0
885 890 901 913 924 929 936 938 946 947 957 959 960 969 974 985
0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0
990 1002 1008 1012 1018 1027 1039 1049 1058 1070 1076 1082 1094 1097 1106 1109
1 0 0 1 0 1 1 1 1 0 0 1 1 0 0 0
1113 1117 1124 1129 1135 1138 1147 1158 1161 1171 1172 1175 1179 1190 1199 1203
1 0 0 0 0 1 1 0 1 0 1 0 1 0 0 1
1211 1222 1223 1226 1227 1232 1240 1246 1256 1261 1262 1272 1279 1288 1298 1308
1 0 0 0 0 1 0 1 0 1 1 1 1 1 1 0
1314 1315 1321 1324 1334 1335 1340 1348 1353 1358 1362 1366 1374 1379 1383 1384
1 1 1 1 1 1 0 0 0 0 0 1 1 1 0 1
1394 1398 1403 1404 1412 1421 1429 1432 1443 1453 1455 1464 1472 1477 1485 1492
0 1 1 0 1 1 1 0 1 1 1 0 0 1 1 0
1495 1497 1507 1515 1521 1523 1527 1531 1538 1542 1552 1558 1563 1571 1575 1576
1 1 1 1 1 0 0 1 0 1 1 0 1 0 0 1
1584 1593 1602 1606 1610 1613 1617 1621 1625 1626 1628 1630 1632 1634 1637 1640
1 1 0 1 0 1 0 1 1 1 0 1 0 0 0 1
1648 1652 1660 1662 1666 1668 1674 1678 1686 1689 1691 1692 1695 1703 1707 1713
0 0 0 0 1 0 0 1 1 0 1 1 1 0 1 1
1719 1722 1730 1735 1736 1742 1745 1750 1754 1758 1765 1766 1768 1775 1778 1786
1 1 1 0 1 0 0 0 0 1 0 0 0 0 1 1
1792 1795 1800 1804 1806 1811 1816 1823 1830 1831 1835 1839 1846 1847 1849 1853
0 0 1 1 1 0 1 1 0 0 1 1 1 1 1 0
1855 1859 1866 1871 1874 1876 1883 1885 1888 1891 1894 1895 1897 1900 1905 1909
0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 1
1910 1915 1917 1922 1927 1932 1937 1941
1 1 1 1 1 1 1 1
$m3a$Srv_ftm_stts_cn
[1] 400 NA 1012 1925 1505 2503 NA 2466 2400 51 3762 304 NA 1217 3584
[16] NA 769 132 NA 1356 3657 673 264 4079 NA 1444 77 549 5074 321
[31] 3839 NA 3170 NA 2847 NA 223 3244 2297 NA 1350 NA NA 3428 NA
[46] 2256 NA NA 708 2598 3853 2386 1000 1434 1360 1847 3282 NA 2224 NA
[61] NA 3090 859 1487 NA 4191 2769 NA 1170 3683 NA NA NA 1827 1191
[76] 71 326 1690 NA 890 2540 3574 NA NA 3358 1657 198 3076 1741 2689
[91] 460 389 NA 750 137 NA 620 NA NA 552 NA NA 110 3086 3092
[106] 3222 NA 2583 NA 2044 2350 3445 951 3395 NA NA 1083 2288 515 2033
[121] 191 NA 971 3903 2468 824 NA 1037 NA 1411 850 NA 2796 NA NA
[136] NA NA 1297 2357 NA NA 2419 786 945 NA 3086 3382 1427 762 NA
[151] NA 1152 NA 140 NA 853 NA 2475 1536 NA NA 186 2055 276 1076
[166] NA 1684 NA 1212 NA NA NA NA NA NA 1492 NA NA NA NA
[181] NA NA 2241 974 2882 1576 733 NA NA NA 216 NA 797 NA NA
[196] 2555 NA NA NA 2932 NA NA NA 2090 2081 NA NA 904 NA NA
[211] NA NA NA 1786 1080 NA 790 NA NA 1235 NA 597 334 NA NA
[226] NA 999 NA 348 NA 1165 NA NA NA 2302 NA 1947 1874 694 NA
[241] 837 2128 930 1690 NA 1435 940 NA NA 2327 NA NA NA 737 NA
[256] NA NA NA NA 1674 NA 1850 1303 1542 1084 NA 179 1191 1898 2010
[271] NA NA 1649 1447 NA NA NA 1996 NA NA 41 1673 NA NA NA
[286] NA NA 1067 799 NA 901 1785 NA NA 875 NA 533 NA NA 207
[301] NA NA 1597 NA NA NA NA NA NA NA NA NA
$m3b
$m3b$M_center
(Intercept)
1 1
203 1
483 1
708 1
946 1
1147 1
1334 1
1507 1
1648 1
1778 1
$m3b$M_lvlone
futime status != "censored" trig copper sexfemale age
1 400 1 172 156 1 58.76523
3 5169 0 88 54 1 56.44627
12 1012 1 55 210 0 70.07255
16 1925 1 92 64 1 54.74059
23 1505 1 72 143 1 38.10541
29 2503 1 63 50 1 66.25873
35 2501 0 213 52 1 55.53457
42 2466 1 189 52 1 53.05681
50 2400 1 88 79 1 42.50787
57 51 1 143 140 1 70.55989
58 3762 1 79 46 1 53.71389
70 304 1 95 94 1 59.13758
72 4247 0 130 40 1 45.68925
84 1217 1 NA 43 0 56.22177
91 3584 1 96 173 1 64.64613
102 4345 0 58 28 1 40.44353
115 769 1 128 159 1 52.18344
118 132 1 200 588 1 53.93018
119 4901 0 123 39 1 49.56057
134 1356 1 135 140 1 59.95346
138 3657 1 83 41 0 64.18891
150 673 1 55 464 1 56.27652
153 264 1 191 558 1 55.96715
155 4079 1 230 124 0 44.52019
168 4796 0 66 40 1 45.07324
180 1444 1 166 53 1 52.02464
186 77 1 168 221 1 54.43943
187 549 1 195 209 1 44.94730
190 5074 1 86 24 1 63.87680
200 321 1 158 172 1 41.38535
203 3839 1 101 114 1 41.55236
215 5192 0 158 101 1 53.99589
231 3170 1 113 82 1 51.28268
241 4602 0 64 37 1 52.06023
255 2847 1 151 201 1 48.61875
259 4281 0 96 18 1 56.41068
270 223 1 118 150 1 61.72758
272 3244 1 87 102 1 36.62697
282 2297 1 111 52 1 55.39220
290 5136 0 NA 105 1 46.66940
305 1350 1 NA 96 1 33.63450
309 5122 0 NA 122 1 33.69473
325 5225 0 89 36 1 48.87064
340 3428 1 50 131 1 37.58248
351 4694 0 NA 19 1 41.79329
360 2256 1 165 161 1 45.79877
368 3245 0 71 68 1 47.42779
375 5096 0 178 281 0 49.13621
384 708 1 NA 58 1 61.15264
388 2598 1 73 43 1 53.50856
397 3853 1 143 54 1 52.08761
407 2386 1 93 158 0 50.54073
416 1000 1 NA 94 1 67.40862
419 1434 1 125 262 1 39.19781
424 1360 1 219 121 0 65.76318
430 1847 1 319 88 1 33.61807
436 3282 1 94 231 1 53.57153
447 5128 0 118 73 0 44.56947
463 2224 1 124 49 1 40.39425
470 5034 0 58 82 1 58.38193
483 4925 0 188 28 0 43.89870
497 3090 1 112 58 1 60.70637
507 859 1 133 95 1 46.62834
510 1487 1 188 52 1 62.90760
516 4842 0 108 74 1 40.20260
522 4191 1 171 105 0 46.45311
535 2769 1 185 84 1 51.28816
545 4708 0 46 58 1 32.61328
559 1170 1 184 159 1 49.33881
563 3683 1 NA 20 1 56.39973
576 4865 0 76 79 1 48.84600
587 4853 0 80 51 1 32.49281
593 4859 0 56 17 1 38.49418
608 1827 1 309 280 1 51.92060
612 1191 1 598 207 1 43.51814
617 71 1 243 111 1 51.94251
618 326 1 91 199 1 49.82615
620 1690 1 104 75 1 47.94524
624 4376 0 70 13 1 46.51608
635 890 1 91 269 0 67.41136
639 2540 1 318 34 1 63.26352
649 3574 1 272 154 1 67.31006
659 4719 0 100 48 1 56.01369
674 4701 0 74 29 1 55.83025
677 3358 1 84 58 1 47.21697
688 1657 1 174 75 1 52.75838
689 198 1 72 75 1 37.27858
691 3076 1 83 37 1 41.39357
695 1741 1 98 50 1 52.44353
699 2689 1 155 94 0 33.47570
708 460 1 56 110 1 45.60712
711 389 1 70 36 1 76.70910
712 4583 0 68 73 1 36.53388
727 750 1 69 178 1 53.91650
730 137 1 NA 182 1 46.39014
731 4520 0 NA 33 1 48.84600
745 620 1 91 62 0 71.89322
749 4492 0 101 77 1 28.88433
763 4489 0 118 148 0 48.46817
776 552 1 122 145 0 51.46886
780 4250 0 166 31 1 44.95003
792 3770 0 84 172 1 56.56947
804 110 1 102 57 1 48.96372
805 3086 1 53 20 1 43.01711
815 3092 1 102 38 1 34.03970
825 3222 1 NA 50 1 68.50924
830 4058 0 77 10 1 62.52156
841 2583 1 84 14 1 50.35729
849 3173 0 52 53 1 44.06297
859 2044 1 109 74 1 38.91034
866 2350 1 78 77 1 41.15264
874 3445 1 90 89 1 55.45791
885 951 1 100 103 1 51.23340
890 3395 1 78 208 0 52.82683
901 4091 0 156 81 1 42.63929
913 4015 0 382 74 1 61.07050
924 1083 1 149 111 1 49.65640
929 2288 1 93 67 1 48.85421
936 515 1 114 129 1 54.25599
938 2033 1 210 444 0 35.15127
946 191 1 49 73 0 67.90691
947 3966 0 85 29 1 55.43600
957 971 1 NA 18 1 45.82067
959 3903 1 107 25 0 52.88980
960 2468 1 137 75 1 47.18138
969 824 1 68 NA 1 53.59890
974 3924 0 95 9 1 44.10404
985 1037 1 NA 42 1 41.94935
990 3908 0 232 30 1 63.61396
1002 1411 1 97 205 1 44.22724
1008 850 1 104 74 1 62.00137
1012 3613 0 175 60 1 40.55305
1018 2796 1 99 13 0 62.64476
1027 3818 0 105 35 1 42.33539
1039 3819 0 121 123 1 42.96783
1049 3767 0 69 27 1 55.96167
1058 3659 0 152 79 1 62.86105
1070 1297 1 218 262 0 51.24983
1076 2357 1 134 159 1 46.76249
1082 3728 0 146 38 1 54.07529
1094 3719 0 56 59 1 47.03628
1097 2419 1 88 20 1 55.72621
1106 786 1 103 86 1 46.10267
1109 945 1 171 152 1 52.28747
1113 3645 0 106 96 1 51.20055
1117 3086 1 NA 58 1 33.86448
1124 3382 1 114 32 1 75.01164
1129 1427 1 280 247 1 30.86379
1135 762 1 112 290 0 61.80424
1138 3560 0 NA 57 1 34.98700
1147 3539 0 174 57 1 55.04175
1158 1152 1 126 243 0 69.94114
1161 3532 0 68 68 1 49.60438
1171 140 1 75 225 0 69.37714
1172 3516 0 80 57 1 43.55647
1175 853 1 205 219 1 59.40862
1179 3504 0 206 34 1 48.75838
1190 2475 1 118 32 1 36.49281
1199 1536 1 140 217 0 45.76044
1203 3441 0 119 13 1 57.37166
1211 3466 0 180 4 1 42.74333
1222 186 1 101 91 1 58.81725
1223 2055 1 68 20 1 53.49760
1226 276 1 NA 161 1 43.41410
1227 1076 1 142 80 0 53.30595
1232 3390 0 108 84 1 41.35524
1240 1684 1 154 200 0 60.95825
1246 3384 0 110 44 1 47.75359
1256 1212 1 137 67 1 35.49076
1261 3361 0 52 32 1 48.66256
1262 3243 0 NA 31 1 52.66804
1272 2970 0 126 65 1 49.86995
1279 3326 0 86 76 1 30.27515
1288 3313 0 NA 63 1 55.56742
1298 3293 0 168 152 1 52.15332
1308 1492 1 NA 77 1 41.60986
1314 3278 0 133 49 1 55.45243
1315 3249 0 NA 51 1 70.00411
1321 3242 0 133 39 1 43.94251
1324 3232 0 76 44 1 42.56810
1334 3225 0 106 63 1 44.56947
1335 3224 0 NA 161 1 56.94456
1340 2241 1 145 44 1 40.26010
1348 974 1 140 358 1 37.60712
1353 2882 1 94 42 1 48.36140
1358 1576 1 62 51 1 70.83641
1362 733 1 137 251 1 35.79192
1366 2635 0 98 41 1 62.62286
1374 3125 0 164 52 1 50.64750
1379 3173 0 NA 24 1 54.52704
1383 216 1 432 233 1 52.69268
1384 3112 0 101 67 1 52.72005
1394 797 1 157 267 1 56.77207
1398 3118 0 56 50 1 44.39699
1403 2999 0 90 76 1 29.55510
1404 2555 1 62 13 1 57.04038
1412 3034 0 102 25 1 44.62697
1421 3025 0 90 38 1 35.79740
1429 2991 0 194 52 1 40.71732
1432 2932 1 114 112 1 32.23272
1443 2963 0 67 26 1 41.09240
1453 2941 0 89 15 1 61.63997
1455 2890 0 57 227 1 37.05681
1464 2090 1 58 23 1 62.57906
1472 2081 1 NA 108 1 48.97741
1477 2924 0 68 12 1 61.99042
1485 2840 0 NA 14 1 72.77207
1492 904 1 131 58 1 61.29500
1495 2888 0 58 11 1 52.62423
1497 2893 0 90 81 0 49.76318
1507 2865 0 NA 27 1 52.91444
1515 2845 0 99 11 1 47.26352
1521 2189 0 55 9 1 50.20397
1523 1786 1 224 34 1 69.34702
1527 1080 1 157 141 1 41.16906
1531 2336 0 NA 20 1 59.16496
1538 790 1 210 65 1 36.07940
1542 2839 0 NA 29 1 34.59548
1552 2826 0 85 67 1 42.71321
1558 1235 1 113 96 1 63.63039
1563 2719 0 154 72 1 56.62971
1571 597 1 135 227 1 46.26420
1575 334 1 155 123 1 61.24298
1576 2614 0 101 67 1 38.62012
1584 2691 0 135 108 1 38.77070
1593 2647 0 104 39 1 56.69541
1602 999 1 63 172 0 58.95140
1606 2636 0 74 41 1 36.92266
1610 348 1 118 200 1 62.41478
1613 2648 0 44 51 1 34.60917
1617 1165 1 90 115 1 58.33539
1621 2620 0 87 47 1 50.18207
1625 2601 0 87 412 1 42.68583
1626 2445 0 177 78 1 34.37919
1628 2302 1 111 71 1 33.18275
1630 2577 0 106 67 1 38.38193
1632 1947 1 108 105 1 59.76181
1634 1874 1 81 NA 1 66.41205
1637 694 1 99 231 1 46.78987
1640 2500 0 139 24 1 56.07940
1648 837 1 322 308 1 41.37440
1652 2128 1 152 86 1 64.57221
1660 930 1 139 139 1 67.48802
1662 1690 1 231 121 1 44.82957
1666 2459 0 214 48 1 45.77139
1668 1435 1 33 63 1 32.95003
1674 940 1 139 89 1 41.22108
1678 2454 0 146 39 1 55.41684
1686 2452 0 71 35 1 47.98084
1689 2327 1 85 23 1 40.79124
1691 2307 0 120 40 1 56.97467
1692 2439 0 90 116 1 68.46270
1695 2434 0 154 380 0 78.43943
1703 737 1 91 121 1 39.85763
1707 2405 0 118 35 1 35.31006
1713 2370 0 108 48 1 31.44422
1719 2283 0 146 36 1 58.26420
1722 2371 0 55 80 1 51.48802
1730 2284 0 126 42 1 59.96988
1735 1674 1 128 188 0 74.52430
1736 2348 0 NA 129 1 52.36413
1742 1850 1 209 65 1 42.78713
1745 1303 1 77 45 1 34.87474
1750 1542 1 130 188 1 44.13963
1754 1084 1 146 121 1 46.38193
1758 2287 0 260 52 1 56.30938
1765 179 1 91 138 1 70.90760
1766 1191 1 179 16 1 55.39493
1768 1898 1 44 143 1 45.08419
1775 2010 1 117 102 1 26.27789
1778 2238 0 177 94 1 50.47228
1786 2194 0 85 22 1 38.39836
1792 1649 1 116 75 1 47.41958
1795 1447 1 NA 136 1 47.98084
1800 2020 0 114 77 1 38.31622
1804 2147 0 108 67 1 50.10815
1806 2105 0 111 44 1 35.08830
1811 1996 1 193 130 1 32.50376
1816 2102 0 56 38 1 56.15332
1823 2081 0 140 69 1 46.15469
1830 41 1 229 220 1 65.88364
1831 1673 1 169 97 1 33.94387
1835 2095 0 113 103 1 62.86105
1839 2087 0 195 75 1 48.56400
1846 2070 0 80 65 1 46.34908
1847 2077 0 118 70 1 38.85284
1849 1552 0 151 155 1 58.64750
1853 1067 1 242 107 1 48.93634
1855 799 1 242 177 0 67.57290
1859 2032 0 69 33 1 65.98494
1866 901 1 84 123 1 40.90075
1871 1785 1 153 84 0 50.24504
1874 1989 0 78 196 1 57.19644
1876 1971 0 133 88 0 60.53662
1883 875 1 194 102 0 35.35113
1885 1990 0 95 62 1 31.38125
1888 533 1 91 100 0 55.98631
1891 1969 0 93 73 1 52.72553
1894 1962 0 127 90 1 38.09172
1895 207 1 NA 234 1 58.17112
1897 1969 0 103 50 1 45.21013
1900 1940 0 104 43 1 37.79877
1905 1597 1 55 108 1 60.65982
1909 1899 0 75 22 1 35.53457
1910 1885 0 144 73 1 43.06639
1915 1885 0 64 31 1 56.39151
1917 1818 0 59 52 1 30.57358
1922 1822 0 113 24 1 61.18275
1927 1663 0 82 41 1 58.29979
1932 1608 0 100 39 1 62.33265
1937 1508 0 88 69 1 37.99863
1941 1457 0 149 186 1 33.15264
abs(age - copper) log(trig)
1 NA NA
3 NA NA
12 NA NA
16 NA NA
23 NA NA
29 NA NA
35 NA NA
42 NA NA
50 NA NA
57 NA NA
58 NA NA
70 NA NA
72 NA NA
84 NA NA
91 NA NA
102 NA NA
115 NA NA
118 NA NA
119 NA NA
134 NA NA
138 NA NA
150 NA NA
153 NA NA
155 NA NA
168 NA NA
180 NA NA
186 NA NA
187 NA NA
190 NA NA
200 NA NA
203 NA NA
215 NA NA
231 NA NA
241 NA NA
255 NA NA
259 NA NA
270 NA NA
272 NA NA
282 NA NA
290 NA NA
305 NA NA
309 NA NA
325 NA NA
340 NA NA
351 NA NA
360 NA NA
368 NA NA
375 NA NA
384 NA NA
388 NA NA
397 NA NA
407 NA NA
416 NA NA
419 NA NA
424 NA NA
430 NA NA
436 NA NA
447 NA NA
463 NA NA
470 NA NA
483 NA NA
497 NA NA
507 NA NA
510 NA NA
516 NA NA
522 NA NA
535 NA NA
545 NA NA
559 NA NA
563 NA NA
576 NA NA
587 NA NA
593 NA NA
608 NA NA
612 NA NA
617 NA NA
618 NA NA
620 NA NA
624 NA NA
635 NA NA
639 NA NA
649 NA NA
659 NA NA
674 NA NA
677 NA NA
688 NA NA
689 NA NA
691 NA NA
695 NA NA
699 NA NA
708 NA NA
711 NA NA
712 NA NA
727 NA NA
730 NA NA
731 NA NA
745 NA NA
749 NA NA
763 NA NA
776 NA NA
780 NA NA
792 NA NA
804 NA NA
805 NA NA
815 NA NA
825 NA NA
830 NA NA
841 NA NA
849 NA NA
859 NA NA
866 NA NA
874 NA NA
885 NA NA
890 NA NA
901 NA NA
913 NA NA
924 NA NA
929 NA NA
936 NA NA
938 NA NA
946 NA NA
947 NA NA
957 NA NA
959 NA NA
960 NA NA
969 NA NA
974 NA NA
985 NA NA
990 NA NA
1002 NA NA
1008 NA NA
1012 NA NA
1018 NA NA
1027 NA NA
1039 NA NA
1049 NA NA
1058 NA NA
1070 NA NA
1076 NA NA
1082 NA NA
1094 NA NA
1097 NA NA
1106 NA NA
1109 NA NA
1113 NA NA
1117 NA NA
1124 NA NA
1129 NA NA
1135 NA NA
1138 NA NA
1147 NA NA
1158 NA NA
1161 NA NA
1171 NA NA
1172 NA NA
1175 NA NA
1179 NA NA
1190 NA NA
1199 NA NA
1203 NA NA
1211 NA NA
1222 NA NA
1223 NA NA
1226 NA NA
1227 NA NA
1232 NA NA
1240 NA NA
1246 NA NA
1256 NA NA
1261 NA NA
1262 NA NA
1272 NA NA
1279 NA NA
1288 NA NA
1298 NA NA
1308 NA NA
1314 NA NA
1315 NA NA
1321 NA NA
1324 NA NA
1334 NA NA
1335 NA NA
1340 NA NA
1348 NA NA
1353 NA NA
1358 NA NA
1362 NA NA
1366 NA NA
1374 NA NA
1379 NA NA
1383 NA NA
1384 NA NA
1394 NA NA
1398 NA NA
1403 NA NA
1404 NA NA
1412 NA NA
1421 NA NA
1429 NA NA
1432 NA NA
1443 NA NA
1453 NA NA
1455 NA NA
1464 NA NA
1472 NA NA
1477 NA NA
1485 NA NA
1492 NA NA
1495 NA NA
1497 NA NA
1507 NA NA
1515 NA NA
1521 NA NA
1523 NA NA
1527 NA NA
1531 NA NA
1538 NA NA
1542 NA NA
1552 NA NA
1558 NA NA
1563 NA NA
1571 NA NA
1575 NA NA
1576 NA NA
1584 NA NA
1593 NA NA
1602 NA NA
1606 NA NA
1610 NA NA
1613 NA NA
1617 NA NA
1621 NA NA
1625 NA NA
1626 NA NA
1628 NA NA
1630 NA NA
1632 NA NA
1634 NA NA
1637 NA NA
1640 NA NA
1648 NA NA
1652 NA NA
1660 NA NA
1662 NA NA
1666 NA NA
1668 NA NA
1674 NA NA
1678 NA NA
1686 NA NA
1689 NA NA
1691 NA NA
1692 NA NA
1695 NA NA
1703 NA NA
1707 NA NA
1713 NA NA
1719 NA NA
1722 NA NA
1730 NA NA
1735 NA NA
1736 NA NA
1742 NA NA
1745 NA NA
1750 NA NA
1754 NA NA
1758 NA NA
1765 NA NA
1766 NA NA
1768 NA NA
1775 NA NA
1778 NA NA
1786 NA NA
1792 NA NA
1795 NA NA
1800 NA NA
1804 NA NA
1806 NA NA
1811 NA NA
1816 NA NA
1823 NA NA
1830 NA NA
1831 NA NA
1835 NA NA
1839 NA NA
1846 NA NA
1847 NA NA
1849 NA NA
1853 NA NA
1855 NA NA
1859 NA NA
1866 NA NA
1871 NA NA
1874 NA NA
1876 NA NA
1883 NA NA
1885 NA NA
1888 NA NA
1891 NA NA
1894 NA NA
1895 NA NA
1897 NA NA
1900 NA NA
1905 NA NA
1909 NA NA
1910 NA NA
1915 NA NA
1917 NA NA
1922 NA NA
1927 NA NA
1932 NA NA
1937 NA NA
1941 NA NA
$m3b$spM_lvlone
center scale
futime 2341.641026 1301.272424
status != "censored" NA NA
trig 124.702128 65.148639
copper 97.648387 85.613920
sexfemale NA NA
age 50.019007 10.581261
abs(age - copper) 61.603707 76.189619
log(trig) 4.720406 0.447384
$m3b$mu_reg_norm
[1] 0
$m3b$tau_reg_norm
[1] 1e-04
$m3b$shape_tau_norm
[1] 0.01
$m3b$rate_tau_norm
[1] 0.01
$m3b$mu_reg_surv
[1] 0
$m3b$tau_reg_surv
[1] 0.001
$m3b$group_center
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[26] 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[51] 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[76] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4
[101] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5
[126] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[151] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
[176] 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
[201] 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
[226] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9
[251] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 10 10 10 10 10
[276] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[301] 10 10 10 10 10 10 10 10 10 10 10 10
$m3b$shape_diag_RinvD
[1] "0.01"
$m3b$rate_diag_RinvD
[1] "0.001"
$m3b$cens_Srv_ftm_stts_cn
1 3 12 16 23 29 35 42 50 57 58 70 72 84 91 102
0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1
115 118 119 134 138 150 153 155 168 180 186 187 190 200 203 215
0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1
231 241 255 259 270 272 282 290 305 309 325 340 351 360 368 375
0 1 0 1 0 0 0 1 0 1 1 0 1 0 1 1
384 388 397 407 416 419 424 430 436 447 463 470 483 497 507 510
0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0
516 522 535 545 559 563 576 587 593 608 612 617 618 620 624 635
1 0 0 1 0 0 1 1 1 0 0 0 0 0 1 0
639 649 659 674 677 688 689 691 695 699 708 711 712 727 730 731
0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1
745 749 763 776 780 792 804 805 815 825 830 841 849 859 866 874
0 1 1 0 1 1 0 0 0 0 1 0 1 0 0 0
885 890 901 913 924 929 936 938 946 947 957 959 960 969 974 985
0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0
990 1002 1008 1012 1018 1027 1039 1049 1058 1070 1076 1082 1094 1097 1106 1109
1 0 0 1 0 1 1 1 1 0 0 1 1 0 0 0
1113 1117 1124 1129 1135 1138 1147 1158 1161 1171 1172 1175 1179 1190 1199 1203
1 0 0 0 0 1 1 0 1 0 1 0 1 0 0 1
1211 1222 1223 1226 1227 1232 1240 1246 1256 1261 1262 1272 1279 1288 1298 1308
1 0 0 0 0 1 0 1 0 1 1 1 1 1 1 0
1314 1315 1321 1324 1334 1335 1340 1348 1353 1358 1362 1366 1374 1379 1383 1384
1 1 1 1 1 1 0 0 0 0 0 1 1 1 0 1
1394 1398 1403 1404 1412 1421 1429 1432 1443 1453 1455 1464 1472 1477 1485 1492
0 1 1 0 1 1 1 0 1 1 1 0 0 1 1 0
1495 1497 1507 1515 1521 1523 1527 1531 1538 1542 1552 1558 1563 1571 1575 1576
1 1 1 1 1 0 0 1 0 1 1 0 1 0 0 1
1584 1593 1602 1606 1610 1613 1617 1621 1625 1626 1628 1630 1632 1634 1637 1640
1 1 0 1 0 1 0 1 1 1 0 1 0 0 0 1
1648 1652 1660 1662 1666 1668 1674 1678 1686 1689 1691 1692 1695 1703 1707 1713
0 0 0 0 1 0 0 1 1 0 1 1 1 0 1 1
1719 1722 1730 1735 1736 1742 1745 1750 1754 1758 1765 1766 1768 1775 1778 1786
1 1 1 0 1 0 0 0 0 1 0 0 0 0 1 1
1792 1795 1800 1804 1806 1811 1816 1823 1830 1831 1835 1839 1846 1847 1849 1853
0 0 1 1 1 0 1 1 0 0 1 1 1 1 1 0
1855 1859 1866 1871 1874 1876 1883 1885 1888 1891 1894 1895 1897 1900 1905 1909
0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 1
1910 1915 1917 1922 1927 1932 1937 1941
1 1 1 1 1 1 1 1
$m3b$Srv_ftm_stts_cn
[1] 400 NA 1012 1925 1505 2503 NA 2466 2400 51 3762 304 NA 1217 3584
[16] NA 769 132 NA 1356 3657 673 264 4079 NA 1444 77 549 5074 321
[31] 3839 NA 3170 NA 2847 NA 223 3244 2297 NA 1350 NA NA 3428 NA
[46] 2256 NA NA 708 2598 3853 2386 1000 1434 1360 1847 3282 NA 2224 NA
[61] NA 3090 859 1487 NA 4191 2769 NA 1170 3683 NA NA NA 1827 1191
[76] 71 326 1690 NA 890 2540 3574 NA NA 3358 1657 198 3076 1741 2689
[91] 460 389 NA 750 137 NA 620 NA NA 552 NA NA 110 3086 3092
[106] 3222 NA 2583 NA 2044 2350 3445 951 3395 NA NA 1083 2288 515 2033
[121] 191 NA 971 3903 2468 824 NA 1037 NA 1411 850 NA 2796 NA NA
[136] NA NA 1297 2357 NA NA 2419 786 945 NA 3086 3382 1427 762 NA
[151] NA 1152 NA 140 NA 853 NA 2475 1536 NA NA 186 2055 276 1076
[166] NA 1684 NA 1212 NA NA NA NA NA NA 1492 NA NA NA NA
[181] NA NA 2241 974 2882 1576 733 NA NA NA 216 NA 797 NA NA
[196] 2555 NA NA NA 2932 NA NA NA 2090 2081 NA NA 904 NA NA
[211] NA NA NA 1786 1080 NA 790 NA NA 1235 NA 597 334 NA NA
[226] NA 999 NA 348 NA 1165 NA NA NA 2302 NA 1947 1874 694 NA
[241] 837 2128 930 1690 NA 1435 940 NA NA 2327 NA NA NA 737 NA
[256] NA NA NA NA 1674 NA 1850 1303 1542 1084 NA 179 1191 1898 2010
[271] NA NA 1649 1447 NA NA NA 1996 NA NA 41 1673 NA NA NA
[286] NA NA 1067 799 NA 901 1785 NA NA 875 NA 533 NA NA 207
[301] NA NA 1597 NA NA NA NA NA NA NA NA NA
jagsmodel remains the same
Code
lapply(models, "[[", "jagsmodel")
Output
$m0a
model {
# Weibull survival model for Srv_ftm_stts_cn ------------------------------------
for (i in 1:312) {
Srv_ftm_stts_cn[i] ~ dgen.gamma(1, rate_Srv_ftm_stts_cn[i], shape_Srv_ftm_stts_cn)
cens_Srv_ftm_stts_cn[i] ~ dinterval(Srv_ftm_stts_cn[i], M_lvlone[i, 1])
log(rate_Srv_ftm_stts_cn[i]) <- -(M_lvlone[i, 3] * beta[1])
}
# Priors for the model for Srv_ftm_stts_cn
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
shape_Srv_ftm_stts_cn ~ dexp(0.01)
}
$m1a
model {
# Weibull survival model for Srv_ftm_stts_cn ------------------------------------
for (i in 1:312) {
Srv_ftm_stts_cn[i] ~ dgen.gamma(1, rate_Srv_ftm_stts_cn[i], shape_Srv_ftm_stts_cn)
cens_Srv_ftm_stts_cn[i] ~ dinterval(Srv_ftm_stts_cn[i], M_lvlone[i, 1])
log(rate_Srv_ftm_stts_cn[i]) <- -(M_lvlone[i, 3] * beta[1] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[2] +
M_lvlone[i, 5] * beta[3])
}
# Priors for the model for Srv_ftm_stts_cn
for (k in 1:3) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
shape_Srv_ftm_stts_cn ~ dexp(0.01)
}
$m1b
model {
# Weibull survival model for Srv_ftm_stts_cn ------------------------------------
for (i in 1:312) {
Srv_ftm_stts_cn[i] ~ dgen.gamma(1, rate_Srv_ftm_stts_cn[i], shape_Srv_ftm_stts_cn)
cens_Srv_ftm_stts_cn[i] ~ dinterval(Srv_ftm_stts_cn[i], M_lvlone[i, 1])
log(rate_Srv_ftm_stts_cn[i]) <- -(M_lvlone[i, 3] * beta[1] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[2] +
M_lvlone[i, 5] * beta[3])
}
# Priors for the model for Srv_ftm_stts_cn
for (k in 1:3) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
shape_Srv_ftm_stts_cn ~ dexp(0.01)
}
$m2a
model {
# Weibull survival model for Srv_ftm_stts_cn ------------------------------------
for (i in 1:312) {
Srv_ftm_stts_cn[i] ~ dgen.gamma(1, rate_Srv_ftm_stts_cn[i], shape_Srv_ftm_stts_cn)
cens_Srv_ftm_stts_cn[i] ~ dinterval(Srv_ftm_stts_cn[i], M_lvlone[i, 1])
log(rate_Srv_ftm_stts_cn[i]) <- -(M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2])
}
# Priors for the model for Srv_ftm_stts_cn
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
shape_Srv_ftm_stts_cn ~ dexp(0.01)
# Normal model for copper -------------------------------------------------------
for (i in 1:312) {
M_lvlone[i, 3] ~ dnorm(mu_copper[i], tau_copper)
mu_copper[i] <- M_lvlone[i, 4] * alpha[1]
}
# Priors for the model for copper
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_copper ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_copper <- sqrt(1/tau_copper)
}
$m3a
model {
# Weibull survival model for Srv_ftm_stts_cn ------------------------------------
for (i in 1:312) {
Srv_ftm_stts_cn[i] ~ dgen.gamma(1, rate_Srv_ftm_stts_cn[i], shape_Srv_ftm_stts_cn)
cens_Srv_ftm_stts_cn[i] ~ dinterval(Srv_ftm_stts_cn[i], M_lvlone[i, 1])
log(rate_Srv_ftm_stts_cn[i]) <- -(M_lvlone[i, 5] * beta[1] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[2] +
M_lvlone[i, 6] * beta[3] +
(M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[4] +
(M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[5] +
(M_lvlone[i, 9] - spM_lvlone[9, 1])/spM_lvlone[9, 2] * beta[6])
}
# Priors for the model for Srv_ftm_stts_cn
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
shape_Srv_ftm_stts_cn ~ dexp(0.01)
# Normal model for trig ---------------------------------------------------------
for (i in 1:312) {
M_lvlone[i, 3] ~ dnorm(mu_trig[i], tau_trig)T(1e-04, )
mu_trig[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] +
(M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * alpha[4]
M_lvlone[i, 9] <- log(M_lvlone[i, 3])
}
# Priors for the model for trig
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_trig ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_trig <- sqrt(1/tau_trig)
# Normal model for copper -------------------------------------------------------
for (i in 1:312) {
M_lvlone[i, 4] ~ dnorm(mu_copper[i], tau_copper)
mu_copper[i] <- M_lvlone[i, 5] * alpha[5] + M_lvlone[i, 6] * alpha[6] +
(M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * alpha[7]
M_lvlone[i, 8] <- abs(M_lvlone[i, 7] - M_lvlone[i, 4])
}
# Priors for the model for copper
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_copper ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_copper <- sqrt(1/tau_copper)
}
$m3b
model {
# Weibull survival model for Srv_ftm_stts_cn ------------------------------------
for (i in 1:312) {
Srv_ftm_stts_cn[i] ~ dgen.gamma(1, rate_Srv_ftm_stts_cn[i], shape_Srv_ftm_stts_cn)
cens_Srv_ftm_stts_cn[i] ~ dinterval(Srv_ftm_stts_cn[i], M_lvlone[i, 1])
log(rate_Srv_ftm_stts_cn[i]) <- -(b_Srv_ftm_stts_cn_center[group_center[i], 1] +
beta[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[3] * M_lvlone[i, 5] +
beta[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] +
beta[5] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] +
beta[6] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2])
}
for (ii in 1:10) {
b_Srv_ftm_stts_cn_center[ii, 1:1] ~ dnorm(mu_b_Srv_ftm_stts_cn_center[ii, ], invD_Srv_ftm_stts_cn_center[ , ])
mu_b_Srv_ftm_stts_cn_center[ii, 1] <- M_center[ii, 1] * beta[1]
}
# Priors for the model for Srv_ftm_stts_cn
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
shape_Srv_ftm_stts_cn ~ dexp(0.01)
invD_Srv_ftm_stts_cn_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_Srv_ftm_stts_cn_center[1, 1] <- 1 / (invD_Srv_ftm_stts_cn_center[1, 1])
# Normal mixed effects model for trig -------------------------------------------
for (i in 1:312) {
M_lvlone[i, 3] ~ dnorm(mu_trig[i], tau_trig)T(1e-04, )
mu_trig[i] <- b_trig_center[group_center[i], 1] +
alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[3] * M_lvlone[i, 5] +
alpha[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
M_lvlone[i, 8] <- log(M_lvlone[i, 3])
}
for (ii in 1:10) {
b_trig_center[ii, 1:1] ~ dnorm(mu_b_trig_center[ii, ], invD_trig_center[ , ])
mu_b_trig_center[ii, 1] <- M_center[ii, 1] * alpha[1]
}
# Priors for the model for trig
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_trig ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_trig <- sqrt(1/tau_trig)
invD_trig_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_trig_center[1, 1] <- 1 / (invD_trig_center[1, 1])
# Normal mixed effects model for copper -----------------------------------------
for (i in 1:312) {
M_lvlone[i, 4] ~ dnorm(mu_copper[i], tau_copper)
mu_copper[i] <- b_copper_center[group_center[i], 1] + alpha[6] * M_lvlone[i, 5] +
alpha[7] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
M_lvlone[i, 7] <- abs(M_lvlone[i, 6] - M_lvlone[i, 4])
}
for (ii in 1:10) {
b_copper_center[ii, 1:1] ~ dnorm(mu_b_copper_center[ii, ], invD_copper_center[ , ])
mu_b_copper_center[ii, 1] <- M_center[ii, 1] * alpha[5]
}
# Priors for the model for copper
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_copper ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_copper <- sqrt(1/tau_copper)
invD_copper_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_copper_center[1, 1] <- 1 / (invD_copper_center[1, 1])
}
GRcrit and MCerror give same result
Code
lapply(models0, GR_crit, multivariate = FALSE)
Output
$m0a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
shape_Srv_ftm_stts_cn NaN NaN
$m1a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
age NaN NaN
sexfemale NaN NaN
shape_Srv_ftm_stts_cn NaN NaN
$m1b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
age NaN NaN
sexfemale NaN NaN
shape_Srv_ftm_stts_cn NaN NaN
$m2a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
copper NaN NaN
shape_Srv_ftm_stts_cn NaN NaN
$m3a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
copper NaN NaN
sexfemale NaN NaN
age NaN NaN
abs(age - copper) NaN NaN
log(trig) NaN NaN
shape_Srv_ftm_stts_cn NaN NaN
$m3b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
copper NaN NaN
sexfemale NaN NaN
age NaN NaN
abs(age - copper) NaN NaN
log(trig) NaN NaN
shape_Srv_ftm_stts_cn NaN NaN
D_Srv_ftm_stts_cn_center[1,1] NaN NaN
Code
lapply(models0, MC_error)
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
shape_Srv_ftm_stts_cn 0 0 0 NaN
$m1a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
age 0 0 0 NaN
sexfemale 0 0 0 NaN
shape_Srv_ftm_stts_cn 0 0 0 NaN
$m1b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
age 0 0 0 NaN
sexfemale 0 0 0 NaN
shape_Srv_ftm_stts_cn 0 0 0 NaN
$m2a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
copper 0 0 0 NaN
shape_Srv_ftm_stts_cn 0 0 0 NaN
$m3a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
copper 0 0 0 NaN
sexfemale 0 0 0 NaN
age 0 0 0 NaN
abs(age - copper) 0 0 0 NaN
log(trig) 0 0 0 NaN
shape_Srv_ftm_stts_cn 0 0 0 NaN
$m3b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
copper 0 0 0 NaN
sexfemale 0 0 0 NaN
age 0 0 0 NaN
abs(age - copper) 0 0 0 NaN
log(trig) 0 0 0 NaN
shape_Srv_ftm_stts_cn 0 0 0 NaN
D_Srv_ftm_stts_cn_center[1,1] 0 0 0 NaN
summary output remained the same
Code
lapply(models0, print)
Output
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ 1,
data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian weibull survival model for "Surv(futime, status != "censored")"
Coefficients:
(Intercept)
0
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ age +
sex, data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian weibull survival model for "Surv(futime, status != "censored")"
Coefficients:
(Intercept) age sexfemale
0 0 0
Call:
survreg_imp(formula = Surv(futime, I(status != "censored")) ~
age + sex, data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian weibull survival model for "Surv(futime, I(status != "censored"))"
Coefficients:
(Intercept) age sexfemale
0 0 0
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ copper,
data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian weibull survival model for "Surv(futime, status != "censored")"
Coefficients:
(Intercept) copper
0 0
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04,
NA)))
Bayesian weibull survival model for "Surv(futime, status != "censored")"
Coefficients:
(Intercept) copper sexfemale age
0 0 0 0
abs(age - copper) log(trig)
0 0
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig) + (1 | center),
data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(trig = c(1e-04, NA)))
Bayesian weibull survival model for "Surv(futime, status != "censored")"
Coefficients:
(Intercept) copper sexfemale age
0 0 0 0
abs(age - copper) log(trig)
0 0
$m0a
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ 1,
data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian weibull survival model for "Surv(futime, status != "censored")"
Coefficients:
(Intercept)
0
$m1a
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ age +
sex, data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian weibull survival model for "Surv(futime, status != "censored")"
Coefficients:
(Intercept) age sexfemale
0 0 0
$m1b
Call:
survreg_imp(formula = Surv(futime, I(status != "censored")) ~
age + sex, data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian weibull survival model for "Surv(futime, I(status != "censored"))"
Coefficients:
(Intercept) age sexfemale
0 0 0
$m2a
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ copper,
data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian weibull survival model for "Surv(futime, status != "censored")"
Coefficients:
(Intercept) copper
0 0
$m3a
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04,
NA)))
Bayesian weibull survival model for "Surv(futime, status != "censored")"
Coefficients:
(Intercept) copper sexfemale age
0 0 0 0
abs(age - copper) log(trig)
0 0
$m3b
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig) + (1 | center),
data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(trig = c(1e-04, NA)))
Bayesian weibull survival model for "Surv(futime, status != "censored")"
Coefficients:
(Intercept) copper sexfemale age
0 0 0 0
abs(age - copper) log(trig)
0 0
Code
lapply(models0, coef)
Output
$m0a
$m0a$`Surv(futime, status != "censored")`
(Intercept) shape_Srv_ftm_stts_cn
0 0
$m1a
$m1a$`Surv(futime, status != "censored")`
(Intercept) age sexfemale
0 0 0
shape_Srv_ftm_stts_cn
0
$m1b
$m1b$`Surv(futime, I(status != "censored"))`
(Intercept) age sexfemale
0 0 0
shape_Srv_ftm_stts_cn
0
$m2a
$m2a$`Surv(futime, status != "censored")`
(Intercept) copper shape_Srv_ftm_stts_cn
0 0 0
$m3a
$m3a$`Surv(futime, status != "censored")`
(Intercept) copper sexfemale
0 0 0
age abs(age - copper) log(trig)
0 0 0
shape_Srv_ftm_stts_cn
0
$m3b
$m3b$`Surv(futime, status != "censored")`
(Intercept) copper
0 0
sexfemale age
0 0
abs(age - copper) log(trig)
0 0
shape_Srv_ftm_stts_cn D_Srv_ftm_stts_cn_center[1,1]
0 0
Code
lapply(models0, confint)
Output
$m0a
$m0a$`Surv(futime, status != "censored")`
2.5% 97.5%
(Intercept) 0 0
shape_Srv_ftm_stts_cn 0 0
$m1a
$m1a$`Surv(futime, status != "censored")`
2.5% 97.5%
(Intercept) 0 0
age 0 0
sexfemale 0 0
shape_Srv_ftm_stts_cn 0 0
$m1b
$m1b$`Surv(futime, I(status != "censored"))`
2.5% 97.5%
(Intercept) 0 0
age 0 0
sexfemale 0 0
shape_Srv_ftm_stts_cn 0 0
$m2a
$m2a$`Surv(futime, status != "censored")`
2.5% 97.5%
(Intercept) 0 0
copper 0 0
shape_Srv_ftm_stts_cn 0 0
$m3a
$m3a$`Surv(futime, status != "censored")`
2.5% 97.5%
(Intercept) 0 0
copper 0 0
sexfemale 0 0
age 0 0
abs(age - copper) 0 0
log(trig) 0 0
shape_Srv_ftm_stts_cn 0 0
$m3b
$m3b$`Surv(futime, status != "censored")`
2.5% 97.5%
(Intercept) 0 0
copper 0 0
sexfemale 0 0
age 0 0
abs(age - copper) 0 0
log(trig) 0 0
shape_Srv_ftm_stts_cn 0 0
D_Srv_ftm_stts_cn_center[1,1] 0 0
Code
lapply(models0, summary)
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a
Bayesian weibull survival model fitted with JointAI
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ 1,
data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of the shape of the Weibull distribution:
Mean SD 2.5% 97.5% GR-crit MCE/SD
shape_Srv_ftm_stts_cn 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 312
$m1a
Bayesian weibull survival model fitted with JointAI
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ age +
sex, data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
Posterior summary of the shape of the Weibull distribution:
Mean SD 2.5% 97.5% GR-crit MCE/SD
shape_Srv_ftm_stts_cn 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 312
$m1b
Bayesian weibull survival model fitted with JointAI
Call:
survreg_imp(formula = Surv(futime, I(status != "censored")) ~
age + sex, data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
Posterior summary of the shape of the Weibull distribution:
Mean SD 2.5% 97.5% GR-crit MCE/SD
shape_Srv_ftm_stts_cn 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 312
$m2a
Bayesian weibull survival model fitted with JointAI
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ copper,
data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
copper 0 0 0 0 0 NaN NaN
Posterior summary of the shape of the Weibull distribution:
Mean SD 2.5% 97.5% GR-crit MCE/SD
shape_Srv_ftm_stts_cn 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 312
$m3a
Bayesian weibull survival model fitted with JointAI
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04,
NA)))
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
copper 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
abs(age - copper) 0 0 0 0 0 NaN NaN
log(trig) 0 0 0 0 0 NaN NaN
Posterior summary of the shape of the Weibull distribution:
Mean SD 2.5% 97.5% GR-crit MCE/SD
shape_Srv_ftm_stts_cn 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 312
$m3b
Bayesian weibull survival model fitted with JointAI
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig) + (1 | center),
data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(trig = c(1e-04, NA)))
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
copper 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
abs(age - copper) 0 0 0 0 0 NaN NaN
log(trig) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_Srv_ftm_stts_cn_center[1,1] 0 0 0 0 NaN NaN
Posterior summary of the shape of the Weibull distribution:
Mean SD 2.5% 97.5% GR-crit MCE/SD
shape_Srv_ftm_stts_cn 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 312
Number of groups:
- center: 10
Code
lapply(models0, function(x) coef(summary(x)))
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a
$m0a$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m1a
$m1a$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
$m1b
$m1b$`Surv(futime, I(status != "censored"))`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
$m2a
$m2a$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
copper 0 0 0 0 0 NaN NaN
$m3a
$m3a$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
copper 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
abs(age - copper) 0 0 0 0 0 NaN NaN
log(trig) 0 0 0 0 0 NaN NaN
$m3b
$m3b$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
copper 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
abs(age - copper) 0 0 0 0 0 NaN NaN
log(trig) 0 0 0 0 0 NaN NaN
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JointAI documentation built on April 27, 2023, 5:15 p.m.
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data_list remains the same
Code
lapply(models, "[[", "data_list")
Output
$m0a
$m0a$M_lvlone
futime status != "censored" (Intercept)
1 400 1 1
3 5169 0 1
12 1012 1 1
16 1925 1 1
23 1505 1 1
29 2503 1 1
35 2501 0 1
42 2466 1 1
50 2400 1 1
57 51 1 1
58 3762 1 1
70 304 1 1
72 4247 0 1
84 1217 1 1
91 3584 1 1
102 4345 0 1
115 769 1 1
118 132 1 1
119 4901 0 1
134 1356 1 1
138 3657 1 1
150 673 1 1
153 264 1 1
155 4079 1 1
168 4796 0 1
180 1444 1 1
186 77 1 1
187 549 1 1
190 5074 1 1
200 321 1 1
203 3839 1 1
215 5192 0 1
231 3170 1 1
241 4602 0 1
255 2847 1 1
259 4281 0 1
270 223 1 1
272 3244 1 1
282 2297 1 1
290 5136 0 1
305 1350 1 1
309 5122 0 1
325 5225 0 1
340 3428 1 1
351 4694 0 1
360 2256 1 1
368 3245 0 1
375 5096 0 1
384 708 1 1
388 2598 1 1
397 3853 1 1
407 2386 1 1
416 1000 1 1
419 1434 1 1
424 1360 1 1
430 1847 1 1
436 3282 1 1
447 5128 0 1
463 2224 1 1
470 5034 0 1
483 4925 0 1
497 3090 1 1
507 859 1 1
510 1487 1 1
516 4842 0 1
522 4191 1 1
535 2769 1 1
545 4708 0 1
559 1170 1 1
563 3683 1 1
576 4865 0 1
587 4853 0 1
593 4859 0 1
608 1827 1 1
612 1191 1 1
617 71 1 1
618 326 1 1
620 1690 1 1
624 4376 0 1
635 890 1 1
639 2540 1 1
649 3574 1 1
659 4719 0 1
674 4701 0 1
677 3358 1 1
688 1657 1 1
689 198 1 1
691 3076 1 1
695 1741 1 1
699 2689 1 1
708 460 1 1
711 389 1 1
712 4583 0 1
727 750 1 1
730 137 1 1
731 4520 0 1
745 620 1 1
749 4492 0 1
763 4489 0 1
776 552 1 1
780 4250 0 1
792 3770 0 1
804 110 1 1
805 3086 1 1
815 3092 1 1
825 3222 1 1
830 4058 0 1
841 2583 1 1
849 3173 0 1
859 2044 1 1
866 2350 1 1
874 3445 1 1
885 951 1 1
890 3395 1 1
901 4091 0 1
913 4015 0 1
924 1083 1 1
929 2288 1 1
936 515 1 1
938 2033 1 1
946 191 1 1
947 3966 0 1
957 971 1 1
959 3903 1 1
960 2468 1 1
969 824 1 1
974 3924 0 1
985 1037 1 1
990 3908 0 1
1002 1411 1 1
1008 850 1 1
1012 3613 0 1
1018 2796 1 1
1027 3818 0 1
1039 3819 0 1
1049 3767 0 1
1058 3659 0 1
1070 1297 1 1
1076 2357 1 1
1082 3728 0 1
1094 3719 0 1
1097 2419 1 1
1106 786 1 1
1109 945 1 1
1113 3645 0 1
1117 3086 1 1
1124 3382 1 1
1129 1427 1 1
1135 762 1 1
1138 3560 0 1
1147 3539 0 1
1158 1152 1 1
1161 3532 0 1
1171 140 1 1
1172 3516 0 1
1175 853 1 1
1179 3504 0 1
1190 2475 1 1
1199 1536 1 1
1203 3441 0 1
1211 3466 0 1
1222 186 1 1
1223 2055 1 1
1226 276 1 1
1227 1076 1 1
1232 3390 0 1
1240 1684 1 1
1246 3384 0 1
1256 1212 1 1
1261 3361 0 1
1262 3243 0 1
1272 2970 0 1
1279 3326 0 1
1288 3313 0 1
1298 3293 0 1
1308 1492 1 1
1314 3278 0 1
1315 3249 0 1
1321 3242 0 1
1324 3232 0 1
1334 3225 0 1
1335 3224 0 1
1340 2241 1 1
1348 974 1 1
1353 2882 1 1
1358 1576 1 1
1362 733 1 1
1366 2635 0 1
1374 3125 0 1
1379 3173 0 1
1383 216 1 1
1384 3112 0 1
1394 797 1 1
1398 3118 0 1
1403 2999 0 1
1404 2555 1 1
1412 3034 0 1
1421 3025 0 1
1429 2991 0 1
1432 2932 1 1
1443 2963 0 1
1453 2941 0 1
1455 2890 0 1
1464 2090 1 1
1472 2081 1 1
1477 2924 0 1
1485 2840 0 1
1492 904 1 1
1495 2888 0 1
1497 2893 0 1
1507 2865 0 1
1515 2845 0 1
1521 2189 0 1
1523 1786 1 1
1527 1080 1 1
1531 2336 0 1
1538 790 1 1
1542 2839 0 1
1552 2826 0 1
1558 1235 1 1
1563 2719 0 1
1571 597 1 1
1575 334 1 1
1576 2614 0 1
1584 2691 0 1
1593 2647 0 1
1602 999 1 1
1606 2636 0 1
1610 348 1 1
1613 2648 0 1
1617 1165 1 1
1621 2620 0 1
1625 2601 0 1
1626 2445 0 1
1628 2302 1 1
1630 2577 0 1
1632 1947 1 1
1634 1874 1 1
1637 694 1 1
1640 2500 0 1
1648 837 1 1
1652 2128 1 1
1660 930 1 1
1662 1690 1 1
1666 2459 0 1
1668 1435 1 1
1674 940 1 1
1678 2454 0 1
1686 2452 0 1
1689 2327 1 1
1691 2307 0 1
1692 2439 0 1
1695 2434 0 1
1703 737 1 1
1707 2405 0 1
1713 2370 0 1
1719 2283 0 1
1722 2371 0 1
1730 2284 0 1
1735 1674 1 1
1736 2348 0 1
1742 1850 1 1
1745 1303 1 1
1750 1542 1 1
1754 1084 1 1
1758 2287 0 1
1765 179 1 1
1766 1191 1 1
1768 1898 1 1
1775 2010 1 1
1778 2238 0 1
1786 2194 0 1
1792 1649 1 1
1795 1447 1 1
1800 2020 0 1
1804 2147 0 1
1806 2105 0 1
1811 1996 1 1
1816 2102 0 1
1823 2081 0 1
1830 41 1 1
1831 1673 1 1
1835 2095 0 1
1839 2087 0 1
1846 2070 0 1
1847 2077 0 1
1849 1552 0 1
1853 1067 1 1
1855 799 1 1
1859 2032 0 1
1866 901 1 1
1871 1785 1 1
1874 1989 0 1
1876 1971 0 1
1883 875 1 1
1885 1990 0 1
1888 533 1 1
1891 1969 0 1
1894 1962 0 1
1895 207 1 1
1897 1969 0 1
1900 1940 0 1
1905 1597 1 1
1909 1899 0 1
1910 1885 0 1
1915 1885 0 1
1917 1818 0 1
1922 1822 0 1
1927 1663 0 1
1932 1608 0 1
1937 1508 0 1
1941 1457 0 1
$m0a$mu_reg_surv
[1] 0
$m0a$tau_reg_surv
[1] 0.001
$m0a$cens_Srv_ftm_stts_cn
1 3 12 16 23 29 35 42 50 57 58 70 72 84 91 102
0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1
115 118 119 134 138 150 153 155 168 180 186 187 190 200 203 215
0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1
231 241 255 259 270 272 282 290 305 309 325 340 351 360 368 375
0 1 0 1 0 0 0 1 0 1 1 0 1 0 1 1
384 388 397 407 416 419 424 430 436 447 463 470 483 497 507 510
0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0
516 522 535 545 559 563 576 587 593 608 612 617 618 620 624 635
1 0 0 1 0 0 1 1 1 0 0 0 0 0 1 0
639 649 659 674 677 688 689 691 695 699 708 711 712 727 730 731
0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1
745 749 763 776 780 792 804 805 815 825 830 841 849 859 866 874
0 1 1 0 1 1 0 0 0 0 1 0 1 0 0 0
885 890 901 913 924 929 936 938 946 947 957 959 960 969 974 985
0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0
990 1002 1008 1012 1018 1027 1039 1049 1058 1070 1076 1082 1094 1097 1106 1109
1 0 0 1 0 1 1 1 1 0 0 1 1 0 0 0
1113 1117 1124 1129 1135 1138 1147 1158 1161 1171 1172 1175 1179 1190 1199 1203
1 0 0 0 0 1 1 0 1 0 1 0 1 0 0 1
1211 1222 1223 1226 1227 1232 1240 1246 1256 1261 1262 1272 1279 1288 1298 1308
1 0 0 0 0 1 0 1 0 1 1 1 1 1 1 0
1314 1315 1321 1324 1334 1335 1340 1348 1353 1358 1362 1366 1374 1379 1383 1384
1 1 1 1 1 1 0 0 0 0 0 1 1 1 0 1
1394 1398 1403 1404 1412 1421 1429 1432 1443 1453 1455 1464 1472 1477 1485 1492
0 1 1 0 1 1 1 0 1 1 1 0 0 1 1 0
1495 1497 1507 1515 1521 1523 1527 1531 1538 1542 1552 1558 1563 1571 1575 1576
1 1 1 1 1 0 0 1 0 1 1 0 1 0 0 1
1584 1593 1602 1606 1610 1613 1617 1621 1625 1626 1628 1630 1632 1634 1637 1640
1 1 0 1 0 1 0 1 1 1 0 1 0 0 0 1
1648 1652 1660 1662 1666 1668 1674 1678 1686 1689 1691 1692 1695 1703 1707 1713
0 0 0 0 1 0 0 1 1 0 1 1 1 0 1 1
1719 1722 1730 1735 1736 1742 1745 1750 1754 1758 1765 1766 1768 1775 1778 1786
1 1 1 0 1 0 0 0 0 1 0 0 0 0 1 1
1792 1795 1800 1804 1806 1811 1816 1823 1830 1831 1835 1839 1846 1847 1849 1853
0 0 1 1 1 0 1 1 0 0 1 1 1 1 1 0
1855 1859 1866 1871 1874 1876 1883 1885 1888 1891 1894 1895 1897 1900 1905 1909
0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 1
1910 1915 1917 1922 1927 1932 1937 1941
1 1 1 1 1 1 1 1
$m0a$Srv_ftm_stts_cn
[1] 400 NA 1012 1925 1505 2503 NA 2466 2400 51 3762 304 NA 1217 3584
[16] NA 769 132 NA 1356 3657 673 264 4079 NA 1444 77 549 5074 321
[31] 3839 NA 3170 NA 2847 NA 223 3244 2297 NA 1350 NA NA 3428 NA
[46] 2256 NA NA 708 2598 3853 2386 1000 1434 1360 1847 3282 NA 2224 NA
[61] NA 3090 859 1487 NA 4191 2769 NA 1170 3683 NA NA NA 1827 1191
[76] 71 326 1690 NA 890 2540 3574 NA NA 3358 1657 198 3076 1741 2689
[91] 460 389 NA 750 137 NA 620 NA NA 552 NA NA 110 3086 3092
[106] 3222 NA 2583 NA 2044 2350 3445 951 3395 NA NA 1083 2288 515 2033
[121] 191 NA 971 3903 2468 824 NA 1037 NA 1411 850 NA 2796 NA NA
[136] NA NA 1297 2357 NA NA 2419 786 945 NA 3086 3382 1427 762 NA
[151] NA 1152 NA 140 NA 853 NA 2475 1536 NA NA 186 2055 276 1076
[166] NA 1684 NA 1212 NA NA NA NA NA NA 1492 NA NA NA NA
[181] NA NA 2241 974 2882 1576 733 NA NA NA 216 NA 797 NA NA
[196] 2555 NA NA NA 2932 NA NA NA 2090 2081 NA NA 904 NA NA
[211] NA NA NA 1786 1080 NA 790 NA NA 1235 NA 597 334 NA NA
[226] NA 999 NA 348 NA 1165 NA NA NA 2302 NA 1947 1874 694 NA
[241] 837 2128 930 1690 NA 1435 940 NA NA 2327 NA NA NA 737 NA
[256] NA NA NA NA 1674 NA 1850 1303 1542 1084 NA 179 1191 1898 2010
[271] NA NA 1649 1447 NA NA NA 1996 NA NA 41 1673 NA NA NA
[286] NA NA 1067 799 NA 901 1785 NA NA 875 NA 533 NA NA 207
[301] NA NA 1597 NA NA NA NA NA NA NA NA NA
$m1a
$m1a$M_lvlone
futime status != "censored" (Intercept) age sexfemale
1 400 1 1 58.76523 1
3 5169 0 1 56.44627 1
12 1012 1 1 70.07255 0
16 1925 1 1 54.74059 1
23 1505 1 1 38.10541 1
29 2503 1 1 66.25873 1
35 2501 0 1 55.53457 1
42 2466 1 1 53.05681 1
50 2400 1 1 42.50787 1
57 51 1 1 70.55989 1
58 3762 1 1 53.71389 1
70 304 1 1 59.13758 1
72 4247 0 1 45.68925 1
84 1217 1 1 56.22177 0
91 3584 1 1 64.64613 1
102 4345 0 1 40.44353 1
115 769 1 1 52.18344 1
118 132 1 1 53.93018 1
119 4901 0 1 49.56057 1
134 1356 1 1 59.95346 1
138 3657 1 1 64.18891 0
150 673 1 1 56.27652 1
153 264 1 1 55.96715 1
155 4079 1 1 44.52019 0
168 4796 0 1 45.07324 1
180 1444 1 1 52.02464 1
186 77 1 1 54.43943 1
187 549 1 1 44.94730 1
190 5074 1 1 63.87680 1
200 321 1 1 41.38535 1
203 3839 1 1 41.55236 1
215 5192 0 1 53.99589 1
231 3170 1 1 51.28268 1
241 4602 0 1 52.06023 1
255 2847 1 1 48.61875 1
259 4281 0 1 56.41068 1
270 223 1 1 61.72758 1
272 3244 1 1 36.62697 1
282 2297 1 1 55.39220 1
290 5136 0 1 46.66940 1
305 1350 1 1 33.63450 1
309 5122 0 1 33.69473 1
325 5225 0 1 48.87064 1
340 3428 1 1 37.58248 1
351 4694 0 1 41.79329 1
360 2256 1 1 45.79877 1
368 3245 0 1 47.42779 1
375 5096 0 1 49.13621 0
384 708 1 1 61.15264 1
388 2598 1 1 53.50856 1
397 3853 1 1 52.08761 1
407 2386 1 1 50.54073 0
416 1000 1 1 67.40862 1
419 1434 1 1 39.19781 1
424 1360 1 1 65.76318 0
430 1847 1 1 33.61807 1
436 3282 1 1 53.57153 1
447 5128 0 1 44.56947 0
463 2224 1 1 40.39425 1
470 5034 0 1 58.38193 1
483 4925 0 1 43.89870 0
497 3090 1 1 60.70637 1
507 859 1 1 46.62834 1
510 1487 1 1 62.90760 1
516 4842 0 1 40.20260 1
522 4191 1 1 46.45311 0
535 2769 1 1 51.28816 1
545 4708 0 1 32.61328 1
559 1170 1 1 49.33881 1
563 3683 1 1 56.39973 1
576 4865 0 1 48.84600 1
587 4853 0 1 32.49281 1
593 4859 0 1 38.49418 1
608 1827 1 1 51.92060 1
612 1191 1 1 43.51814 1
617 71 1 1 51.94251 1
618 326 1 1 49.82615 1
620 1690 1 1 47.94524 1
624 4376 0 1 46.51608 1
635 890 1 1 67.41136 0
639 2540 1 1 63.26352 1
649 3574 1 1 67.31006 1
659 4719 0 1 56.01369 1
674 4701 0 1 55.83025 1
677 3358 1 1 47.21697 1
688 1657 1 1 52.75838 1
689 198 1 1 37.27858 1
691 3076 1 1 41.39357 1
695 1741 1 1 52.44353 1
699 2689 1 1 33.47570 0
708 460 1 1 45.60712 1
711 389 1 1 76.70910 1
712 4583 0 1 36.53388 1
727 750 1 1 53.91650 1
730 137 1 1 46.39014 1
731 4520 0 1 48.84600 1
745 620 1 1 71.89322 0
749 4492 0 1 28.88433 1
763 4489 0 1 48.46817 0
776 552 1 1 51.46886 0
780 4250 0 1 44.95003 1
792 3770 0 1 56.56947 1
804 110 1 1 48.96372 1
805 3086 1 1 43.01711 1
815 3092 1 1 34.03970 1
825 3222 1 1 68.50924 1
830 4058 0 1 62.52156 1
841 2583 1 1 50.35729 1
849 3173 0 1 44.06297 1
859 2044 1 1 38.91034 1
866 2350 1 1 41.15264 1
874 3445 1 1 55.45791 1
885 951 1 1 51.23340 1
890 3395 1 1 52.82683 0
901 4091 0 1 42.63929 1
913 4015 0 1 61.07050 1
924 1083 1 1 49.65640 1
929 2288 1 1 48.85421 1
936 515 1 1 54.25599 1
938 2033 1 1 35.15127 0
946 191 1 1 67.90691 0
947 3966 0 1 55.43600 1
957 971 1 1 45.82067 1
959 3903 1 1 52.88980 0
960 2468 1 1 47.18138 1
969 824 1 1 53.59890 1
974 3924 0 1 44.10404 1
985 1037 1 1 41.94935 1
990 3908 0 1 63.61396 1
1002 1411 1 1 44.22724 1
1008 850 1 1 62.00137 1
1012 3613 0 1 40.55305 1
1018 2796 1 1 62.64476 0
1027 3818 0 1 42.33539 1
1039 3819 0 1 42.96783 1
1049 3767 0 1 55.96167 1
1058 3659 0 1 62.86105 1
1070 1297 1 1 51.24983 0
1076 2357 1 1 46.76249 1
1082 3728 0 1 54.07529 1
1094 3719 0 1 47.03628 1
1097 2419 1 1 55.72621 1
1106 786 1 1 46.10267 1
1109 945 1 1 52.28747 1
1113 3645 0 1 51.20055 1
1117 3086 1 1 33.86448 1
1124 3382 1 1 75.01164 1
1129 1427 1 1 30.86379 1
1135 762 1 1 61.80424 0
1138 3560 0 1 34.98700 1
1147 3539 0 1 55.04175 1
1158 1152 1 1 69.94114 0
1161 3532 0 1 49.60438 1
1171 140 1 1 69.37714 0
1172 3516 0 1 43.55647 1
1175 853 1 1 59.40862 1
1179 3504 0 1 48.75838 1
1190 2475 1 1 36.49281 1
1199 1536 1 1 45.76044 0
1203 3441 0 1 57.37166 1
1211 3466 0 1 42.74333 1
1222 186 1 1 58.81725 1
1223 2055 1 1 53.49760 1
1226 276 1 1 43.41410 1
1227 1076 1 1 53.30595 0
1232 3390 0 1 41.35524 1
1240 1684 1 1 60.95825 0
1246 3384 0 1 47.75359 1
1256 1212 1 1 35.49076 1
1261 3361 0 1 48.66256 1
1262 3243 0 1 52.66804 1
1272 2970 0 1 49.86995 1
1279 3326 0 1 30.27515 1
1288 3313 0 1 55.56742 1
1298 3293 0 1 52.15332 1
1308 1492 1 1 41.60986 1
1314 3278 0 1 55.45243 1
1315 3249 0 1 70.00411 1
1321 3242 0 1 43.94251 1
1324 3232 0 1 42.56810 1
1334 3225 0 1 44.56947 1
1335 3224 0 1 56.94456 1
1340 2241 1 1 40.26010 1
1348 974 1 1 37.60712 1
1353 2882 1 1 48.36140 1
1358 1576 1 1 70.83641 1
1362 733 1 1 35.79192 1
1366 2635 0 1 62.62286 1
1374 3125 0 1 50.64750 1
1379 3173 0 1 54.52704 1
1383 216 1 1 52.69268 1
1384 3112 0 1 52.72005 1
1394 797 1 1 56.77207 1
1398 3118 0 1 44.39699 1
1403 2999 0 1 29.55510 1
1404 2555 1 1 57.04038 1
1412 3034 0 1 44.62697 1
1421 3025 0 1 35.79740 1
1429 2991 0 1 40.71732 1
1432 2932 1 1 32.23272 1
1443 2963 0 1 41.09240 1
1453 2941 0 1 61.63997 1
1455 2890 0 1 37.05681 1
1464 2090 1 1 62.57906 1
1472 2081 1 1 48.97741 1
1477 2924 0 1 61.99042 1
1485 2840 0 1 72.77207 1
1492 904 1 1 61.29500 1
1495 2888 0 1 52.62423 1
1497 2893 0 1 49.76318 0
1507 2865 0 1 52.91444 1
1515 2845 0 1 47.26352 1
1521 2189 0 1 50.20397 1
1523 1786 1 1 69.34702 1
1527 1080 1 1 41.16906 1
1531 2336 0 1 59.16496 1
1538 790 1 1 36.07940 1
1542 2839 0 1 34.59548 1
1552 2826 0 1 42.71321 1
1558 1235 1 1 63.63039 1
1563 2719 0 1 56.62971 1
1571 597 1 1 46.26420 1
1575 334 1 1 61.24298 1
1576 2614 0 1 38.62012 1
1584 2691 0 1 38.77070 1
1593 2647 0 1 56.69541 1
1602 999 1 1 58.95140 0
1606 2636 0 1 36.92266 1
1610 348 1 1 62.41478 1
1613 2648 0 1 34.60917 1
1617 1165 1 1 58.33539 1
1621 2620 0 1 50.18207 1
1625 2601 0 1 42.68583 1
1626 2445 0 1 34.37919 1
1628 2302 1 1 33.18275 1
1630 2577 0 1 38.38193 1
1632 1947 1 1 59.76181 1
1634 1874 1 1 66.41205 1
1637 694 1 1 46.78987 1
1640 2500 0 1 56.07940 1
1648 837 1 1 41.37440 1
1652 2128 1 1 64.57221 1
1660 930 1 1 67.48802 1
1662 1690 1 1 44.82957 1
1666 2459 0 1 45.77139 1
1668 1435 1 1 32.95003 1
1674 940 1 1 41.22108 1
1678 2454 0 1 55.41684 1
1686 2452 0 1 47.98084 1
1689 2327 1 1 40.79124 1
1691 2307 0 1 56.97467 1
1692 2439 0 1 68.46270 1
1695 2434 0 1 78.43943 0
1703 737 1 1 39.85763 1
1707 2405 0 1 35.31006 1
1713 2370 0 1 31.44422 1
1719 2283 0 1 58.26420 1
1722 2371 0 1 51.48802 1
1730 2284 0 1 59.96988 1
1735 1674 1 1 74.52430 0
1736 2348 0 1 52.36413 1
1742 1850 1 1 42.78713 1
1745 1303 1 1 34.87474 1
1750 1542 1 1 44.13963 1
1754 1084 1 1 46.38193 1
1758 2287 0 1 56.30938 1
1765 179 1 1 70.90760 1
1766 1191 1 1 55.39493 1
1768 1898 1 1 45.08419 1
1775 2010 1 1 26.27789 1
1778 2238 0 1 50.47228 1
1786 2194 0 1 38.39836 1
1792 1649 1 1 47.41958 1
1795 1447 1 1 47.98084 1
1800 2020 0 1 38.31622 1
1804 2147 0 1 50.10815 1
1806 2105 0 1 35.08830 1
1811 1996 1 1 32.50376 1
1816 2102 0 1 56.15332 1
1823 2081 0 1 46.15469 1
1830 41 1 1 65.88364 1
1831 1673 1 1 33.94387 1
1835 2095 0 1 62.86105 1
1839 2087 0 1 48.56400 1
1846 2070 0 1 46.34908 1
1847 2077 0 1 38.85284 1
1849 1552 0 1 58.64750 1
1853 1067 1 1 48.93634 1
1855 799 1 1 67.57290 0
1859 2032 0 1 65.98494 1
1866 901 1 1 40.90075 1
1871 1785 1 1 50.24504 0
1874 1989 0 1 57.19644 1
1876 1971 0 1 60.53662 0
1883 875 1 1 35.35113 0
1885 1990 0 1 31.38125 1
1888 533 1 1 55.98631 0
1891 1969 0 1 52.72553 1
1894 1962 0 1 38.09172 1
1895 207 1 1 58.17112 1
1897 1969 0 1 45.21013 1
1900 1940 0 1 37.79877 1
1905 1597 1 1 60.65982 1
1909 1899 0 1 35.53457 1
1910 1885 0 1 43.06639 1
1915 1885 0 1 56.39151 1
1917 1818 0 1 30.57358 1
1922 1822 0 1 61.18275 1
1927 1663 0 1 58.29979 1
1932 1608 0 1 62.33265 1
1937 1508 0 1 37.99863 1
1941 1457 0 1 33.15264 1
$m1a$spM_lvlone
center scale
futime 2341.64103 1301.27242
status != "censored" NA NA
(Intercept) NA NA
age 50.01901 10.58126
sexfemale NA NA
$m1a$mu_reg_surv
[1] 0
$m1a$tau_reg_surv
[1] 0.001
$m1a$cens_Srv_ftm_stts_cn
1 3 12 16 23 29 35 42 50 57 58 70 72 84 91 102
0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1
115 118 119 134 138 150 153 155 168 180 186 187 190 200 203 215
0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1
231 241 255 259 270 272 282 290 305 309 325 340 351 360 368 375
0 1 0 1 0 0 0 1 0 1 1 0 1 0 1 1
384 388 397 407 416 419 424 430 436 447 463 470 483 497 507 510
0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0
516 522 535 545 559 563 576 587 593 608 612 617 618 620 624 635
1 0 0 1 0 0 1 1 1 0 0 0 0 0 1 0
639 649 659 674 677 688 689 691 695 699 708 711 712 727 730 731
0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1
745 749 763 776 780 792 804 805 815 825 830 841 849 859 866 874
0 1 1 0 1 1 0 0 0 0 1 0 1 0 0 0
885 890 901 913 924 929 936 938 946 947 957 959 960 969 974 985
0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0
990 1002 1008 1012 1018 1027 1039 1049 1058 1070 1076 1082 1094 1097 1106 1109
1 0 0 1 0 1 1 1 1 0 0 1 1 0 0 0
1113 1117 1124 1129 1135 1138 1147 1158 1161 1171 1172 1175 1179 1190 1199 1203
1 0 0 0 0 1 1 0 1 0 1 0 1 0 0 1
1211 1222 1223 1226 1227 1232 1240 1246 1256 1261 1262 1272 1279 1288 1298 1308
1 0 0 0 0 1 0 1 0 1 1 1 1 1 1 0
1314 1315 1321 1324 1334 1335 1340 1348 1353 1358 1362 1366 1374 1379 1383 1384
1 1 1 1 1 1 0 0 0 0 0 1 1 1 0 1
1394 1398 1403 1404 1412 1421 1429 1432 1443 1453 1455 1464 1472 1477 1485 1492
0 1 1 0 1 1 1 0 1 1 1 0 0 1 1 0
1495 1497 1507 1515 1521 1523 1527 1531 1538 1542 1552 1558 1563 1571 1575 1576
1 1 1 1 1 0 0 1 0 1 1 0 1 0 0 1
1584 1593 1602 1606 1610 1613 1617 1621 1625 1626 1628 1630 1632 1634 1637 1640
1 1 0 1 0 1 0 1 1 1 0 1 0 0 0 1
1648 1652 1660 1662 1666 1668 1674 1678 1686 1689 1691 1692 1695 1703 1707 1713
0 0 0 0 1 0 0 1 1 0 1 1 1 0 1 1
1719 1722 1730 1735 1736 1742 1745 1750 1754 1758 1765 1766 1768 1775 1778 1786
1 1 1 0 1 0 0 0 0 1 0 0 0 0 1 1
1792 1795 1800 1804 1806 1811 1816 1823 1830 1831 1835 1839 1846 1847 1849 1853
0 0 1 1 1 0 1 1 0 0 1 1 1 1 1 0
1855 1859 1866 1871 1874 1876 1883 1885 1888 1891 1894 1895 1897 1900 1905 1909
0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 1
1910 1915 1917 1922 1927 1932 1937 1941
1 1 1 1 1 1 1 1
$m1a$Srv_ftm_stts_cn
[1] 400 NA 1012 1925 1505 2503 NA 2466 2400 51 3762 304 NA 1217 3584
[16] NA 769 132 NA 1356 3657 673 264 4079 NA 1444 77 549 5074 321
[31] 3839 NA 3170 NA 2847 NA 223 3244 2297 NA 1350 NA NA 3428 NA
[46] 2256 NA NA 708 2598 3853 2386 1000 1434 1360 1847 3282 NA 2224 NA
[61] NA 3090 859 1487 NA 4191 2769 NA 1170 3683 NA NA NA 1827 1191
[76] 71 326 1690 NA 890 2540 3574 NA NA 3358 1657 198 3076 1741 2689
[91] 460 389 NA 750 137 NA 620 NA NA 552 NA NA 110 3086 3092
[106] 3222 NA 2583 NA 2044 2350 3445 951 3395 NA NA 1083 2288 515 2033
[121] 191 NA 971 3903 2468 824 NA 1037 NA 1411 850 NA 2796 NA NA
[136] NA NA 1297 2357 NA NA 2419 786 945 NA 3086 3382 1427 762 NA
[151] NA 1152 NA 140 NA 853 NA 2475 1536 NA NA 186 2055 276 1076
[166] NA 1684 NA 1212 NA NA NA NA NA NA 1492 NA NA NA NA
[181] NA NA 2241 974 2882 1576 733 NA NA NA 216 NA 797 NA NA
[196] 2555 NA NA NA 2932 NA NA NA 2090 2081 NA NA 904 NA NA
[211] NA NA NA 1786 1080 NA 790 NA NA 1235 NA 597 334 NA NA
[226] NA 999 NA 348 NA 1165 NA NA NA 2302 NA 1947 1874 694 NA
[241] 837 2128 930 1690 NA 1435 940 NA NA 2327 NA NA NA 737 NA
[256] NA NA NA NA 1674 NA 1850 1303 1542 1084 NA 179 1191 1898 2010
[271] NA NA 1649 1447 NA NA NA 1996 NA NA 41 1673 NA NA NA
[286] NA NA 1067 799 NA 901 1785 NA NA 875 NA 533 NA NA 207
[301] NA NA 1597 NA NA NA NA NA NA NA NA NA
$m1b
$m1b$M_lvlone
futime I(status != "censored") (Intercept) age sexfemale
1 400 1 1 58.76523 1
3 5169 0 1 56.44627 1
12 1012 1 1 70.07255 0
16 1925 1 1 54.74059 1
23 1505 1 1 38.10541 1
29 2503 1 1 66.25873 1
35 2501 0 1 55.53457 1
42 2466 1 1 53.05681 1
50 2400 1 1 42.50787 1
57 51 1 1 70.55989 1
58 3762 1 1 53.71389 1
70 304 1 1 59.13758 1
72 4247 0 1 45.68925 1
84 1217 1 1 56.22177 0
91 3584 1 1 64.64613 1
102 4345 0 1 40.44353 1
115 769 1 1 52.18344 1
118 132 1 1 53.93018 1
119 4901 0 1 49.56057 1
134 1356 1 1 59.95346 1
138 3657 1 1 64.18891 0
150 673 1 1 56.27652 1
153 264 1 1 55.96715 1
155 4079 1 1 44.52019 0
168 4796 0 1 45.07324 1
180 1444 1 1 52.02464 1
186 77 1 1 54.43943 1
187 549 1 1 44.94730 1
190 5074 1 1 63.87680 1
200 321 1 1 41.38535 1
203 3839 1 1 41.55236 1
215 5192 0 1 53.99589 1
231 3170 1 1 51.28268 1
241 4602 0 1 52.06023 1
255 2847 1 1 48.61875 1
259 4281 0 1 56.41068 1
270 223 1 1 61.72758 1
272 3244 1 1 36.62697 1
282 2297 1 1 55.39220 1
290 5136 0 1 46.66940 1
305 1350 1 1 33.63450 1
309 5122 0 1 33.69473 1
325 5225 0 1 48.87064 1
340 3428 1 1 37.58248 1
351 4694 0 1 41.79329 1
360 2256 1 1 45.79877 1
368 3245 0 1 47.42779 1
375 5096 0 1 49.13621 0
384 708 1 1 61.15264 1
388 2598 1 1 53.50856 1
397 3853 1 1 52.08761 1
407 2386 1 1 50.54073 0
416 1000 1 1 67.40862 1
419 1434 1 1 39.19781 1
424 1360 1 1 65.76318 0
430 1847 1 1 33.61807 1
436 3282 1 1 53.57153 1
447 5128 0 1 44.56947 0
463 2224 1 1 40.39425 1
470 5034 0 1 58.38193 1
483 4925 0 1 43.89870 0
497 3090 1 1 60.70637 1
507 859 1 1 46.62834 1
510 1487 1 1 62.90760 1
516 4842 0 1 40.20260 1
522 4191 1 1 46.45311 0
535 2769 1 1 51.28816 1
545 4708 0 1 32.61328 1
559 1170 1 1 49.33881 1
563 3683 1 1 56.39973 1
576 4865 0 1 48.84600 1
587 4853 0 1 32.49281 1
593 4859 0 1 38.49418 1
608 1827 1 1 51.92060 1
612 1191 1 1 43.51814 1
617 71 1 1 51.94251 1
618 326 1 1 49.82615 1
620 1690 1 1 47.94524 1
624 4376 0 1 46.51608 1
635 890 1 1 67.41136 0
639 2540 1 1 63.26352 1
649 3574 1 1 67.31006 1
659 4719 0 1 56.01369 1
674 4701 0 1 55.83025 1
677 3358 1 1 47.21697 1
688 1657 1 1 52.75838 1
689 198 1 1 37.27858 1
691 3076 1 1 41.39357 1
695 1741 1 1 52.44353 1
699 2689 1 1 33.47570 0
708 460 1 1 45.60712 1
711 389 1 1 76.70910 1
712 4583 0 1 36.53388 1
727 750 1 1 53.91650 1
730 137 1 1 46.39014 1
731 4520 0 1 48.84600 1
745 620 1 1 71.89322 0
749 4492 0 1 28.88433 1
763 4489 0 1 48.46817 0
776 552 1 1 51.46886 0
780 4250 0 1 44.95003 1
792 3770 0 1 56.56947 1
804 110 1 1 48.96372 1
805 3086 1 1 43.01711 1
815 3092 1 1 34.03970 1
825 3222 1 1 68.50924 1
830 4058 0 1 62.52156 1
841 2583 1 1 50.35729 1
849 3173 0 1 44.06297 1
859 2044 1 1 38.91034 1
866 2350 1 1 41.15264 1
874 3445 1 1 55.45791 1
885 951 1 1 51.23340 1
890 3395 1 1 52.82683 0
901 4091 0 1 42.63929 1
913 4015 0 1 61.07050 1
924 1083 1 1 49.65640 1
929 2288 1 1 48.85421 1
936 515 1 1 54.25599 1
938 2033 1 1 35.15127 0
946 191 1 1 67.90691 0
947 3966 0 1 55.43600 1
957 971 1 1 45.82067 1
959 3903 1 1 52.88980 0
960 2468 1 1 47.18138 1
969 824 1 1 53.59890 1
974 3924 0 1 44.10404 1
985 1037 1 1 41.94935 1
990 3908 0 1 63.61396 1
1002 1411 1 1 44.22724 1
1008 850 1 1 62.00137 1
1012 3613 0 1 40.55305 1
1018 2796 1 1 62.64476 0
1027 3818 0 1 42.33539 1
1039 3819 0 1 42.96783 1
1049 3767 0 1 55.96167 1
1058 3659 0 1 62.86105 1
1070 1297 1 1 51.24983 0
1076 2357 1 1 46.76249 1
1082 3728 0 1 54.07529 1
1094 3719 0 1 47.03628 1
1097 2419 1 1 55.72621 1
1106 786 1 1 46.10267 1
1109 945 1 1 52.28747 1
1113 3645 0 1 51.20055 1
1117 3086 1 1 33.86448 1
1124 3382 1 1 75.01164 1
1129 1427 1 1 30.86379 1
1135 762 1 1 61.80424 0
1138 3560 0 1 34.98700 1
1147 3539 0 1 55.04175 1
1158 1152 1 1 69.94114 0
1161 3532 0 1 49.60438 1
1171 140 1 1 69.37714 0
1172 3516 0 1 43.55647 1
1175 853 1 1 59.40862 1
1179 3504 0 1 48.75838 1
1190 2475 1 1 36.49281 1
1199 1536 1 1 45.76044 0
1203 3441 0 1 57.37166 1
1211 3466 0 1 42.74333 1
1222 186 1 1 58.81725 1
1223 2055 1 1 53.49760 1
1226 276 1 1 43.41410 1
1227 1076 1 1 53.30595 0
1232 3390 0 1 41.35524 1
1240 1684 1 1 60.95825 0
1246 3384 0 1 47.75359 1
1256 1212 1 1 35.49076 1
1261 3361 0 1 48.66256 1
1262 3243 0 1 52.66804 1
1272 2970 0 1 49.86995 1
1279 3326 0 1 30.27515 1
1288 3313 0 1 55.56742 1
1298 3293 0 1 52.15332 1
1308 1492 1 1 41.60986 1
1314 3278 0 1 55.45243 1
1315 3249 0 1 70.00411 1
1321 3242 0 1 43.94251 1
1324 3232 0 1 42.56810 1
1334 3225 0 1 44.56947 1
1335 3224 0 1 56.94456 1
1340 2241 1 1 40.26010 1
1348 974 1 1 37.60712 1
1353 2882 1 1 48.36140 1
1358 1576 1 1 70.83641 1
1362 733 1 1 35.79192 1
1366 2635 0 1 62.62286 1
1374 3125 0 1 50.64750 1
1379 3173 0 1 54.52704 1
1383 216 1 1 52.69268 1
1384 3112 0 1 52.72005 1
1394 797 1 1 56.77207 1
1398 3118 0 1 44.39699 1
1403 2999 0 1 29.55510 1
1404 2555 1 1 57.04038 1
1412 3034 0 1 44.62697 1
1421 3025 0 1 35.79740 1
1429 2991 0 1 40.71732 1
1432 2932 1 1 32.23272 1
1443 2963 0 1 41.09240 1
1453 2941 0 1 61.63997 1
1455 2890 0 1 37.05681 1
1464 2090 1 1 62.57906 1
1472 2081 1 1 48.97741 1
1477 2924 0 1 61.99042 1
1485 2840 0 1 72.77207 1
1492 904 1 1 61.29500 1
1495 2888 0 1 52.62423 1
1497 2893 0 1 49.76318 0
1507 2865 0 1 52.91444 1
1515 2845 0 1 47.26352 1
1521 2189 0 1 50.20397 1
1523 1786 1 1 69.34702 1
1527 1080 1 1 41.16906 1
1531 2336 0 1 59.16496 1
1538 790 1 1 36.07940 1
1542 2839 0 1 34.59548 1
1552 2826 0 1 42.71321 1
1558 1235 1 1 63.63039 1
1563 2719 0 1 56.62971 1
1571 597 1 1 46.26420 1
1575 334 1 1 61.24298 1
1576 2614 0 1 38.62012 1
1584 2691 0 1 38.77070 1
1593 2647 0 1 56.69541 1
1602 999 1 1 58.95140 0
1606 2636 0 1 36.92266 1
1610 348 1 1 62.41478 1
1613 2648 0 1 34.60917 1
1617 1165 1 1 58.33539 1
1621 2620 0 1 50.18207 1
1625 2601 0 1 42.68583 1
1626 2445 0 1 34.37919 1
1628 2302 1 1 33.18275 1
1630 2577 0 1 38.38193 1
1632 1947 1 1 59.76181 1
1634 1874 1 1 66.41205 1
1637 694 1 1 46.78987 1
1640 2500 0 1 56.07940 1
1648 837 1 1 41.37440 1
1652 2128 1 1 64.57221 1
1660 930 1 1 67.48802 1
1662 1690 1 1 44.82957 1
1666 2459 0 1 45.77139 1
1668 1435 1 1 32.95003 1
1674 940 1 1 41.22108 1
1678 2454 0 1 55.41684 1
1686 2452 0 1 47.98084 1
1689 2327 1 1 40.79124 1
1691 2307 0 1 56.97467 1
1692 2439 0 1 68.46270 1
1695 2434 0 1 78.43943 0
1703 737 1 1 39.85763 1
1707 2405 0 1 35.31006 1
1713 2370 0 1 31.44422 1
1719 2283 0 1 58.26420 1
1722 2371 0 1 51.48802 1
1730 2284 0 1 59.96988 1
1735 1674 1 1 74.52430 0
1736 2348 0 1 52.36413 1
1742 1850 1 1 42.78713 1
1745 1303 1 1 34.87474 1
1750 1542 1 1 44.13963 1
1754 1084 1 1 46.38193 1
1758 2287 0 1 56.30938 1
1765 179 1 1 70.90760 1
1766 1191 1 1 55.39493 1
1768 1898 1 1 45.08419 1
1775 2010 1 1 26.27789 1
1778 2238 0 1 50.47228 1
1786 2194 0 1 38.39836 1
1792 1649 1 1 47.41958 1
1795 1447 1 1 47.98084 1
1800 2020 0 1 38.31622 1
1804 2147 0 1 50.10815 1
1806 2105 0 1 35.08830 1
1811 1996 1 1 32.50376 1
1816 2102 0 1 56.15332 1
1823 2081 0 1 46.15469 1
1830 41 1 1 65.88364 1
1831 1673 1 1 33.94387 1
1835 2095 0 1 62.86105 1
1839 2087 0 1 48.56400 1
1846 2070 0 1 46.34908 1
1847 2077 0 1 38.85284 1
1849 1552 0 1 58.64750 1
1853 1067 1 1 48.93634 1
1855 799 1 1 67.57290 0
1859 2032 0 1 65.98494 1
1866 901 1 1 40.90075 1
1871 1785 1 1 50.24504 0
1874 1989 0 1 57.19644 1
1876 1971 0 1 60.53662 0
1883 875 1 1 35.35113 0
1885 1990 0 1 31.38125 1
1888 533 1 1 55.98631 0
1891 1969 0 1 52.72553 1
1894 1962 0 1 38.09172 1
1895 207 1 1 58.17112 1
1897 1969 0 1 45.21013 1
1900 1940 0 1 37.79877 1
1905 1597 1 1 60.65982 1
1909 1899 0 1 35.53457 1
1910 1885 0 1 43.06639 1
1915 1885 0 1 56.39151 1
1917 1818 0 1 30.57358 1
1922 1822 0 1 61.18275 1
1927 1663 0 1 58.29979 1
1932 1608 0 1 62.33265 1
1937 1508 0 1 37.99863 1
1941 1457 0 1 33.15264 1
$m1b$spM_lvlone
center scale
futime 2341.64103 1301.27242
I(status != "censored") NA NA
(Intercept) NA NA
age 50.01901 10.58126
sexfemale NA NA
$m1b$mu_reg_surv
[1] 0
$m1b$tau_reg_surv
[1] 0.001
$m1b$cens_Srv_ftm_stts_cn
1 3 12 16 23 29 35 42 50 57 58 70 72 84 91 102
0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1
115 118 119 134 138 150 153 155 168 180 186 187 190 200 203 215
0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1
231 241 255 259 270 272 282 290 305 309 325 340 351 360 368 375
0 1 0 1 0 0 0 1 0 1 1 0 1 0 1 1
384 388 397 407 416 419 424 430 436 447 463 470 483 497 507 510
0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0
516 522 535 545 559 563 576 587 593 608 612 617 618 620 624 635
1 0 0 1 0 0 1 1 1 0 0 0 0 0 1 0
639 649 659 674 677 688 689 691 695 699 708 711 712 727 730 731
0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1
745 749 763 776 780 792 804 805 815 825 830 841 849 859 866 874
0 1 1 0 1 1 0 0 0 0 1 0 1 0 0 0
885 890 901 913 924 929 936 938 946 947 957 959 960 969 974 985
0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0
990 1002 1008 1012 1018 1027 1039 1049 1058 1070 1076 1082 1094 1097 1106 1109
1 0 0 1 0 1 1 1 1 0 0 1 1 0 0 0
1113 1117 1124 1129 1135 1138 1147 1158 1161 1171 1172 1175 1179 1190 1199 1203
1 0 0 0 0 1 1 0 1 0 1 0 1 0 0 1
1211 1222 1223 1226 1227 1232 1240 1246 1256 1261 1262 1272 1279 1288 1298 1308
1 0 0 0 0 1 0 1 0 1 1 1 1 1 1 0
1314 1315 1321 1324 1334 1335 1340 1348 1353 1358 1362 1366 1374 1379 1383 1384
1 1 1 1 1 1 0 0 0 0 0 1 1 1 0 1
1394 1398 1403 1404 1412 1421 1429 1432 1443 1453 1455 1464 1472 1477 1485 1492
0 1 1 0 1 1 1 0 1 1 1 0 0 1 1 0
1495 1497 1507 1515 1521 1523 1527 1531 1538 1542 1552 1558 1563 1571 1575 1576
1 1 1 1 1 0 0 1 0 1 1 0 1 0 0 1
1584 1593 1602 1606 1610 1613 1617 1621 1625 1626 1628 1630 1632 1634 1637 1640
1 1 0 1 0 1 0 1 1 1 0 1 0 0 0 1
1648 1652 1660 1662 1666 1668 1674 1678 1686 1689 1691 1692 1695 1703 1707 1713
0 0 0 0 1 0 0 1 1 0 1 1 1 0 1 1
1719 1722 1730 1735 1736 1742 1745 1750 1754 1758 1765 1766 1768 1775 1778 1786
1 1 1 0 1 0 0 0 0 1 0 0 0 0 1 1
1792 1795 1800 1804 1806 1811 1816 1823 1830 1831 1835 1839 1846 1847 1849 1853
0 0 1 1 1 0 1 1 0 0 1 1 1 1 1 0
1855 1859 1866 1871 1874 1876 1883 1885 1888 1891 1894 1895 1897 1900 1905 1909
0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 1
1910 1915 1917 1922 1927 1932 1937 1941
1 1 1 1 1 1 1 1
$m1b$Srv_ftm_stts_cn
[1] 400 NA 1012 1925 1505 2503 NA 2466 2400 51 3762 304 NA 1217 3584
[16] NA 769 132 NA 1356 3657 673 264 4079 NA 1444 77 549 5074 321
[31] 3839 NA 3170 NA 2847 NA 223 3244 2297 NA 1350 NA NA 3428 NA
[46] 2256 NA NA 708 2598 3853 2386 1000 1434 1360 1847 3282 NA 2224 NA
[61] NA 3090 859 1487 NA 4191 2769 NA 1170 3683 NA NA NA 1827 1191
[76] 71 326 1690 NA 890 2540 3574 NA NA 3358 1657 198 3076 1741 2689
[91] 460 389 NA 750 137 NA 620 NA NA 552 NA NA 110 3086 3092
[106] 3222 NA 2583 NA 2044 2350 3445 951 3395 NA NA 1083 2288 515 2033
[121] 191 NA 971 3903 2468 824 NA 1037 NA 1411 850 NA 2796 NA NA
[136] NA NA 1297 2357 NA NA 2419 786 945 NA 3086 3382 1427 762 NA
[151] NA 1152 NA 140 NA 853 NA 2475 1536 NA NA 186 2055 276 1076
[166] NA 1684 NA 1212 NA NA NA NA NA NA 1492 NA NA NA NA
[181] NA NA 2241 974 2882 1576 733 NA NA NA 216 NA 797 NA NA
[196] 2555 NA NA NA 2932 NA NA NA 2090 2081 NA NA 904 NA NA
[211] NA NA NA 1786 1080 NA 790 NA NA 1235 NA 597 334 NA NA
[226] NA 999 NA 348 NA 1165 NA NA NA 2302 NA 1947 1874 694 NA
[241] 837 2128 930 1690 NA 1435 940 NA NA 2327 NA NA NA 737 NA
[256] NA NA NA NA 1674 NA 1850 1303 1542 1084 NA 179 1191 1898 2010
[271] NA NA 1649 1447 NA NA NA 1996 NA NA 41 1673 NA NA NA
[286] NA NA 1067 799 NA 901 1785 NA NA 875 NA 533 NA NA 207
[301] NA NA 1597 NA NA NA NA NA NA NA NA NA
$m2a
$m2a$M_lvlone
futime status != "censored" copper (Intercept)
1 400 1 156 1
3 5169 0 54 1
12 1012 1 210 1
16 1925 1 64 1
23 1505 1 143 1
29 2503 1 50 1
35 2501 0 52 1
42 2466 1 52 1
50 2400 1 79 1
57 51 1 140 1
58 3762 1 46 1
70 304 1 94 1
72 4247 0 40 1
84 1217 1 43 1
91 3584 1 173 1
102 4345 0 28 1
115 769 1 159 1
118 132 1 588 1
119 4901 0 39 1
134 1356 1 140 1
138 3657 1 41 1
150 673 1 464 1
153 264 1 558 1
155 4079 1 124 1
168 4796 0 40 1
180 1444 1 53 1
186 77 1 221 1
187 549 1 209 1
190 5074 1 24 1
200 321 1 172 1
203 3839 1 114 1
215 5192 0 101 1
231 3170 1 82 1
241 4602 0 37 1
255 2847 1 201 1
259 4281 0 18 1
270 223 1 150 1
272 3244 1 102 1
282 2297 1 52 1
290 5136 0 105 1
305 1350 1 96 1
309 5122 0 122 1
325 5225 0 36 1
340 3428 1 131 1
351 4694 0 19 1
360 2256 1 161 1
368 3245 0 68 1
375 5096 0 281 1
384 708 1 58 1
388 2598 1 43 1
397 3853 1 54 1
407 2386 1 158 1
416 1000 1 94 1
419 1434 1 262 1
424 1360 1 121 1
430 1847 1 88 1
436 3282 1 231 1
447 5128 0 73 1
463 2224 1 49 1
470 5034 0 82 1
483 4925 0 28 1
497 3090 1 58 1
507 859 1 95 1
510 1487 1 52 1
516 4842 0 74 1
522 4191 1 105 1
535 2769 1 84 1
545 4708 0 58 1
559 1170 1 159 1
563 3683 1 20 1
576 4865 0 79 1
587 4853 0 51 1
593 4859 0 17 1
608 1827 1 280 1
612 1191 1 207 1
617 71 1 111 1
618 326 1 199 1
620 1690 1 75 1
624 4376 0 13 1
635 890 1 269 1
639 2540 1 34 1
649 3574 1 154 1
659 4719 0 48 1
674 4701 0 29 1
677 3358 1 58 1
688 1657 1 75 1
689 198 1 75 1
691 3076 1 37 1
695 1741 1 50 1
699 2689 1 94 1
708 460 1 110 1
711 389 1 36 1
712 4583 0 73 1
727 750 1 178 1
730 137 1 182 1
731 4520 0 33 1
745 620 1 62 1
749 4492 0 77 1
763 4489 0 148 1
776 552 1 145 1
780 4250 0 31 1
792 3770 0 172 1
804 110 1 57 1
805 3086 1 20 1
815 3092 1 38 1
825 3222 1 50 1
830 4058 0 10 1
841 2583 1 14 1
849 3173 0 53 1
859 2044 1 74 1
866 2350 1 77 1
874 3445 1 89 1
885 951 1 103 1
890 3395 1 208 1
901 4091 0 81 1
913 4015 0 74 1
924 1083 1 111 1
929 2288 1 67 1
936 515 1 129 1
938 2033 1 444 1
946 191 1 73 1
947 3966 0 29 1
957 971 1 18 1
959 3903 1 25 1
960 2468 1 75 1
969 824 1 NA 1
974 3924 0 9 1
985 1037 1 42 1
990 3908 0 30 1
1002 1411 1 205 1
1008 850 1 74 1
1012 3613 0 60 1
1018 2796 1 13 1
1027 3818 0 35 1
1039 3819 0 123 1
1049 3767 0 27 1
1058 3659 0 79 1
1070 1297 1 262 1
1076 2357 1 159 1
1082 3728 0 38 1
1094 3719 0 59 1
1097 2419 1 20 1
1106 786 1 86 1
1109 945 1 152 1
1113 3645 0 96 1
1117 3086 1 58 1
1124 3382 1 32 1
1129 1427 1 247 1
1135 762 1 290 1
1138 3560 0 57 1
1147 3539 0 57 1
1158 1152 1 243 1
1161 3532 0 68 1
1171 140 1 225 1
1172 3516 0 57 1
1175 853 1 219 1
1179 3504 0 34 1
1190 2475 1 32 1
1199 1536 1 217 1
1203 3441 0 13 1
1211 3466 0 4 1
1222 186 1 91 1
1223 2055 1 20 1
1226 276 1 161 1
1227 1076 1 80 1
1232 3390 0 84 1
1240 1684 1 200 1
1246 3384 0 44 1
1256 1212 1 67 1
1261 3361 0 32 1
1262 3243 0 31 1
1272 2970 0 65 1
1279 3326 0 76 1
1288 3313 0 63 1
1298 3293 0 152 1
1308 1492 1 77 1
1314 3278 0 49 1
1315 3249 0 51 1
1321 3242 0 39 1
1324 3232 0 44 1
1334 3225 0 63 1
1335 3224 0 161 1
1340 2241 1 44 1
1348 974 1 358 1
1353 2882 1 42 1
1358 1576 1 51 1
1362 733 1 251 1
1366 2635 0 41 1
1374 3125 0 52 1
1379 3173 0 24 1
1383 216 1 233 1
1384 3112 0 67 1
1394 797 1 267 1
1398 3118 0 50 1
1403 2999 0 76 1
1404 2555 1 13 1
1412 3034 0 25 1
1421 3025 0 38 1
1429 2991 0 52 1
1432 2932 1 112 1
1443 2963 0 26 1
1453 2941 0 15 1
1455 2890 0 227 1
1464 2090 1 23 1
1472 2081 1 108 1
1477 2924 0 12 1
1485 2840 0 14 1
1492 904 1 58 1
1495 2888 0 11 1
1497 2893 0 81 1
1507 2865 0 27 1
1515 2845 0 11 1
1521 2189 0 9 1
1523 1786 1 34 1
1527 1080 1 141 1
1531 2336 0 20 1
1538 790 1 65 1
1542 2839 0 29 1
1552 2826 0 67 1
1558 1235 1 96 1
1563 2719 0 72 1
1571 597 1 227 1
1575 334 1 123 1
1576 2614 0 67 1
1584 2691 0 108 1
1593 2647 0 39 1
1602 999 1 172 1
1606 2636 0 41 1
1610 348 1 200 1
1613 2648 0 51 1
1617 1165 1 115 1
1621 2620 0 47 1
1625 2601 0 412 1
1626 2445 0 78 1
1628 2302 1 71 1
1630 2577 0 67 1
1632 1947 1 105 1
1634 1874 1 NA 1
1637 694 1 231 1
1640 2500 0 24 1
1648 837 1 308 1
1652 2128 1 86 1
1660 930 1 139 1
1662 1690 1 121 1
1666 2459 0 48 1
1668 1435 1 63 1
1674 940 1 89 1
1678 2454 0 39 1
1686 2452 0 35 1
1689 2327 1 23 1
1691 2307 0 40 1
1692 2439 0 116 1
1695 2434 0 380 1
1703 737 1 121 1
1707 2405 0 35 1
1713 2370 0 48 1
1719 2283 0 36 1
1722 2371 0 80 1
1730 2284 0 42 1
1735 1674 1 188 1
1736 2348 0 129 1
1742 1850 1 65 1
1745 1303 1 45 1
1750 1542 1 188 1
1754 1084 1 121 1
1758 2287 0 52 1
1765 179 1 138 1
1766 1191 1 16 1
1768 1898 1 143 1
1775 2010 1 102 1
1778 2238 0 94 1
1786 2194 0 22 1
1792 1649 1 75 1
1795 1447 1 136 1
1800 2020 0 77 1
1804 2147 0 67 1
1806 2105 0 44 1
1811 1996 1 130 1
1816 2102 0 38 1
1823 2081 0 69 1
1830 41 1 220 1
1831 1673 1 97 1
1835 2095 0 103 1
1839 2087 0 75 1
1846 2070 0 65 1
1847 2077 0 70 1
1849 1552 0 155 1
1853 1067 1 107 1
1855 799 1 177 1
1859 2032 0 33 1
1866 901 1 123 1
1871 1785 1 84 1
1874 1989 0 196 1
1876 1971 0 88 1
1883 875 1 102 1
1885 1990 0 62 1
1888 533 1 100 1
1891 1969 0 73 1
1894 1962 0 90 1
1895 207 1 234 1
1897 1969 0 50 1
1900 1940 0 43 1
1905 1597 1 108 1
1909 1899 0 22 1
1910 1885 0 73 1
1915 1885 0 31 1
1917 1818 0 52 1
1922 1822 0 24 1
1927 1663 0 41 1
1932 1608 0 39 1
1937 1508 0 69 1
1941 1457 0 186 1
$m2a$spM_lvlone
center scale
futime 2341.64103 1301.27242
status != "censored" NA NA
copper 97.64839 85.61392
(Intercept) NA NA
$m2a$mu_reg_norm
[1] 0
$m2a$tau_reg_norm
[1] 1e-04
$m2a$shape_tau_norm
[1] 0.01
$m2a$rate_tau_norm
[1] 0.01
$m2a$mu_reg_surv
[1] 0
$m2a$tau_reg_surv
[1] 0.001
$m2a$cens_Srv_ftm_stts_cn
1 3 12 16 23 29 35 42 50 57 58 70 72 84 91 102
0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1
115 118 119 134 138 150 153 155 168 180 186 187 190 200 203 215
0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1
231 241 255 259 270 272 282 290 305 309 325 340 351 360 368 375
0 1 0 1 0 0 0 1 0 1 1 0 1 0 1 1
384 388 397 407 416 419 424 430 436 447 463 470 483 497 507 510
0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0
516 522 535 545 559 563 576 587 593 608 612 617 618 620 624 635
1 0 0 1 0 0 1 1 1 0 0 0 0 0 1 0
639 649 659 674 677 688 689 691 695 699 708 711 712 727 730 731
0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1
745 749 763 776 780 792 804 805 815 825 830 841 849 859 866 874
0 1 1 0 1 1 0 0 0 0 1 0 1 0 0 0
885 890 901 913 924 929 936 938 946 947 957 959 960 969 974 985
0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0
990 1002 1008 1012 1018 1027 1039 1049 1058 1070 1076 1082 1094 1097 1106 1109
1 0 0 1 0 1 1 1 1 0 0 1 1 0 0 0
1113 1117 1124 1129 1135 1138 1147 1158 1161 1171 1172 1175 1179 1190 1199 1203
1 0 0 0 0 1 1 0 1 0 1 0 1 0 0 1
1211 1222 1223 1226 1227 1232 1240 1246 1256 1261 1262 1272 1279 1288 1298 1308
1 0 0 0 0 1 0 1 0 1 1 1 1 1 1 0
1314 1315 1321 1324 1334 1335 1340 1348 1353 1358 1362 1366 1374 1379 1383 1384
1 1 1 1 1 1 0 0 0 0 0 1 1 1 0 1
1394 1398 1403 1404 1412 1421 1429 1432 1443 1453 1455 1464 1472 1477 1485 1492
0 1 1 0 1 1 1 0 1 1 1 0 0 1 1 0
1495 1497 1507 1515 1521 1523 1527 1531 1538 1542 1552 1558 1563 1571 1575 1576
1 1 1 1 1 0 0 1 0 1 1 0 1 0 0 1
1584 1593 1602 1606 1610 1613 1617 1621 1625 1626 1628 1630 1632 1634 1637 1640
1 1 0 1 0 1 0 1 1 1 0 1 0 0 0 1
1648 1652 1660 1662 1666 1668 1674 1678 1686 1689 1691 1692 1695 1703 1707 1713
0 0 0 0 1 0 0 1 1 0 1 1 1 0 1 1
1719 1722 1730 1735 1736 1742 1745 1750 1754 1758 1765 1766 1768 1775 1778 1786
1 1 1 0 1 0 0 0 0 1 0 0 0 0 1 1
1792 1795 1800 1804 1806 1811 1816 1823 1830 1831 1835 1839 1846 1847 1849 1853
0 0 1 1 1 0 1 1 0 0 1 1 1 1 1 0
1855 1859 1866 1871 1874 1876 1883 1885 1888 1891 1894 1895 1897 1900 1905 1909
0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 1
1910 1915 1917 1922 1927 1932 1937 1941
1 1 1 1 1 1 1 1
$m2a$Srv_ftm_stts_cn
[1] 400 NA 1012 1925 1505 2503 NA 2466 2400 51 3762 304 NA 1217 3584
[16] NA 769 132 NA 1356 3657 673 264 4079 NA 1444 77 549 5074 321
[31] 3839 NA 3170 NA 2847 NA 223 3244 2297 NA 1350 NA NA 3428 NA
[46] 2256 NA NA 708 2598 3853 2386 1000 1434 1360 1847 3282 NA 2224 NA
[61] NA 3090 859 1487 NA 4191 2769 NA 1170 3683 NA NA NA 1827 1191
[76] 71 326 1690 NA 890 2540 3574 NA NA 3358 1657 198 3076 1741 2689
[91] 460 389 NA 750 137 NA 620 NA NA 552 NA NA 110 3086 3092
[106] 3222 NA 2583 NA 2044 2350 3445 951 3395 NA NA 1083 2288 515 2033
[121] 191 NA 971 3903 2468 824 NA 1037 NA 1411 850 NA 2796 NA NA
[136] NA NA 1297 2357 NA NA 2419 786 945 NA 3086 3382 1427 762 NA
[151] NA 1152 NA 140 NA 853 NA 2475 1536 NA NA 186 2055 276 1076
[166] NA 1684 NA 1212 NA NA NA NA NA NA 1492 NA NA NA NA
[181] NA NA 2241 974 2882 1576 733 NA NA NA 216 NA 797 NA NA
[196] 2555 NA NA NA 2932 NA NA NA 2090 2081 NA NA 904 NA NA
[211] NA NA NA 1786 1080 NA 790 NA NA 1235 NA 597 334 NA NA
[226] NA 999 NA 348 NA 1165 NA NA NA 2302 NA 1947 1874 694 NA
[241] 837 2128 930 1690 NA 1435 940 NA NA 2327 NA NA NA 737 NA
[256] NA NA NA NA 1674 NA 1850 1303 1542 1084 NA 179 1191 1898 2010
[271] NA NA 1649 1447 NA NA NA 1996 NA NA 41 1673 NA NA NA
[286] NA NA 1067 799 NA 901 1785 NA NA 875 NA 533 NA NA 207
[301] NA NA 1597 NA NA NA NA NA NA NA NA NA
$m3a
$m3a$M_lvlone
futime status != "censored" trig copper (Intercept) sexfemale age
1 400 1 172 156 1 1 58.76523
3 5169 0 88 54 1 1 56.44627
12 1012 1 55 210 1 0 70.07255
16 1925 1 92 64 1 1 54.74059
23 1505 1 72 143 1 1 38.10541
29 2503 1 63 50 1 1 66.25873
35 2501 0 213 52 1 1 55.53457
42 2466 1 189 52 1 1 53.05681
50 2400 1 88 79 1 1 42.50787
57 51 1 143 140 1 1 70.55989
58 3762 1 79 46 1 1 53.71389
70 304 1 95 94 1 1 59.13758
72 4247 0 130 40 1 1 45.68925
84 1217 1 NA 43 1 0 56.22177
91 3584 1 96 173 1 1 64.64613
102 4345 0 58 28 1 1 40.44353
115 769 1 128 159 1 1 52.18344
118 132 1 200 588 1 1 53.93018
119 4901 0 123 39 1 1 49.56057
134 1356 1 135 140 1 1 59.95346
138 3657 1 83 41 1 0 64.18891
150 673 1 55 464 1 1 56.27652
153 264 1 191 558 1 1 55.96715
155 4079 1 230 124 1 0 44.52019
168 4796 0 66 40 1 1 45.07324
180 1444 1 166 53 1 1 52.02464
186 77 1 168 221 1 1 54.43943
187 549 1 195 209 1 1 44.94730
190 5074 1 86 24 1 1 63.87680
200 321 1 158 172 1 1 41.38535
203 3839 1 101 114 1 1 41.55236
215 5192 0 158 101 1 1 53.99589
231 3170 1 113 82 1 1 51.28268
241 4602 0 64 37 1 1 52.06023
255 2847 1 151 201 1 1 48.61875
259 4281 0 96 18 1 1 56.41068
270 223 1 118 150 1 1 61.72758
272 3244 1 87 102 1 1 36.62697
282 2297 1 111 52 1 1 55.39220
290 5136 0 NA 105 1 1 46.66940
305 1350 1 NA 96 1 1 33.63450
309 5122 0 NA 122 1 1 33.69473
325 5225 0 89 36 1 1 48.87064
340 3428 1 50 131 1 1 37.58248
351 4694 0 NA 19 1 1 41.79329
360 2256 1 165 161 1 1 45.79877
368 3245 0 71 68 1 1 47.42779
375 5096 0 178 281 1 0 49.13621
384 708 1 NA 58 1 1 61.15264
388 2598 1 73 43 1 1 53.50856
397 3853 1 143 54 1 1 52.08761
407 2386 1 93 158 1 0 50.54073
416 1000 1 NA 94 1 1 67.40862
419 1434 1 125 262 1 1 39.19781
424 1360 1 219 121 1 0 65.76318
430 1847 1 319 88 1 1 33.61807
436 3282 1 94 231 1 1 53.57153
447 5128 0 118 73 1 0 44.56947
463 2224 1 124 49 1 1 40.39425
470 5034 0 58 82 1 1 58.38193
483 4925 0 188 28 1 0 43.89870
497 3090 1 112 58 1 1 60.70637
507 859 1 133 95 1 1 46.62834
510 1487 1 188 52 1 1 62.90760
516 4842 0 108 74 1 1 40.20260
522 4191 1 171 105 1 0 46.45311
535 2769 1 185 84 1 1 51.28816
545 4708 0 46 58 1 1 32.61328
559 1170 1 184 159 1 1 49.33881
563 3683 1 NA 20 1 1 56.39973
576 4865 0 76 79 1 1 48.84600
587 4853 0 80 51 1 1 32.49281
593 4859 0 56 17 1 1 38.49418
608 1827 1 309 280 1 1 51.92060
612 1191 1 598 207 1 1 43.51814
617 71 1 243 111 1 1 51.94251
618 326 1 91 199 1 1 49.82615
620 1690 1 104 75 1 1 47.94524
624 4376 0 70 13 1 1 46.51608
635 890 1 91 269 1 0 67.41136
639 2540 1 318 34 1 1 63.26352
649 3574 1 272 154 1 1 67.31006
659 4719 0 100 48 1 1 56.01369
674 4701 0 74 29 1 1 55.83025
677 3358 1 84 58 1 1 47.21697
688 1657 1 174 75 1 1 52.75838
689 198 1 72 75 1 1 37.27858
691 3076 1 83 37 1 1 41.39357
695 1741 1 98 50 1 1 52.44353
699 2689 1 155 94 1 0 33.47570
708 460 1 56 110 1 1 45.60712
711 389 1 70 36 1 1 76.70910
712 4583 0 68 73 1 1 36.53388
727 750 1 69 178 1 1 53.91650
730 137 1 NA 182 1 1 46.39014
731 4520 0 NA 33 1 1 48.84600
745 620 1 91 62 1 0 71.89322
749 4492 0 101 77 1 1 28.88433
763 4489 0 118 148 1 0 48.46817
776 552 1 122 145 1 0 51.46886
780 4250 0 166 31 1 1 44.95003
792 3770 0 84 172 1 1 56.56947
804 110 1 102 57 1 1 48.96372
805 3086 1 53 20 1 1 43.01711
815 3092 1 102 38 1 1 34.03970
825 3222 1 NA 50 1 1 68.50924
830 4058 0 77 10 1 1 62.52156
841 2583 1 84 14 1 1 50.35729
849 3173 0 52 53 1 1 44.06297
859 2044 1 109 74 1 1 38.91034
866 2350 1 78 77 1 1 41.15264
874 3445 1 90 89 1 1 55.45791
885 951 1 100 103 1 1 51.23340
890 3395 1 78 208 1 0 52.82683
901 4091 0 156 81 1 1 42.63929
913 4015 0 382 74 1 1 61.07050
924 1083 1 149 111 1 1 49.65640
929 2288 1 93 67 1 1 48.85421
936 515 1 114 129 1 1 54.25599
938 2033 1 210 444 1 0 35.15127
946 191 1 49 73 1 0 67.90691
947 3966 0 85 29 1 1 55.43600
957 971 1 NA 18 1 1 45.82067
959 3903 1 107 25 1 0 52.88980
960 2468 1 137 75 1 1 47.18138
969 824 1 68 NA 1 1 53.59890
974 3924 0 95 9 1 1 44.10404
985 1037 1 NA 42 1 1 41.94935
990 3908 0 232 30 1 1 63.61396
1002 1411 1 97 205 1 1 44.22724
1008 850 1 104 74 1 1 62.00137
1012 3613 0 175 60 1 1 40.55305
1018 2796 1 99 13 1 0 62.64476
1027 3818 0 105 35 1 1 42.33539
1039 3819 0 121 123 1 1 42.96783
1049 3767 0 69 27 1 1 55.96167
1058 3659 0 152 79 1 1 62.86105
1070 1297 1 218 262 1 0 51.24983
1076 2357 1 134 159 1 1 46.76249
1082 3728 0 146 38 1 1 54.07529
1094 3719 0 56 59 1 1 47.03628
1097 2419 1 88 20 1 1 55.72621
1106 786 1 103 86 1 1 46.10267
1109 945 1 171 152 1 1 52.28747
1113 3645 0 106 96 1 1 51.20055
1117 3086 1 NA 58 1 1 33.86448
1124 3382 1 114 32 1 1 75.01164
1129 1427 1 280 247 1 1 30.86379
1135 762 1 112 290 1 0 61.80424
1138 3560 0 NA 57 1 1 34.98700
1147 3539 0 174 57 1 1 55.04175
1158 1152 1 126 243 1 0 69.94114
1161 3532 0 68 68 1 1 49.60438
1171 140 1 75 225 1 0 69.37714
1172 3516 0 80 57 1 1 43.55647
1175 853 1 205 219 1 1 59.40862
1179 3504 0 206 34 1 1 48.75838
1190 2475 1 118 32 1 1 36.49281
1199 1536 1 140 217 1 0 45.76044
1203 3441 0 119 13 1 1 57.37166
1211 3466 0 180 4 1 1 42.74333
1222 186 1 101 91 1 1 58.81725
1223 2055 1 68 20 1 1 53.49760
1226 276 1 NA 161 1 1 43.41410
1227 1076 1 142 80 1 0 53.30595
1232 3390 0 108 84 1 1 41.35524
1240 1684 1 154 200 1 0 60.95825
1246 3384 0 110 44 1 1 47.75359
1256 1212 1 137 67 1 1 35.49076
1261 3361 0 52 32 1 1 48.66256
1262 3243 0 NA 31 1 1 52.66804
1272 2970 0 126 65 1 1 49.86995
1279 3326 0 86 76 1 1 30.27515
1288 3313 0 NA 63 1 1 55.56742
1298 3293 0 168 152 1 1 52.15332
1308 1492 1 NA 77 1 1 41.60986
1314 3278 0 133 49 1 1 55.45243
1315 3249 0 NA 51 1 1 70.00411
1321 3242 0 133 39 1 1 43.94251
1324 3232 0 76 44 1 1 42.56810
1334 3225 0 106 63 1 1 44.56947
1335 3224 0 NA 161 1 1 56.94456
1340 2241 1 145 44 1 1 40.26010
1348 974 1 140 358 1 1 37.60712
1353 2882 1 94 42 1 1 48.36140
1358 1576 1 62 51 1 1 70.83641
1362 733 1 137 251 1 1 35.79192
1366 2635 0 98 41 1 1 62.62286
1374 3125 0 164 52 1 1 50.64750
1379 3173 0 NA 24 1 1 54.52704
1383 216 1 432 233 1 1 52.69268
1384 3112 0 101 67 1 1 52.72005
1394 797 1 157 267 1 1 56.77207
1398 3118 0 56 50 1 1 44.39699
1403 2999 0 90 76 1 1 29.55510
1404 2555 1 62 13 1 1 57.04038
1412 3034 0 102 25 1 1 44.62697
1421 3025 0 90 38 1 1 35.79740
1429 2991 0 194 52 1 1 40.71732
1432 2932 1 114 112 1 1 32.23272
1443 2963 0 67 26 1 1 41.09240
1453 2941 0 89 15 1 1 61.63997
1455 2890 0 57 227 1 1 37.05681
1464 2090 1 58 23 1 1 62.57906
1472 2081 1 NA 108 1 1 48.97741
1477 2924 0 68 12 1 1 61.99042
1485 2840 0 NA 14 1 1 72.77207
1492 904 1 131 58 1 1 61.29500
1495 2888 0 58 11 1 1 52.62423
1497 2893 0 90 81 1 0 49.76318
1507 2865 0 NA 27 1 1 52.91444
1515 2845 0 99 11 1 1 47.26352
1521 2189 0 55 9 1 1 50.20397
1523 1786 1 224 34 1 1 69.34702
1527 1080 1 157 141 1 1 41.16906
1531 2336 0 NA 20 1 1 59.16496
1538 790 1 210 65 1 1 36.07940
1542 2839 0 NA 29 1 1 34.59548
1552 2826 0 85 67 1 1 42.71321
1558 1235 1 113 96 1 1 63.63039
1563 2719 0 154 72 1 1 56.62971
1571 597 1 135 227 1 1 46.26420
1575 334 1 155 123 1 1 61.24298
1576 2614 0 101 67 1 1 38.62012
1584 2691 0 135 108 1 1 38.77070
1593 2647 0 104 39 1 1 56.69541
1602 999 1 63 172 1 0 58.95140
1606 2636 0 74 41 1 1 36.92266
1610 348 1 118 200 1 1 62.41478
1613 2648 0 44 51 1 1 34.60917
1617 1165 1 90 115 1 1 58.33539
1621 2620 0 87 47 1 1 50.18207
1625 2601 0 87 412 1 1 42.68583
1626 2445 0 177 78 1 1 34.37919
1628 2302 1 111 71 1 1 33.18275
1630 2577 0 106 67 1 1 38.38193
1632 1947 1 108 105 1 1 59.76181
1634 1874 1 81 NA 1 1 66.41205
1637 694 1 99 231 1 1 46.78987
1640 2500 0 139 24 1 1 56.07940
1648 837 1 322 308 1 1 41.37440
1652 2128 1 152 86 1 1 64.57221
1660 930 1 139 139 1 1 67.48802
1662 1690 1 231 121 1 1 44.82957
1666 2459 0 214 48 1 1 45.77139
1668 1435 1 33 63 1 1 32.95003
1674 940 1 139 89 1 1 41.22108
1678 2454 0 146 39 1 1 55.41684
1686 2452 0 71 35 1 1 47.98084
1689 2327 1 85 23 1 1 40.79124
1691 2307 0 120 40 1 1 56.97467
1692 2439 0 90 116 1 1 68.46270
1695 2434 0 154 380 1 0 78.43943
1703 737 1 91 121 1 1 39.85763
1707 2405 0 118 35 1 1 35.31006
1713 2370 0 108 48 1 1 31.44422
1719 2283 0 146 36 1 1 58.26420
1722 2371 0 55 80 1 1 51.48802
1730 2284 0 126 42 1 1 59.96988
1735 1674 1 128 188 1 0 74.52430
1736 2348 0 NA 129 1 1 52.36413
1742 1850 1 209 65 1 1 42.78713
1745 1303 1 77 45 1 1 34.87474
1750 1542 1 130 188 1 1 44.13963
1754 1084 1 146 121 1 1 46.38193
1758 2287 0 260 52 1 1 56.30938
1765 179 1 91 138 1 1 70.90760
1766 1191 1 179 16 1 1 55.39493
1768 1898 1 44 143 1 1 45.08419
1775 2010 1 117 102 1 1 26.27789
1778 2238 0 177 94 1 1 50.47228
1786 2194 0 85 22 1 1 38.39836
1792 1649 1 116 75 1 1 47.41958
1795 1447 1 NA 136 1 1 47.98084
1800 2020 0 114 77 1 1 38.31622
1804 2147 0 108 67 1 1 50.10815
1806 2105 0 111 44 1 1 35.08830
1811 1996 1 193 130 1 1 32.50376
1816 2102 0 56 38 1 1 56.15332
1823 2081 0 140 69 1 1 46.15469
1830 41 1 229 220 1 1 65.88364
1831 1673 1 169 97 1 1 33.94387
1835 2095 0 113 103 1 1 62.86105
1839 2087 0 195 75 1 1 48.56400
1846 2070 0 80 65 1 1 46.34908
1847 2077 0 118 70 1 1 38.85284
1849 1552 0 151 155 1 1 58.64750
1853 1067 1 242 107 1 1 48.93634
1855 799 1 242 177 1 0 67.57290
1859 2032 0 69 33 1 1 65.98494
1866 901 1 84 123 1 1 40.90075
1871 1785 1 153 84 1 0 50.24504
1874 1989 0 78 196 1 1 57.19644
1876 1971 0 133 88 1 0 60.53662
1883 875 1 194 102 1 0 35.35113
1885 1990 0 95 62 1 1 31.38125
1888 533 1 91 100 1 0 55.98631
1891 1969 0 93 73 1 1 52.72553
1894 1962 0 127 90 1 1 38.09172
1895 207 1 NA 234 1 1 58.17112
1897 1969 0 103 50 1 1 45.21013
1900 1940 0 104 43 1 1 37.79877
1905 1597 1 55 108 1 1 60.65982
1909 1899 0 75 22 1 1 35.53457
1910 1885 0 144 73 1 1 43.06639
1915 1885 0 64 31 1 1 56.39151
1917 1818 0 59 52 1 1 30.57358
1922 1822 0 113 24 1 1 61.18275
1927 1663 0 82 41 1 1 58.29979
1932 1608 0 100 39 1 1 62.33265
1937 1508 0 88 69 1 1 37.99863
1941 1457 0 149 186 1 1 33.15264
abs(age - copper) log(trig)
1 NA NA
3 NA NA
12 NA NA
16 NA NA
23 NA NA
29 NA NA
35 NA NA
42 NA NA
50 NA NA
57 NA NA
58 NA NA
70 NA NA
72 NA NA
84 NA NA
91 NA NA
102 NA NA
115 NA NA
118 NA NA
119 NA NA
134 NA NA
138 NA NA
150 NA NA
153 NA NA
155 NA NA
168 NA NA
180 NA NA
186 NA NA
187 NA NA
190 NA NA
200 NA NA
203 NA NA
215 NA NA
231 NA NA
241 NA NA
255 NA NA
259 NA NA
270 NA NA
272 NA NA
282 NA NA
290 NA NA
305 NA NA
309 NA NA
325 NA NA
340 NA NA
351 NA NA
360 NA NA
368 NA NA
375 NA NA
384 NA NA
388 NA NA
397 NA NA
407 NA NA
416 NA NA
419 NA NA
424 NA NA
430 NA NA
436 NA NA
447 NA NA
463 NA NA
470 NA NA
483 NA NA
497 NA NA
507 NA NA
510 NA NA
516 NA NA
522 NA NA
535 NA NA
545 NA NA
559 NA NA
563 NA NA
576 NA NA
587 NA NA
593 NA NA
608 NA NA
612 NA NA
617 NA NA
618 NA NA
620 NA NA
624 NA NA
635 NA NA
639 NA NA
649 NA NA
659 NA NA
674 NA NA
677 NA NA
688 NA NA
689 NA NA
691 NA NA
695 NA NA
699 NA NA
708 NA NA
711 NA NA
712 NA NA
727 NA NA
730 NA NA
731 NA NA
745 NA NA
749 NA NA
763 NA NA
776 NA NA
780 NA NA
792 NA NA
804 NA NA
805 NA NA
815 NA NA
825 NA NA
830 NA NA
841 NA NA
849 NA NA
859 NA NA
866 NA NA
874 NA NA
885 NA NA
890 NA NA
901 NA NA
913 NA NA
924 NA NA
929 NA NA
936 NA NA
938 NA NA
946 NA NA
947 NA NA
957 NA NA
959 NA NA
960 NA NA
969 NA NA
974 NA NA
985 NA NA
990 NA NA
1002 NA NA
1008 NA NA
1012 NA NA
1018 NA NA
1027 NA NA
1039 NA NA
1049 NA NA
1058 NA NA
1070 NA NA
1076 NA NA
1082 NA NA
1094 NA NA
1097 NA NA
1106 NA NA
1109 NA NA
1113 NA NA
1117 NA NA
1124 NA NA
1129 NA NA
1135 NA NA
1138 NA NA
1147 NA NA
1158 NA NA
1161 NA NA
1171 NA NA
1172 NA NA
1175 NA NA
1179 NA NA
1190 NA NA
1199 NA NA
1203 NA NA
1211 NA NA
1222 NA NA
1223 NA NA
1226 NA NA
1227 NA NA
1232 NA NA
1240 NA NA
1246 NA NA
1256 NA NA
1261 NA NA
1262 NA NA
1272 NA NA
1279 NA NA
1288 NA NA
1298 NA NA
1308 NA NA
1314 NA NA
1315 NA NA
1321 NA NA
1324 NA NA
1334 NA NA
1335 NA NA
1340 NA NA
1348 NA NA
1353 NA NA
1358 NA NA
1362 NA NA
1366 NA NA
1374 NA NA
1379 NA NA
1383 NA NA
1384 NA NA
1394 NA NA
1398 NA NA
1403 NA NA
1404 NA NA
1412 NA NA
1421 NA NA
1429 NA NA
1432 NA NA
1443 NA NA
1453 NA NA
1455 NA NA
1464 NA NA
1472 NA NA
1477 NA NA
1485 NA NA
1492 NA NA
1495 NA NA
1497 NA NA
1507 NA NA
1515 NA NA
1521 NA NA
1523 NA NA
1527 NA NA
1531 NA NA
1538 NA NA
1542 NA NA
1552 NA NA
1558 NA NA
1563 NA NA
1571 NA NA
1575 NA NA
1576 NA NA
1584 NA NA
1593 NA NA
1602 NA NA
1606 NA NA
1610 NA NA
1613 NA NA
1617 NA NA
1621 NA NA
1625 NA NA
1626 NA NA
1628 NA NA
1630 NA NA
1632 NA NA
1634 NA NA
1637 NA NA
1640 NA NA
1648 NA NA
1652 NA NA
1660 NA NA
1662 NA NA
1666 NA NA
1668 NA NA
1674 NA NA
1678 NA NA
1686 NA NA
1689 NA NA
1691 NA NA
1692 NA NA
1695 NA NA
1703 NA NA
1707 NA NA
1713 NA NA
1719 NA NA
1722 NA NA
1730 NA NA
1735 NA NA
1736 NA NA
1742 NA NA
1745 NA NA
1750 NA NA
1754 NA NA
1758 NA NA
1765 NA NA
1766 NA NA
1768 NA NA
1775 NA NA
1778 NA NA
1786 NA NA
1792 NA NA
1795 NA NA
1800 NA NA
1804 NA NA
1806 NA NA
1811 NA NA
1816 NA NA
1823 NA NA
1830 NA NA
1831 NA NA
1835 NA NA
1839 NA NA
1846 NA NA
1847 NA NA
1849 NA NA
1853 NA NA
1855 NA NA
1859 NA NA
1866 NA NA
1871 NA NA
1874 NA NA
1876 NA NA
1883 NA NA
1885 NA NA
1888 NA NA
1891 NA NA
1894 NA NA
1895 NA NA
1897 NA NA
1900 NA NA
1905 NA NA
1909 NA NA
1910 NA NA
1915 NA NA
1917 NA NA
1922 NA NA
1927 NA NA
1932 NA NA
1937 NA NA
1941 NA NA
$m3a$spM_lvlone
center scale
futime 2341.641026 1301.272424
status != "censored" NA NA
trig 124.702128 65.148639
copper 97.648387 85.613920
(Intercept) NA NA
sexfemale NA NA
age 50.019007 10.581261
abs(age - copper) 61.603707 76.189619
log(trig) 4.720406 0.447384
$m3a$mu_reg_norm
[1] 0
$m3a$tau_reg_norm
[1] 1e-04
$m3a$shape_tau_norm
[1] 0.01
$m3a$rate_tau_norm
[1] 0.01
$m3a$mu_reg_surv
[1] 0
$m3a$tau_reg_surv
[1] 0.001
$m3a$cens_Srv_ftm_stts_cn
1 3 12 16 23 29 35 42 50 57 58 70 72 84 91 102
0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1
115 118 119 134 138 150 153 155 168 180 186 187 190 200 203 215
0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1
231 241 255 259 270 272 282 290 305 309 325 340 351 360 368 375
0 1 0 1 0 0 0 1 0 1 1 0 1 0 1 1
384 388 397 407 416 419 424 430 436 447 463 470 483 497 507 510
0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0
516 522 535 545 559 563 576 587 593 608 612 617 618 620 624 635
1 0 0 1 0 0 1 1 1 0 0 0 0 0 1 0
639 649 659 674 677 688 689 691 695 699 708 711 712 727 730 731
0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1
745 749 763 776 780 792 804 805 815 825 830 841 849 859 866 874
0 1 1 0 1 1 0 0 0 0 1 0 1 0 0 0
885 890 901 913 924 929 936 938 946 947 957 959 960 969 974 985
0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0
990 1002 1008 1012 1018 1027 1039 1049 1058 1070 1076 1082 1094 1097 1106 1109
1 0 0 1 0 1 1 1 1 0 0 1 1 0 0 0
1113 1117 1124 1129 1135 1138 1147 1158 1161 1171 1172 1175 1179 1190 1199 1203
1 0 0 0 0 1 1 0 1 0 1 0 1 0 0 1
1211 1222 1223 1226 1227 1232 1240 1246 1256 1261 1262 1272 1279 1288 1298 1308
1 0 0 0 0 1 0 1 0 1 1 1 1 1 1 0
1314 1315 1321 1324 1334 1335 1340 1348 1353 1358 1362 1366 1374 1379 1383 1384
1 1 1 1 1 1 0 0 0 0 0 1 1 1 0 1
1394 1398 1403 1404 1412 1421 1429 1432 1443 1453 1455 1464 1472 1477 1485 1492
0 1 1 0 1 1 1 0 1 1 1 0 0 1 1 0
1495 1497 1507 1515 1521 1523 1527 1531 1538 1542 1552 1558 1563 1571 1575 1576
1 1 1 1 1 0 0 1 0 1 1 0 1 0 0 1
1584 1593 1602 1606 1610 1613 1617 1621 1625 1626 1628 1630 1632 1634 1637 1640
1 1 0 1 0 1 0 1 1 1 0 1 0 0 0 1
1648 1652 1660 1662 1666 1668 1674 1678 1686 1689 1691 1692 1695 1703 1707 1713
0 0 0 0 1 0 0 1 1 0 1 1 1 0 1 1
1719 1722 1730 1735 1736 1742 1745 1750 1754 1758 1765 1766 1768 1775 1778 1786
1 1 1 0 1 0 0 0 0 1 0 0 0 0 1 1
1792 1795 1800 1804 1806 1811 1816 1823 1830 1831 1835 1839 1846 1847 1849 1853
0 0 1 1 1 0 1 1 0 0 1 1 1 1 1 0
1855 1859 1866 1871 1874 1876 1883 1885 1888 1891 1894 1895 1897 1900 1905 1909
0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 1
1910 1915 1917 1922 1927 1932 1937 1941
1 1 1 1 1 1 1 1
$m3a$Srv_ftm_stts_cn
[1] 400 NA 1012 1925 1505 2503 NA 2466 2400 51 3762 304 NA 1217 3584
[16] NA 769 132 NA 1356 3657 673 264 4079 NA 1444 77 549 5074 321
[31] 3839 NA 3170 NA 2847 NA 223 3244 2297 NA 1350 NA NA 3428 NA
[46] 2256 NA NA 708 2598 3853 2386 1000 1434 1360 1847 3282 NA 2224 NA
[61] NA 3090 859 1487 NA 4191 2769 NA 1170 3683 NA NA NA 1827 1191
[76] 71 326 1690 NA 890 2540 3574 NA NA 3358 1657 198 3076 1741 2689
[91] 460 389 NA 750 137 NA 620 NA NA 552 NA NA 110 3086 3092
[106] 3222 NA 2583 NA 2044 2350 3445 951 3395 NA NA 1083 2288 515 2033
[121] 191 NA 971 3903 2468 824 NA 1037 NA 1411 850 NA 2796 NA NA
[136] NA NA 1297 2357 NA NA 2419 786 945 NA 3086 3382 1427 762 NA
[151] NA 1152 NA 140 NA 853 NA 2475 1536 NA NA 186 2055 276 1076
[166] NA 1684 NA 1212 NA NA NA NA NA NA 1492 NA NA NA NA
[181] NA NA 2241 974 2882 1576 733 NA NA NA 216 NA 797 NA NA
[196] 2555 NA NA NA 2932 NA NA NA 2090 2081 NA NA 904 NA NA
[211] NA NA NA 1786 1080 NA 790 NA NA 1235 NA 597 334 NA NA
[226] NA 999 NA 348 NA 1165 NA NA NA 2302 NA 1947 1874 694 NA
[241] 837 2128 930 1690 NA 1435 940 NA NA 2327 NA NA NA 737 NA
[256] NA NA NA NA 1674 NA 1850 1303 1542 1084 NA 179 1191 1898 2010
[271] NA NA 1649 1447 NA NA NA 1996 NA NA 41 1673 NA NA NA
[286] NA NA 1067 799 NA 901 1785 NA NA 875 NA 533 NA NA 207
[301] NA NA 1597 NA NA NA NA NA NA NA NA NA
$m3b
$m3b$M_center
(Intercept)
1 1
203 1
483 1
708 1
946 1
1147 1
1334 1
1507 1
1648 1
1778 1
$m3b$M_lvlone
futime status != "censored" trig copper sexfemale age
1 400 1 172 156 1 58.76523
3 5169 0 88 54 1 56.44627
12 1012 1 55 210 0 70.07255
16 1925 1 92 64 1 54.74059
23 1505 1 72 143 1 38.10541
29 2503 1 63 50 1 66.25873
35 2501 0 213 52 1 55.53457
42 2466 1 189 52 1 53.05681
50 2400 1 88 79 1 42.50787
57 51 1 143 140 1 70.55989
58 3762 1 79 46 1 53.71389
70 304 1 95 94 1 59.13758
72 4247 0 130 40 1 45.68925
84 1217 1 NA 43 0 56.22177
91 3584 1 96 173 1 64.64613
102 4345 0 58 28 1 40.44353
115 769 1 128 159 1 52.18344
118 132 1 200 588 1 53.93018
119 4901 0 123 39 1 49.56057
134 1356 1 135 140 1 59.95346
138 3657 1 83 41 0 64.18891
150 673 1 55 464 1 56.27652
153 264 1 191 558 1 55.96715
155 4079 1 230 124 0 44.52019
168 4796 0 66 40 1 45.07324
180 1444 1 166 53 1 52.02464
186 77 1 168 221 1 54.43943
187 549 1 195 209 1 44.94730
190 5074 1 86 24 1 63.87680
200 321 1 158 172 1 41.38535
203 3839 1 101 114 1 41.55236
215 5192 0 158 101 1 53.99589
231 3170 1 113 82 1 51.28268
241 4602 0 64 37 1 52.06023
255 2847 1 151 201 1 48.61875
259 4281 0 96 18 1 56.41068
270 223 1 118 150 1 61.72758
272 3244 1 87 102 1 36.62697
282 2297 1 111 52 1 55.39220
290 5136 0 NA 105 1 46.66940
305 1350 1 NA 96 1 33.63450
309 5122 0 NA 122 1 33.69473
325 5225 0 89 36 1 48.87064
340 3428 1 50 131 1 37.58248
351 4694 0 NA 19 1 41.79329
360 2256 1 165 161 1 45.79877
368 3245 0 71 68 1 47.42779
375 5096 0 178 281 0 49.13621
384 708 1 NA 58 1 61.15264
388 2598 1 73 43 1 53.50856
397 3853 1 143 54 1 52.08761
407 2386 1 93 158 0 50.54073
416 1000 1 NA 94 1 67.40862
419 1434 1 125 262 1 39.19781
424 1360 1 219 121 0 65.76318
430 1847 1 319 88 1 33.61807
436 3282 1 94 231 1 53.57153
447 5128 0 118 73 0 44.56947
463 2224 1 124 49 1 40.39425
470 5034 0 58 82 1 58.38193
483 4925 0 188 28 0 43.89870
497 3090 1 112 58 1 60.70637
507 859 1 133 95 1 46.62834
510 1487 1 188 52 1 62.90760
516 4842 0 108 74 1 40.20260
522 4191 1 171 105 0 46.45311
535 2769 1 185 84 1 51.28816
545 4708 0 46 58 1 32.61328
559 1170 1 184 159 1 49.33881
563 3683 1 NA 20 1 56.39973
576 4865 0 76 79 1 48.84600
587 4853 0 80 51 1 32.49281
593 4859 0 56 17 1 38.49418
608 1827 1 309 280 1 51.92060
612 1191 1 598 207 1 43.51814
617 71 1 243 111 1 51.94251
618 326 1 91 199 1 49.82615
620 1690 1 104 75 1 47.94524
624 4376 0 70 13 1 46.51608
635 890 1 91 269 0 67.41136
639 2540 1 318 34 1 63.26352
649 3574 1 272 154 1 67.31006
659 4719 0 100 48 1 56.01369
674 4701 0 74 29 1 55.83025
677 3358 1 84 58 1 47.21697
688 1657 1 174 75 1 52.75838
689 198 1 72 75 1 37.27858
691 3076 1 83 37 1 41.39357
695 1741 1 98 50 1 52.44353
699 2689 1 155 94 0 33.47570
708 460 1 56 110 1 45.60712
711 389 1 70 36 1 76.70910
712 4583 0 68 73 1 36.53388
727 750 1 69 178 1 53.91650
730 137 1 NA 182 1 46.39014
731 4520 0 NA 33 1 48.84600
745 620 1 91 62 0 71.89322
749 4492 0 101 77 1 28.88433
763 4489 0 118 148 0 48.46817
776 552 1 122 145 0 51.46886
780 4250 0 166 31 1 44.95003
792 3770 0 84 172 1 56.56947
804 110 1 102 57 1 48.96372
805 3086 1 53 20 1 43.01711
815 3092 1 102 38 1 34.03970
825 3222 1 NA 50 1 68.50924
830 4058 0 77 10 1 62.52156
841 2583 1 84 14 1 50.35729
849 3173 0 52 53 1 44.06297
859 2044 1 109 74 1 38.91034
866 2350 1 78 77 1 41.15264
874 3445 1 90 89 1 55.45791
885 951 1 100 103 1 51.23340
890 3395 1 78 208 0 52.82683
901 4091 0 156 81 1 42.63929
913 4015 0 382 74 1 61.07050
924 1083 1 149 111 1 49.65640
929 2288 1 93 67 1 48.85421
936 515 1 114 129 1 54.25599
938 2033 1 210 444 0 35.15127
946 191 1 49 73 0 67.90691
947 3966 0 85 29 1 55.43600
957 971 1 NA 18 1 45.82067
959 3903 1 107 25 0 52.88980
960 2468 1 137 75 1 47.18138
969 824 1 68 NA 1 53.59890
974 3924 0 95 9 1 44.10404
985 1037 1 NA 42 1 41.94935
990 3908 0 232 30 1 63.61396
1002 1411 1 97 205 1 44.22724
1008 850 1 104 74 1 62.00137
1012 3613 0 175 60 1 40.55305
1018 2796 1 99 13 0 62.64476
1027 3818 0 105 35 1 42.33539
1039 3819 0 121 123 1 42.96783
1049 3767 0 69 27 1 55.96167
1058 3659 0 152 79 1 62.86105
1070 1297 1 218 262 0 51.24983
1076 2357 1 134 159 1 46.76249
1082 3728 0 146 38 1 54.07529
1094 3719 0 56 59 1 47.03628
1097 2419 1 88 20 1 55.72621
1106 786 1 103 86 1 46.10267
1109 945 1 171 152 1 52.28747
1113 3645 0 106 96 1 51.20055
1117 3086 1 NA 58 1 33.86448
1124 3382 1 114 32 1 75.01164
1129 1427 1 280 247 1 30.86379
1135 762 1 112 290 0 61.80424
1138 3560 0 NA 57 1 34.98700
1147 3539 0 174 57 1 55.04175
1158 1152 1 126 243 0 69.94114
1161 3532 0 68 68 1 49.60438
1171 140 1 75 225 0 69.37714
1172 3516 0 80 57 1 43.55647
1175 853 1 205 219 1 59.40862
1179 3504 0 206 34 1 48.75838
1190 2475 1 118 32 1 36.49281
1199 1536 1 140 217 0 45.76044
1203 3441 0 119 13 1 57.37166
1211 3466 0 180 4 1 42.74333
1222 186 1 101 91 1 58.81725
1223 2055 1 68 20 1 53.49760
1226 276 1 NA 161 1 43.41410
1227 1076 1 142 80 0 53.30595
1232 3390 0 108 84 1 41.35524
1240 1684 1 154 200 0 60.95825
1246 3384 0 110 44 1 47.75359
1256 1212 1 137 67 1 35.49076
1261 3361 0 52 32 1 48.66256
1262 3243 0 NA 31 1 52.66804
1272 2970 0 126 65 1 49.86995
1279 3326 0 86 76 1 30.27515
1288 3313 0 NA 63 1 55.56742
1298 3293 0 168 152 1 52.15332
1308 1492 1 NA 77 1 41.60986
1314 3278 0 133 49 1 55.45243
1315 3249 0 NA 51 1 70.00411
1321 3242 0 133 39 1 43.94251
1324 3232 0 76 44 1 42.56810
1334 3225 0 106 63 1 44.56947
1335 3224 0 NA 161 1 56.94456
1340 2241 1 145 44 1 40.26010
1348 974 1 140 358 1 37.60712
1353 2882 1 94 42 1 48.36140
1358 1576 1 62 51 1 70.83641
1362 733 1 137 251 1 35.79192
1366 2635 0 98 41 1 62.62286
1374 3125 0 164 52 1 50.64750
1379 3173 0 NA 24 1 54.52704
1383 216 1 432 233 1 52.69268
1384 3112 0 101 67 1 52.72005
1394 797 1 157 267 1 56.77207
1398 3118 0 56 50 1 44.39699
1403 2999 0 90 76 1 29.55510
1404 2555 1 62 13 1 57.04038
1412 3034 0 102 25 1 44.62697
1421 3025 0 90 38 1 35.79740
1429 2991 0 194 52 1 40.71732
1432 2932 1 114 112 1 32.23272
1443 2963 0 67 26 1 41.09240
1453 2941 0 89 15 1 61.63997
1455 2890 0 57 227 1 37.05681
1464 2090 1 58 23 1 62.57906
1472 2081 1 NA 108 1 48.97741
1477 2924 0 68 12 1 61.99042
1485 2840 0 NA 14 1 72.77207
1492 904 1 131 58 1 61.29500
1495 2888 0 58 11 1 52.62423
1497 2893 0 90 81 0 49.76318
1507 2865 0 NA 27 1 52.91444
1515 2845 0 99 11 1 47.26352
1521 2189 0 55 9 1 50.20397
1523 1786 1 224 34 1 69.34702
1527 1080 1 157 141 1 41.16906
1531 2336 0 NA 20 1 59.16496
1538 790 1 210 65 1 36.07940
1542 2839 0 NA 29 1 34.59548
1552 2826 0 85 67 1 42.71321
1558 1235 1 113 96 1 63.63039
1563 2719 0 154 72 1 56.62971
1571 597 1 135 227 1 46.26420
1575 334 1 155 123 1 61.24298
1576 2614 0 101 67 1 38.62012
1584 2691 0 135 108 1 38.77070
1593 2647 0 104 39 1 56.69541
1602 999 1 63 172 0 58.95140
1606 2636 0 74 41 1 36.92266
1610 348 1 118 200 1 62.41478
1613 2648 0 44 51 1 34.60917
1617 1165 1 90 115 1 58.33539
1621 2620 0 87 47 1 50.18207
1625 2601 0 87 412 1 42.68583
1626 2445 0 177 78 1 34.37919
1628 2302 1 111 71 1 33.18275
1630 2577 0 106 67 1 38.38193
1632 1947 1 108 105 1 59.76181
1634 1874 1 81 NA 1 66.41205
1637 694 1 99 231 1 46.78987
1640 2500 0 139 24 1 56.07940
1648 837 1 322 308 1 41.37440
1652 2128 1 152 86 1 64.57221
1660 930 1 139 139 1 67.48802
1662 1690 1 231 121 1 44.82957
1666 2459 0 214 48 1 45.77139
1668 1435 1 33 63 1 32.95003
1674 940 1 139 89 1 41.22108
1678 2454 0 146 39 1 55.41684
1686 2452 0 71 35 1 47.98084
1689 2327 1 85 23 1 40.79124
1691 2307 0 120 40 1 56.97467
1692 2439 0 90 116 1 68.46270
1695 2434 0 154 380 0 78.43943
1703 737 1 91 121 1 39.85763
1707 2405 0 118 35 1 35.31006
1713 2370 0 108 48 1 31.44422
1719 2283 0 146 36 1 58.26420
1722 2371 0 55 80 1 51.48802
1730 2284 0 126 42 1 59.96988
1735 1674 1 128 188 0 74.52430
1736 2348 0 NA 129 1 52.36413
1742 1850 1 209 65 1 42.78713
1745 1303 1 77 45 1 34.87474
1750 1542 1 130 188 1 44.13963
1754 1084 1 146 121 1 46.38193
1758 2287 0 260 52 1 56.30938
1765 179 1 91 138 1 70.90760
1766 1191 1 179 16 1 55.39493
1768 1898 1 44 143 1 45.08419
1775 2010 1 117 102 1 26.27789
1778 2238 0 177 94 1 50.47228
1786 2194 0 85 22 1 38.39836
1792 1649 1 116 75 1 47.41958
1795 1447 1 NA 136 1 47.98084
1800 2020 0 114 77 1 38.31622
1804 2147 0 108 67 1 50.10815
1806 2105 0 111 44 1 35.08830
1811 1996 1 193 130 1 32.50376
1816 2102 0 56 38 1 56.15332
1823 2081 0 140 69 1 46.15469
1830 41 1 229 220 1 65.88364
1831 1673 1 169 97 1 33.94387
1835 2095 0 113 103 1 62.86105
1839 2087 0 195 75 1 48.56400
1846 2070 0 80 65 1 46.34908
1847 2077 0 118 70 1 38.85284
1849 1552 0 151 155 1 58.64750
1853 1067 1 242 107 1 48.93634
1855 799 1 242 177 0 67.57290
1859 2032 0 69 33 1 65.98494
1866 901 1 84 123 1 40.90075
1871 1785 1 153 84 0 50.24504
1874 1989 0 78 196 1 57.19644
1876 1971 0 133 88 0 60.53662
1883 875 1 194 102 0 35.35113
1885 1990 0 95 62 1 31.38125
1888 533 1 91 100 0 55.98631
1891 1969 0 93 73 1 52.72553
1894 1962 0 127 90 1 38.09172
1895 207 1 NA 234 1 58.17112
1897 1969 0 103 50 1 45.21013
1900 1940 0 104 43 1 37.79877
1905 1597 1 55 108 1 60.65982
1909 1899 0 75 22 1 35.53457
1910 1885 0 144 73 1 43.06639
1915 1885 0 64 31 1 56.39151
1917 1818 0 59 52 1 30.57358
1922 1822 0 113 24 1 61.18275
1927 1663 0 82 41 1 58.29979
1932 1608 0 100 39 1 62.33265
1937 1508 0 88 69 1 37.99863
1941 1457 0 149 186 1 33.15264
abs(age - copper) log(trig)
1 NA NA
3 NA NA
12 NA NA
16 NA NA
23 NA NA
29 NA NA
35 NA NA
42 NA NA
50 NA NA
57 NA NA
58 NA NA
70 NA NA
72 NA NA
84 NA NA
91 NA NA
102 NA NA
115 NA NA
118 NA NA
119 NA NA
134 NA NA
138 NA NA
150 NA NA
153 NA NA
155 NA NA
168 NA NA
180 NA NA
186 NA NA
187 NA NA
190 NA NA
200 NA NA
203 NA NA
215 NA NA
231 NA NA
241 NA NA
255 NA NA
259 NA NA
270 NA NA
272 NA NA
282 NA NA
290 NA NA
305 NA NA
309 NA NA
325 NA NA
340 NA NA
351 NA NA
360 NA NA
368 NA NA
375 NA NA
384 NA NA
388 NA NA
397 NA NA
407 NA NA
416 NA NA
419 NA NA
424 NA NA
430 NA NA
436 NA NA
447 NA NA
463 NA NA
470 NA NA
483 NA NA
497 NA NA
507 NA NA
510 NA NA
516 NA NA
522 NA NA
535 NA NA
545 NA NA
559 NA NA
563 NA NA
576 NA NA
587 NA NA
593 NA NA
608 NA NA
612 NA NA
617 NA NA
618 NA NA
620 NA NA
624 NA NA
635 NA NA
639 NA NA
649 NA NA
659 NA NA
674 NA NA
677 NA NA
688 NA NA
689 NA NA
691 NA NA
695 NA NA
699 NA NA
708 NA NA
711 NA NA
712 NA NA
727 NA NA
730 NA NA
731 NA NA
745 NA NA
749 NA NA
763 NA NA
776 NA NA
780 NA NA
792 NA NA
804 NA NA
805 NA NA
815 NA NA
825 NA NA
830 NA NA
841 NA NA
849 NA NA
859 NA NA
866 NA NA
874 NA NA
885 NA NA
890 NA NA
901 NA NA
913 NA NA
924 NA NA
929 NA NA
936 NA NA
938 NA NA
946 NA NA
947 NA NA
957 NA NA
959 NA NA
960 NA NA
969 NA NA
974 NA NA
985 NA NA
990 NA NA
1002 NA NA
1008 NA NA
1012 NA NA
1018 NA NA
1027 NA NA
1039 NA NA
1049 NA NA
1058 NA NA
1070 NA NA
1076 NA NA
1082 NA NA
1094 NA NA
1097 NA NA
1106 NA NA
1109 NA NA
1113 NA NA
1117 NA NA
1124 NA NA
1129 NA NA
1135 NA NA
1138 NA NA
1147 NA NA
1158 NA NA
1161 NA NA
1171 NA NA
1172 NA NA
1175 NA NA
1179 NA NA
1190 NA NA
1199 NA NA
1203 NA NA
1211 NA NA
1222 NA NA
1223 NA NA
1226 NA NA
1227 NA NA
1232 NA NA
1240 NA NA
1246 NA NA
1256 NA NA
1261 NA NA
1262 NA NA
1272 NA NA
1279 NA NA
1288 NA NA
1298 NA NA
1308 NA NA
1314 NA NA
1315 NA NA
1321 NA NA
1324 NA NA
1334 NA NA
1335 NA NA
1340 NA NA
1348 NA NA
1353 NA NA
1358 NA NA
1362 NA NA
1366 NA NA
1374 NA NA
1379 NA NA
1383 NA NA
1384 NA NA
1394 NA NA
1398 NA NA
1403 NA NA
1404 NA NA
1412 NA NA
1421 NA NA
1429 NA NA
1432 NA NA
1443 NA NA
1453 NA NA
1455 NA NA
1464 NA NA
1472 NA NA
1477 NA NA
1485 NA NA
1492 NA NA
1495 NA NA
1497 NA NA
1507 NA NA
1515 NA NA
1521 NA NA
1523 NA NA
1527 NA NA
1531 NA NA
1538 NA NA
1542 NA NA
1552 NA NA
1558 NA NA
1563 NA NA
1571 NA NA
1575 NA NA
1576 NA NA
1584 NA NA
1593 NA NA
1602 NA NA
1606 NA NA
1610 NA NA
1613 NA NA
1617 NA NA
1621 NA NA
1625 NA NA
1626 NA NA
1628 NA NA
1630 NA NA
1632 NA NA
1634 NA NA
1637 NA NA
1640 NA NA
1648 NA NA
1652 NA NA
1660 NA NA
1662 NA NA
1666 NA NA
1668 NA NA
1674 NA NA
1678 NA NA
1686 NA NA
1689 NA NA
1691 NA NA
1692 NA NA
1695 NA NA
1703 NA NA
1707 NA NA
1713 NA NA
1719 NA NA
1722 NA NA
1730 NA NA
1735 NA NA
1736 NA NA
1742 NA NA
1745 NA NA
1750 NA NA
1754 NA NA
1758 NA NA
1765 NA NA
1766 NA NA
1768 NA NA
1775 NA NA
1778 NA NA
1786 NA NA
1792 NA NA
1795 NA NA
1800 NA NA
1804 NA NA
1806 NA NA
1811 NA NA
1816 NA NA
1823 NA NA
1830 NA NA
1831 NA NA
1835 NA NA
1839 NA NA
1846 NA NA
1847 NA NA
1849 NA NA
1853 NA NA
1855 NA NA
1859 NA NA
1866 NA NA
1871 NA NA
1874 NA NA
1876 NA NA
1883 NA NA
1885 NA NA
1888 NA NA
1891 NA NA
1894 NA NA
1895 NA NA
1897 NA NA
1900 NA NA
1905 NA NA
1909 NA NA
1910 NA NA
1915 NA NA
1917 NA NA
1922 NA NA
1927 NA NA
1932 NA NA
1937 NA NA
1941 NA NA
$m3b$spM_lvlone
center scale
futime 2341.641026 1301.272424
status != "censored" NA NA
trig 124.702128 65.148639
copper 97.648387 85.613920
sexfemale NA NA
age 50.019007 10.581261
abs(age - copper) 61.603707 76.189619
log(trig) 4.720406 0.447384
$m3b$mu_reg_norm
[1] 0
$m3b$tau_reg_norm
[1] 1e-04
$m3b$shape_tau_norm
[1] 0.01
$m3b$rate_tau_norm
[1] 0.01
$m3b$mu_reg_surv
[1] 0
$m3b$tau_reg_surv
[1] 0.001
$m3b$group_center
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[26] 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[51] 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[76] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4
[101] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5
[126] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[151] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
[176] 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
[201] 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
[226] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9
[251] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 10 10 10 10 10
[276] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[301] 10 10 10 10 10 10 10 10 10 10 10 10
$m3b$shape_diag_RinvD
[1] "0.01"
$m3b$rate_diag_RinvD
[1] "0.001"
$m3b$cens_Srv_ftm_stts_cn
1 3 12 16 23 29 35 42 50 57 58 70 72 84 91 102
0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1
115 118 119 134 138 150 153 155 168 180 186 187 190 200 203 215
0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1
231 241 255 259 270 272 282 290 305 309 325 340 351 360 368 375
0 1 0 1 0 0 0 1 0 1 1 0 1 0 1 1
384 388 397 407 416 419 424 430 436 447 463 470 483 497 507 510
0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0
516 522 535 545 559 563 576 587 593 608 612 617 618 620 624 635
1 0 0 1 0 0 1 1 1 0 0 0 0 0 1 0
639 649 659 674 677 688 689 691 695 699 708 711 712 727 730 731
0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1
745 749 763 776 780 792 804 805 815 825 830 841 849 859 866 874
0 1 1 0 1 1 0 0 0 0 1 0 1 0 0 0
885 890 901 913 924 929 936 938 946 947 957 959 960 969 974 985
0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0
990 1002 1008 1012 1018 1027 1039 1049 1058 1070 1076 1082 1094 1097 1106 1109
1 0 0 1 0 1 1 1 1 0 0 1 1 0 0 0
1113 1117 1124 1129 1135 1138 1147 1158 1161 1171 1172 1175 1179 1190 1199 1203
1 0 0 0 0 1 1 0 1 0 1 0 1 0 0 1
1211 1222 1223 1226 1227 1232 1240 1246 1256 1261 1262 1272 1279 1288 1298 1308
1 0 0 0 0 1 0 1 0 1 1 1 1 1 1 0
1314 1315 1321 1324 1334 1335 1340 1348 1353 1358 1362 1366 1374 1379 1383 1384
1 1 1 1 1 1 0 0 0 0 0 1 1 1 0 1
1394 1398 1403 1404 1412 1421 1429 1432 1443 1453 1455 1464 1472 1477 1485 1492
0 1 1 0 1 1 1 0 1 1 1 0 0 1 1 0
1495 1497 1507 1515 1521 1523 1527 1531 1538 1542 1552 1558 1563 1571 1575 1576
1 1 1 1 1 0 0 1 0 1 1 0 1 0 0 1
1584 1593 1602 1606 1610 1613 1617 1621 1625 1626 1628 1630 1632 1634 1637 1640
1 1 0 1 0 1 0 1 1 1 0 1 0 0 0 1
1648 1652 1660 1662 1666 1668 1674 1678 1686 1689 1691 1692 1695 1703 1707 1713
0 0 0 0 1 0 0 1 1 0 1 1 1 0 1 1
1719 1722 1730 1735 1736 1742 1745 1750 1754 1758 1765 1766 1768 1775 1778 1786
1 1 1 0 1 0 0 0 0 1 0 0 0 0 1 1
1792 1795 1800 1804 1806 1811 1816 1823 1830 1831 1835 1839 1846 1847 1849 1853
0 0 1 1 1 0 1 1 0 0 1 1 1 1 1 0
1855 1859 1866 1871 1874 1876 1883 1885 1888 1891 1894 1895 1897 1900 1905 1909
0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 1
1910 1915 1917 1922 1927 1932 1937 1941
1 1 1 1 1 1 1 1
$m3b$Srv_ftm_stts_cn
[1] 400 NA 1012 1925 1505 2503 NA 2466 2400 51 3762 304 NA 1217 3584
[16] NA 769 132 NA 1356 3657 673 264 4079 NA 1444 77 549 5074 321
[31] 3839 NA 3170 NA 2847 NA 223 3244 2297 NA 1350 NA NA 3428 NA
[46] 2256 NA NA 708 2598 3853 2386 1000 1434 1360 1847 3282 NA 2224 NA
[61] NA 3090 859 1487 NA 4191 2769 NA 1170 3683 NA NA NA 1827 1191
[76] 71 326 1690 NA 890 2540 3574 NA NA 3358 1657 198 3076 1741 2689
[91] 460 389 NA 750 137 NA 620 NA NA 552 NA NA 110 3086 3092
[106] 3222 NA 2583 NA 2044 2350 3445 951 3395 NA NA 1083 2288 515 2033
[121] 191 NA 971 3903 2468 824 NA 1037 NA 1411 850 NA 2796 NA NA
[136] NA NA 1297 2357 NA NA 2419 786 945 NA 3086 3382 1427 762 NA
[151] NA 1152 NA 140 NA 853 NA 2475 1536 NA NA 186 2055 276 1076
[166] NA 1684 NA 1212 NA NA NA NA NA NA 1492 NA NA NA NA
[181] NA NA 2241 974 2882 1576 733 NA NA NA 216 NA 797 NA NA
[196] 2555 NA NA NA 2932 NA NA NA 2090 2081 NA NA 904 NA NA
[211] NA NA NA 1786 1080 NA 790 NA NA 1235 NA 597 334 NA NA
[226] NA 999 NA 348 NA 1165 NA NA NA 2302 NA 1947 1874 694 NA
[241] 837 2128 930 1690 NA 1435 940 NA NA 2327 NA NA NA 737 NA
[256] NA NA NA NA 1674 NA 1850 1303 1542 1084 NA 179 1191 1898 2010
[271] NA NA 1649 1447 NA NA NA 1996 NA NA 41 1673 NA NA NA
[286] NA NA 1067 799 NA 901 1785 NA NA 875 NA 533 NA NA 207
[301] NA NA 1597 NA NA NA NA NA NA NA NA NA
jagsmodel remains the same
Code
lapply(models, "[[", "jagsmodel")
Output
$m0a
model {
# Weibull survival model for Srv_ftm_stts_cn ------------------------------------
for (i in 1:312) {
Srv_ftm_stts_cn[i] ~ dgen.gamma(1, rate_Srv_ftm_stts_cn[i], shape_Srv_ftm_stts_cn)
cens_Srv_ftm_stts_cn[i] ~ dinterval(Srv_ftm_stts_cn[i], M_lvlone[i, 1])
log(rate_Srv_ftm_stts_cn[i]) <- -(M_lvlone[i, 3] * beta[1])
}
# Priors for the model for Srv_ftm_stts_cn
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
shape_Srv_ftm_stts_cn ~ dexp(0.01)
}
$m1a
model {
# Weibull survival model for Srv_ftm_stts_cn ------------------------------------
for (i in 1:312) {
Srv_ftm_stts_cn[i] ~ dgen.gamma(1, rate_Srv_ftm_stts_cn[i], shape_Srv_ftm_stts_cn)
cens_Srv_ftm_stts_cn[i] ~ dinterval(Srv_ftm_stts_cn[i], M_lvlone[i, 1])
log(rate_Srv_ftm_stts_cn[i]) <- -(M_lvlone[i, 3] * beta[1] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[2] +
M_lvlone[i, 5] * beta[3])
}
# Priors for the model for Srv_ftm_stts_cn
for (k in 1:3) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
shape_Srv_ftm_stts_cn ~ dexp(0.01)
}
$m1b
model {
# Weibull survival model for Srv_ftm_stts_cn ------------------------------------
for (i in 1:312) {
Srv_ftm_stts_cn[i] ~ dgen.gamma(1, rate_Srv_ftm_stts_cn[i], shape_Srv_ftm_stts_cn)
cens_Srv_ftm_stts_cn[i] ~ dinterval(Srv_ftm_stts_cn[i], M_lvlone[i, 1])
log(rate_Srv_ftm_stts_cn[i]) <- -(M_lvlone[i, 3] * beta[1] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[2] +
M_lvlone[i, 5] * beta[3])
}
# Priors for the model for Srv_ftm_stts_cn
for (k in 1:3) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
shape_Srv_ftm_stts_cn ~ dexp(0.01)
}
$m2a
model {
# Weibull survival model for Srv_ftm_stts_cn ------------------------------------
for (i in 1:312) {
Srv_ftm_stts_cn[i] ~ dgen.gamma(1, rate_Srv_ftm_stts_cn[i], shape_Srv_ftm_stts_cn)
cens_Srv_ftm_stts_cn[i] ~ dinterval(Srv_ftm_stts_cn[i], M_lvlone[i, 1])
log(rate_Srv_ftm_stts_cn[i]) <- -(M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2])
}
# Priors for the model for Srv_ftm_stts_cn
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
shape_Srv_ftm_stts_cn ~ dexp(0.01)
# Normal model for copper -------------------------------------------------------
for (i in 1:312) {
M_lvlone[i, 3] ~ dnorm(mu_copper[i], tau_copper)
mu_copper[i] <- M_lvlone[i, 4] * alpha[1]
}
# Priors for the model for copper
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_copper ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_copper <- sqrt(1/tau_copper)
}
$m3a
model {
# Weibull survival model for Srv_ftm_stts_cn ------------------------------------
for (i in 1:312) {
Srv_ftm_stts_cn[i] ~ dgen.gamma(1, rate_Srv_ftm_stts_cn[i], shape_Srv_ftm_stts_cn)
cens_Srv_ftm_stts_cn[i] ~ dinterval(Srv_ftm_stts_cn[i], M_lvlone[i, 1])
log(rate_Srv_ftm_stts_cn[i]) <- -(M_lvlone[i, 5] * beta[1] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[2] +
M_lvlone[i, 6] * beta[3] +
(M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[4] +
(M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[5] +
(M_lvlone[i, 9] - spM_lvlone[9, 1])/spM_lvlone[9, 2] * beta[6])
}
# Priors for the model for Srv_ftm_stts_cn
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
shape_Srv_ftm_stts_cn ~ dexp(0.01)
# Normal model for trig ---------------------------------------------------------
for (i in 1:312) {
M_lvlone[i, 3] ~ dnorm(mu_trig[i], tau_trig)T(1e-04, )
mu_trig[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] +
(M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * alpha[4]
M_lvlone[i, 9] <- log(M_lvlone[i, 3])
}
# Priors for the model for trig
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_trig ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_trig <- sqrt(1/tau_trig)
# Normal model for copper -------------------------------------------------------
for (i in 1:312) {
M_lvlone[i, 4] ~ dnorm(mu_copper[i], tau_copper)
mu_copper[i] <- M_lvlone[i, 5] * alpha[5] + M_lvlone[i, 6] * alpha[6] +
(M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * alpha[7]
M_lvlone[i, 8] <- abs(M_lvlone[i, 7] - M_lvlone[i, 4])
}
# Priors for the model for copper
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_copper ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_copper <- sqrt(1/tau_copper)
}
$m3b
model {
# Weibull survival model for Srv_ftm_stts_cn ------------------------------------
for (i in 1:312) {
Srv_ftm_stts_cn[i] ~ dgen.gamma(1, rate_Srv_ftm_stts_cn[i], shape_Srv_ftm_stts_cn)
cens_Srv_ftm_stts_cn[i] ~ dinterval(Srv_ftm_stts_cn[i], M_lvlone[i, 1])
log(rate_Srv_ftm_stts_cn[i]) <- -(b_Srv_ftm_stts_cn_center[group_center[i], 1] +
beta[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[3] * M_lvlone[i, 5] +
beta[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] +
beta[5] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] +
beta[6] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2])
}
for (ii in 1:10) {
b_Srv_ftm_stts_cn_center[ii, 1:1] ~ dnorm(mu_b_Srv_ftm_stts_cn_center[ii, ], invD_Srv_ftm_stts_cn_center[ , ])
mu_b_Srv_ftm_stts_cn_center[ii, 1] <- M_center[ii, 1] * beta[1]
}
# Priors for the model for Srv_ftm_stts_cn
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
shape_Srv_ftm_stts_cn ~ dexp(0.01)
invD_Srv_ftm_stts_cn_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_Srv_ftm_stts_cn_center[1, 1] <- 1 / (invD_Srv_ftm_stts_cn_center[1, 1])
# Normal mixed effects model for trig -------------------------------------------
for (i in 1:312) {
M_lvlone[i, 3] ~ dnorm(mu_trig[i], tau_trig)T(1e-04, )
mu_trig[i] <- b_trig_center[group_center[i], 1] +
alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[3] * M_lvlone[i, 5] +
alpha[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
M_lvlone[i, 8] <- log(M_lvlone[i, 3])
}
for (ii in 1:10) {
b_trig_center[ii, 1:1] ~ dnorm(mu_b_trig_center[ii, ], invD_trig_center[ , ])
mu_b_trig_center[ii, 1] <- M_center[ii, 1] * alpha[1]
}
# Priors for the model for trig
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_trig ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_trig <- sqrt(1/tau_trig)
invD_trig_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_trig_center[1, 1] <- 1 / (invD_trig_center[1, 1])
# Normal mixed effects model for copper -----------------------------------------
for (i in 1:312) {
M_lvlone[i, 4] ~ dnorm(mu_copper[i], tau_copper)
mu_copper[i] <- b_copper_center[group_center[i], 1] + alpha[6] * M_lvlone[i, 5] +
alpha[7] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
M_lvlone[i, 7] <- abs(M_lvlone[i, 6] - M_lvlone[i, 4])
}
for (ii in 1:10) {
b_copper_center[ii, 1:1] ~ dnorm(mu_b_copper_center[ii, ], invD_copper_center[ , ])
mu_b_copper_center[ii, 1] <- M_center[ii, 1] * alpha[5]
}
# Priors for the model for copper
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_copper ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_copper <- sqrt(1/tau_copper)
invD_copper_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_copper_center[1, 1] <- 1 / (invD_copper_center[1, 1])
}
GRcrit and MCerror give same result
Code
lapply(models0, GR_crit, multivariate = FALSE)
Output
$m0a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
shape_Srv_ftm_stts_cn NaN NaN
$m1a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
age NaN NaN
sexfemale NaN NaN
shape_Srv_ftm_stts_cn NaN NaN
$m1b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
age NaN NaN
sexfemale NaN NaN
shape_Srv_ftm_stts_cn NaN NaN
$m2a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
copper NaN NaN
shape_Srv_ftm_stts_cn NaN NaN
$m3a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
copper NaN NaN
sexfemale NaN NaN
age NaN NaN
abs(age - copper) NaN NaN
log(trig) NaN NaN
shape_Srv_ftm_stts_cn NaN NaN
$m3b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
copper NaN NaN
sexfemale NaN NaN
age NaN NaN
abs(age - copper) NaN NaN
log(trig) NaN NaN
shape_Srv_ftm_stts_cn NaN NaN
D_Srv_ftm_stts_cn_center[1,1] NaN NaN
Code
lapply(models0, MC_error)
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
shape_Srv_ftm_stts_cn 0 0 0 NaN
$m1a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
age 0 0 0 NaN
sexfemale 0 0 0 NaN
shape_Srv_ftm_stts_cn 0 0 0 NaN
$m1b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
age 0 0 0 NaN
sexfemale 0 0 0 NaN
shape_Srv_ftm_stts_cn 0 0 0 NaN
$m2a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
copper 0 0 0 NaN
shape_Srv_ftm_stts_cn 0 0 0 NaN
$m3a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
copper 0 0 0 NaN
sexfemale 0 0 0 NaN
age 0 0 0 NaN
abs(age - copper) 0 0 0 NaN
log(trig) 0 0 0 NaN
shape_Srv_ftm_stts_cn 0 0 0 NaN
$m3b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
copper 0 0 0 NaN
sexfemale 0 0 0 NaN
age 0 0 0 NaN
abs(age - copper) 0 0 0 NaN
log(trig) 0 0 0 NaN
shape_Srv_ftm_stts_cn 0 0 0 NaN
D_Srv_ftm_stts_cn_center[1,1] 0 0 0 NaN
summary output remained the same
Code
lapply(models0, print)
Output
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ 1,
data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian weibull survival model for "Surv(futime, status != "censored")"
Coefficients:
(Intercept)
0
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ age +
sex, data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian weibull survival model for "Surv(futime, status != "censored")"
Coefficients:
(Intercept) age sexfemale
0 0 0
Call:
survreg_imp(formula = Surv(futime, I(status != "censored")) ~
age + sex, data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian weibull survival model for "Surv(futime, I(status != "censored"))"
Coefficients:
(Intercept) age sexfemale
0 0 0
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ copper,
data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian weibull survival model for "Surv(futime, status != "censored")"
Coefficients:
(Intercept) copper
0 0
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04,
NA)))
Bayesian weibull survival model for "Surv(futime, status != "censored")"
Coefficients:
(Intercept) copper sexfemale age
0 0 0 0
abs(age - copper) log(trig)
0 0
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig) + (1 | center),
data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(trig = c(1e-04, NA)))
Bayesian weibull survival model for "Surv(futime, status != "censored")"
Coefficients:
(Intercept) copper sexfemale age
0 0 0 0
abs(age - copper) log(trig)
0 0
$m0a
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ 1,
data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian weibull survival model for "Surv(futime, status != "censored")"
Coefficients:
(Intercept)
0
$m1a
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ age +
sex, data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian weibull survival model for "Surv(futime, status != "censored")"
Coefficients:
(Intercept) age sexfemale
0 0 0
$m1b
Call:
survreg_imp(formula = Surv(futime, I(status != "censored")) ~
age + sex, data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian weibull survival model for "Surv(futime, I(status != "censored"))"
Coefficients:
(Intercept) age sexfemale
0 0 0
$m2a
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ copper,
data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian weibull survival model for "Surv(futime, status != "censored")"
Coefficients:
(Intercept) copper
0 0
$m3a
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04,
NA)))
Bayesian weibull survival model for "Surv(futime, status != "censored")"
Coefficients:
(Intercept) copper sexfemale age
0 0 0 0
abs(age - copper) log(trig)
0 0
$m3b
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig) + (1 | center),
data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(trig = c(1e-04, NA)))
Bayesian weibull survival model for "Surv(futime, status != "censored")"
Coefficients:
(Intercept) copper sexfemale age
0 0 0 0
abs(age - copper) log(trig)
0 0
Code
lapply(models0, coef)
Output
$m0a
$m0a$`Surv(futime, status != "censored")`
(Intercept) shape_Srv_ftm_stts_cn
0 0
$m1a
$m1a$`Surv(futime, status != "censored")`
(Intercept) age sexfemale
0 0 0
shape_Srv_ftm_stts_cn
0
$m1b
$m1b$`Surv(futime, I(status != "censored"))`
(Intercept) age sexfemale
0 0 0
shape_Srv_ftm_stts_cn
0
$m2a
$m2a$`Surv(futime, status != "censored")`
(Intercept) copper shape_Srv_ftm_stts_cn
0 0 0
$m3a
$m3a$`Surv(futime, status != "censored")`
(Intercept) copper sexfemale
0 0 0
age abs(age - copper) log(trig)
0 0 0
shape_Srv_ftm_stts_cn
0
$m3b
$m3b$`Surv(futime, status != "censored")`
(Intercept) copper
0 0
sexfemale age
0 0
abs(age - copper) log(trig)
0 0
shape_Srv_ftm_stts_cn D_Srv_ftm_stts_cn_center[1,1]
0 0
Code
lapply(models0, confint)
Output
$m0a
$m0a$`Surv(futime, status != "censored")`
2.5% 97.5%
(Intercept) 0 0
shape_Srv_ftm_stts_cn 0 0
$m1a
$m1a$`Surv(futime, status != "censored")`
2.5% 97.5%
(Intercept) 0 0
age 0 0
sexfemale 0 0
shape_Srv_ftm_stts_cn 0 0
$m1b
$m1b$`Surv(futime, I(status != "censored"))`
2.5% 97.5%
(Intercept) 0 0
age 0 0
sexfemale 0 0
shape_Srv_ftm_stts_cn 0 0
$m2a
$m2a$`Surv(futime, status != "censored")`
2.5% 97.5%
(Intercept) 0 0
copper 0 0
shape_Srv_ftm_stts_cn 0 0
$m3a
$m3a$`Surv(futime, status != "censored")`
2.5% 97.5%
(Intercept) 0 0
copper 0 0
sexfemale 0 0
age 0 0
abs(age - copper) 0 0
log(trig) 0 0
shape_Srv_ftm_stts_cn 0 0
$m3b
$m3b$`Surv(futime, status != "censored")`
2.5% 97.5%
(Intercept) 0 0
copper 0 0
sexfemale 0 0
age 0 0
abs(age - copper) 0 0
log(trig) 0 0
shape_Srv_ftm_stts_cn 0 0
D_Srv_ftm_stts_cn_center[1,1] 0 0
Code
lapply(models0, summary)
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a
Bayesian weibull survival model fitted with JointAI
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ 1,
data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of the shape of the Weibull distribution:
Mean SD 2.5% 97.5% GR-crit MCE/SD
shape_Srv_ftm_stts_cn 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 312
$m1a
Bayesian weibull survival model fitted with JointAI
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ age +
sex, data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
Posterior summary of the shape of the Weibull distribution:
Mean SD 2.5% 97.5% GR-crit MCE/SD
shape_Srv_ftm_stts_cn 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 312
$m1b
Bayesian weibull survival model fitted with JointAI
Call:
survreg_imp(formula = Surv(futime, I(status != "censored")) ~
age + sex, data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
Posterior summary of the shape of the Weibull distribution:
Mean SD 2.5% 97.5% GR-crit MCE/SD
shape_Srv_ftm_stts_cn 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 312
$m2a
Bayesian weibull survival model fitted with JointAI
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ copper,
data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
copper 0 0 0 0 0 NaN NaN
Posterior summary of the shape of the Weibull distribution:
Mean SD 2.5% 97.5% GR-crit MCE/SD
shape_Srv_ftm_stts_cn 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 312
$m3a
Bayesian weibull survival model fitted with JointAI
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04,
NA)))
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
copper 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
abs(age - copper) 0 0 0 0 0 NaN NaN
log(trig) 0 0 0 0 0 NaN NaN
Posterior summary of the shape of the Weibull distribution:
Mean SD 2.5% 97.5% GR-crit MCE/SD
shape_Srv_ftm_stts_cn 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 312
$m3b
Bayesian weibull survival model fitted with JointAI
Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig) + (1 | center),
data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(trig = c(1e-04, NA)))
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
copper 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
abs(age - copper) 0 0 0 0 0 NaN NaN
log(trig) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_Srv_ftm_stts_cn_center[1,1] 0 0 0 0 NaN NaN
Posterior summary of the shape of the Weibull distribution:
Mean SD 2.5% 97.5% GR-crit MCE/SD
shape_Srv_ftm_stts_cn 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 312
Number of groups:
- center: 10
Code
lapply(models0, function(x) coef(summary(x)))
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a
$m0a$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m1a
$m1a$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
$m1b
$m1b$`Surv(futime, I(status != "censored"))`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
$m2a
$m2a$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
copper 0 0 0 0 0 NaN NaN
$m3a
$m3a$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
copper 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
abs(age - copper) 0 0 0 0 0 NaN NaN
log(trig) 0 0 0 0 0 NaN NaN
$m3b
$m3b$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
copper 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
abs(age - copper) 0 0 0 0 0 NaN NaN
log(trig) 0 0 0 0 0 NaN NaN
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