Cl[1] 0.02257 0.002282 6.937E-5 0.01804 0.02257 0.0271 10001 90000 Cl[2] 0.04348 0.003794 7.745E-5 0.03636 0.04336 0.05121 10001 90000 Cl[3] 0.04105 0.003602 7.53E-5 0.03408 0.041 0.04836 10001 90000 Cl[4] 0.03765 0.003312 7.255E-5 0.03128 0.03757 0.0443 10001 90000 Cl[5] 0.04328 0.003451 6.781E-5 0.03668 0.04319 0.05018 10001 90000 Cl[6] 0.04866 0.005072 1.029E-4 0.03902 0.04857 0.05889 10001 90000 Cl[7] 0.0486 0.004986 1.173E-4 0.03896 0.04854 0.0585 10001 90000 Cl[8] 0.04536 0.0043 8.932E-5 0.03723 0.04525 0.05424 10001 90000 Cl[9] 0.03324 0.00341 8.144E-5 0.02674 0.03314 0.04031 10001 90000 Cl[10] 0.03364 0.003032 7.726E-5 0.02772 0.03365 0.03958 10001 90000 Cl[11] 0.0555 0.005467 1.273E-4 0.04507 0.05536 0.06661 10001 90000 Cl[12] 0.04033 0.003352 7.843E-5 0.0338 0.04031 0.04702 10001 90000 Clmed 0.04016 0.003722 3.519E-5 0.03326 0.04 0.04801 10001 90000 D[1,1] 0.05473 0.0328 4.742E-4 0.01877 0.04666 0.1385 10001 90000 D[1,2] -9.571E-4 0.06946 0.001086 -0.1494 0.001556 0.1322 10001 90000 D[1,3] 0.03752 0.03353 5.49E-4 -0.004981 0.03036 0.1218 10001 90000 D[2,1] -9.571E-4 0.06946 0.001086 -0.1494 0.001556 0.1322 10001 90000 D[2,2] 0.5084 0.295 0.002454 0.181 0.4353 1.267 10001 90000 D[2,3] -0.008007 0.0793 8.166E-4 -0.1769 -0.005041 0.1442 10001 90000 D[3,1] 0.03752 0.03353 5.49E-4 -0.004981 0.03036 0.1218 10001 90000 D[3,2] -0.008007 0.0793 8.166E-4 -0.1769 -0.005041 0.1442 10001 90000 D[3,3] 0.09061 0.04948 5.742E-4 0.03408 0.07861 0.2174 10001 90000 beta[1] -2.446 0.0832 0.001328 -2.612 -2.446 -2.281 4001 96000 beta[2] 0.4451 0.2152 0.001271 0.02201 0.4427 0.8814 4001 96000 beta[3] -3.219 0.09235 8.61E-4 -3.403 -3.219 -3.036 4001 96000 fitted[1,2] 3.695 0.3898 0.004152 2.993 3.674 4.521 10001 90000 fitted[1,3] 6.605 0.5089 0.004599 5.633 6.595 7.636 10001 90000 fitted[1,4] 8.969 0.4507 0.003862 8.081 8.97 9.853 10001 90000 fitted[1,5] 9.882 0.4477 0.006068 9.016 9.88 10.79 10001 90000 fitted[1,6] 9.241 0.4481 0.005201 8.395 9.231 10.16 10001 90000 fitted[1,7] 8.544 0.3978 0.003831 7.799 8.532 9.359 10001 90000 fitted[1,8] 7.556 0.3485 0.004735 6.895 7.549 8.265 10001 90000 fitted[1,9] 6.642 0.3423 0.007149 5.983 6.637 7.328 10001 90000 fitted[1,10] 5.463 0.3729 0.01014 4.741 5.457 6.207 10001 90000 fitted[1,11] 2.523 0.4138 0.01291 1.763 2.5 3.412 10001 90000 fitted[2,2] 4.003 0.4188 0.003541 3.246 3.977 4.895 10001 90000 fitted[2,3] 6.097 0.4727 0.003381 5.197 6.084 7.054 10001 90000 fitted[2,4] 7.886 0.418 0.002732 7.082 7.881 8.724 10001 90000 fitted[2,5] 8.304 0.4491 0.0045 7.445 8.296 9.213 10001 90000 fitted[2,6] 7.339 0.4059 0.003784 6.577 7.325 8.17 10001 90000 fitted[2,7] 6.344 0.3342 0.003378 5.712 6.334 7.026 10001 90000 fitted[2,8] 5.22 0.2975 0.00443 4.645 5.217 5.811 10001 90000 fitted[2,9] 4.313 0.3002 0.005515 3.727 4.315 4.903 10001 90000 fitted[2,10] 3.229 0.3131 0.00641 2.619 3.23 3.845 10001 90000 fitted[2,11] 0.9992 0.2322 0.004959 0.5871 0.986 1.491 10001 90000 fitted[3,2] 4.352 0.4941 0.004415 3.486 4.319 5.418 10001 90000 fitted[3,3] 6.768 0.4898 0.003193 5.835 6.759 7.75 10001 90000 fitted[3,4] 8.061 0.4113 0.002843 7.274 8.054 8.891 10001 90000 fitted[3,5] 8.207 0.4577 0.005037 7.323 8.204 9.115 10001 90000 fitted[3,6] 7.25 0.396 0.003772 6.501 7.241 8.051 10001 90000 fitted[3,7] 6.392 0.3295 0.003095 5.769 6.387 7.055 10001 90000 fitted[3,8] 5.38 0.2902 0.003917 4.826 5.378 5.96 10001 90000 fitted[3,9] 4.554 0.2881 0.005019 3.996 4.553 5.129 10001 90000 fitted[3,10] 3.472 0.3024 0.00619 2.888 3.468 4.078 10001 90000 fitted[3,11] 1.246 0.2542 0.005645 0.7967 1.23 1.797 10001 90000 fitted[4,2] 3.356 0.3504 0.003585 2.727 3.338 4.098 10001 90000 fitted[4,3] 4.966 0.4328 0.004006 4.162 4.951 5.858 10001 90000 fitted[4,4] 6.836 0.4385 0.003169 5.986 6.831 7.709 10001 90000 fitted[4,5] 8.196 0.4024 0.003627 7.428 8.19 9.005 10001 90000 fitted[4,6] 7.931 0.4274 0.004609 7.115 7.923 8.797 10001 90000 fitted[4,7] 7.094 0.3927 0.003835 6.36 7.083 7.903 10001 90000 fitted[4,8] 5.986 0.3349 0.003731 5.349 5.978 6.669 10001 90000 fitted[4,9] 5.028 0.3138 0.004931 4.421 5.025 5.66 10001 90000 fitted[4,10] 3.883 0.3204 0.006515 3.258 3.882 4.525 10001 90000 fitted[4,11] 1.3 0.2713 0.006413 0.8091 1.286 1.883 10001 90000 fitted[5,2] 4.087 0.377 0.003623 3.396 4.07 4.878 10001 90000 fitted[5,3] 6.067 0.4638 0.004031 5.193 6.056 7.014 10001 90000 fitted[5,4] 8.542 0.4596 0.003125 7.653 8.54 9.453 10001 90000 fitted[5,5] 9.888 0.4456 0.004395 9.019 9.881 10.78 10001 90000 fitted[5,6] 9.248 0.456 0.004674 8.376 9.24 10.17 10001 90000 fitted[5,7] 8.165 0.3954 0.003439 7.421 8.153 8.972 10001 90000 fitted[5,8] 6.855 0.3393 0.003599 6.209 6.847 7.549 10001 90000 fitted[5,9] 5.707 0.3294 0.004965 5.073 5.704 6.372 10001 90000 fitted[5,10] 4.423 0.3427 0.006409 3.76 4.421 5.105 10001 90000 fitted[5,11] 1.508 0.2861 0.006137 0.9875 1.496 2.104 10001 90000 fitted[6,2] 2.057 0.2785 0.002795 1.571 2.035 2.663 10001 90000 fitted[6,3] 3.649 0.3933 0.003525 2.93 3.631 4.475 10001 90000 fitted[6,4] 5.243 0.3933 0.00267 4.487 5.236 6.035 10001 90000 fitted[6,5] 6.012 0.36 0.002946 5.324 6.004 6.74 10001 90000 fitted[6,6] 5.758 0.3719 0.003812 5.047 5.749 6.515 10001 90000 fitted[6,7] 5.146 0.3406 0.003279 4.507 5.135 5.848 10001 90000 fitted[6,8] 4.304 0.2938 0.003319 3.75 4.294 4.907 10001 90000 fitted[6,9] 3.513 0.2781 0.004383 2.98 3.506 4.074 10001 90000 fitted[6,10] 2.7 0.2835 0.005489 2.156 2.696 3.268 10001 90000 fitted[6,11] 0.9372 0.2327 0.005116 0.5294 0.9216 1.438 10001 90000 fitted[7,2] 1.545 0.1969 0.002309 1.208 1.529 1.976 10001 90000 fitted[7,3] 2.786 0.3153 0.003496 2.233 2.765 3.463 10001 90000 fitted[7,4] 4.622 0.4111 0.003883 3.865 4.608 5.469 10001 90000 fitted[7,5] 6.344 0.3928 0.002642 5.589 6.339 7.132 10001 90000 fitted[7,6] 6.817 0.3786 0.003637 6.087 6.812 7.578 10001 90000 fitted[7,7] 6.393 0.3834 0.003939 5.658 6.387 7.172 10001 90000 fitted[7,8] 5.519 0.3535 0.003486 4.853 5.509 6.239 10001 90000 fitted[7,9] 4.642 0.3265 0.004376 4.022 4.633 5.305 10001 90000 fitted[7,10] 3.537 0.3268 0.006573 2.91 3.529 4.192 10001 90000 fitted[7,11] 1.198 0.2945 0.007502 0.6727 1.179 1.825 10001 90000 fitted[8,2] 2.644 0.3204 0.00274 2.078 2.621 3.333 10001 90000 fitted[8,3] 4.522 0.4314 0.003242 3.723 4.504 5.416 10001 90000 fitted[8,4] 6.285 0.4233 0.002511 5.468 6.282 7.132 10001 90000 fitted[8,5] 7.299 0.4013 0.003757 6.529 7.292 8.109 10001 90000 fitted[8,6] 6.803 0.4066 0.003938 6.034 6.794 7.629 10001 90000 fitted[8,7] 6.004 0.3532 0.003045 5.339 5.996 6.723 10001 90000 fitted[8,8] 4.996 0.302 0.003543 4.419 4.989 5.605 10001 90000 fitted[8,9] 4.219 0.2922 0.004698 3.648 4.215 4.803 10001 90000 fitted[8,10] 3.233 0.3008 0.005982 2.643 3.23 3.834 10001 90000 fitted[8,11] 1.139 0.2502 0.005585 0.687 1.126 1.674 10001 90000 fitted[9,2] 6.805 0.6309 0.005329 5.587 6.803 8.035 10001 90000 fitted[9,3] 7.74 0.412 0.00415 6.956 7.733 8.571 10001 90000 fitted[9,4] 7.669 0.4202 0.004708 6.871 7.659 8.521 10001 90000 fitted[9,5] 7.062 0.3667 0.003276 6.375 7.05 7.813 10001 90000 fitted[9,6] 6.168 0.2951 0.002383 5.606 6.161 6.766 10001 90000 fitted[9,7] 5.398 0.27 0.0036 4.874 5.395 5.937 10001 90000 fitted[9,8] 4.456 0.2779 0.005468 3.908 4.456 5.002 10001 90000 fitted[9,9] 3.854 0.293 0.006462 3.273 3.855 4.431 10001 90000 fitted[9,10] 3.007 0.311 0.007406 2.398 3.005 3.628 10001 90000 fitted[9,11] 0.9772 0.2398 0.006013 0.5597 0.9597 1.503 10001 90000 fitted[10,2] 2.884 0.277 0.003433 2.388 2.868 3.473 10001 90000 fitted[10,3] 5.148 0.408 0.00445 4.394 5.133 5.99 10001 90000 fitted[10,4] 6.214 0.4379 0.004283 5.39 6.202 7.105 10001 90000 fitted[10,5] 8.73 0.418 0.002515 7.925 8.724 9.57 10001 90000 fitted[10,6] 9.554 0.4254 0.005014 8.724 9.551 10.4 10001 90000 fitted[10,7] 9.137 0.4499 0.005737 8.26 9.136 10.03 10001 90000 fitted[10,8] 8.071 0.4184 0.004502 7.271 8.065 8.919 10001 90000 fitted[10,9] 6.829 0.3745 0.004693 6.109 6.825 7.589 10001 90000 fitted[10,10] 5.556 0.3706 0.007157 4.849 5.552 6.303 10001 90000 fitted[10,11] 2.299 0.3973 0.01067 1.569 2.282 3.129 10001 90000 fitted[11,2] 4.895 0.562 0.005017 3.894 4.861 6.094 10001 90000 fitted[11,3] 6.774 0.4664 0.003136 5.863 6.771 7.693 10001 90000 fitted[11,4] 7.649 0.4319 0.005169 6.808 7.644 8.514 10001 90000 fitted[11,5] 7.245 0.4482 0.005365 6.4 7.235 8.171 10001 90000 fitted[11,6] 6.209 0.3462 0.003121 5.559 6.198 6.923 10001 90000 fitted[11,7] 5.413 0.2969 0.003336 4.851 5.406 6.013 10001 90000 fitted[11,8] 4.46 0.2836 0.004991 3.912 4.456 5.027 10001 90000 fitted[11,9] 3.681 0.295 0.006291 3.108 3.68 4.268 10001 90000 fitted[11,10] 2.74 0.309 0.007281 2.137 2.736 3.36 10001 90000 fitted[11,11] 0.8867 0.2291 0.005689 0.4867 0.8673 1.39 10001 90000 fitted[12,2] 2.469 0.2537 0.002967 2.012 2.453 3.006 10001 90000 fitted[12,3] 4.38 0.3921 0.004223 3.656 4.362 5.193 10001 90000 fitted[12,4] 6.969 0.4788 0.004078 6.055 6.96 7.927 10001 90000 fitted[12,5] 9.193 0.4464 0.003246 8.331 9.189 10.08 10001 90000 fitted[12,6] 9.407 0.4584 0.0054 8.518 9.401 10.32 10001 90000 fitted[12,7] 8.511 0.4388 0.004773 7.673 8.5 9.395 10001 90000 fitted[12,8] 7.145 0.3764 0.00382 6.431 7.136 7.913 10001 90000 fitted[12,9] 5.944 0.35 0.005291 5.275 5.937 6.646 10001 90000 fitted[12,10] 4.458 0.3653 0.007859 3.756 4.454 5.186 10001 90000 fitted[12,11] 1.422 0.3149 0.008007 0.8577 1.404 2.095 10001 90000 gamma 0.7281 0.3573 0.007114 0.09816 0.7111 1.474 4001 96000 ka[1] 1.607 0.2664 0.004234 1.16 1.582 2.212 10001 90000 ka[2] 2.007 0.3627 0.004355 1.408 1.969 2.842 10001 90000 ka[3] 2.343 0.4892 0.00618 1.592 2.271 3.489 10001 90000 ka[4] 1.174 0.203 0.003029 0.8315 1.155 1.631 10001 90000 ka[5] 1.436 0.2158 0.00301 1.064 1.417 1.907 10001 90000 ka[6] 1.214 0.2517 0.003543 0.7978 1.186 1.789 10001 90000 ka[7] 0.752 0.1476 0.00255 0.5051 0.7382 1.082 10001 90000 ka[8] 1.467 0.2767 0.003508 1.014 1.437 2.096 10001 90000 ka[9] 6.674 3.303 0.03533 3.625 6.03 13.59 10001 90000 ka[10] 0.7246 0.1195 0.002301 0.5185 0.7145 0.9863 10001 90000 ka[11] 3.604 0.9337 0.01229 2.297 3.461 5.746 10001 90000 ka[12] 0.8937 0.1445 0.002586 0.6414 0.8829 1.203 10001 90000 kamed 1.598 0.3557 0.002119 1.024 1.557 2.415 10001 90000 ke[1] 0.064 0.008851 2.694E-4 0.04727 0.06377 0.08239 10001 90000 ke[2] 0.09725 0.01223 2.517E-4 0.07548 0.09644 0.1231 10001 90000 ke[3] 0.08668 0.0107 2.292E-4 0.06662 0.0863 0.1087 10001 90000 ke[4] 0.08784 0.01142 2.64E-4 0.06716 0.08716 0.112 10001 90000 ke[5] 0.08836 0.01017 2.163E-4 0.06962 0.08789 0.1097 10001 90000 ke[6] 0.0923 0.01411 3.07E-4 0.06739 0.09145 0.1223 10001 90000 ke[7] 0.09127 0.01528 3.853E-4 0.06509 0.09007 0.1241 10001 90000 ke[8] 0.08848 0.01201 2.648E-4 0.06688 0.08767 0.114 10001 90000 ke[9] 0.08956 0.01185 2.858E-4 0.06766 0.08904 0.1145 10001 90000 ke[10] 0.07729 0.01127 3.087E-4 0.05711 0.07658 0.1013 10001 90000 ke[11] 0.09662 0.01318 3.187E-4 0.07239 0.09605 0.1247 10001 90000 ke[12] 0.09628 0.01319 3.322E-4 0.07293 0.09543 0.1249 10001 90000 kemed 0.08696 0.007265 1.197E-4 0.07334 0.08665 0.1022 10001 90000 resid[1,2] -2.651 0.3898 0.004152 -3.477 -2.63 -1.949 10001 90000 resid[1,3] -4.722 0.5089 0.004599 -5.754 -4.712 -3.75 10001 90000 resid[1,4] -6.617 0.4507 0.003862 -7.501 -6.619 -5.73 10001 90000 resid[1,5] -7.614 0.4477 0.006068 -8.517 -7.612 -6.748 10001 90000 resid[1,6] -7.092 0.4481 0.005201 -8.008 -7.081 -6.245 10001 90000 resid[1,7] -6.42 0.3978 0.003831 -7.235 -6.408 -5.676 10001 90000 resid[1,8] -5.546 0.3485 0.004735 -6.254 -5.538 -4.884 10001 90000 resid[1,9] -4.712 0.3423 0.007149 -5.397 -4.707 -4.053 10001 90000 resid[1,10] -3.681 0.3729 0.01014 -4.426 -3.676 -2.959 10001 90000 resid[1,11] -1.335 0.4138 0.01291 -2.224 -1.313 -0.5753 10001 90000 resid[2,2] -3.461 0.4188 0.003541 -4.352 -3.435 -2.704 10001 90000 resid[2,3] -4.028 0.4727 0.003381 -4.986 -4.016 -3.129 10001 90000 resid[2,4] -5.768 0.418 0.002732 -6.606 -5.764 -4.965 10001 90000 resid[2,5] -6.184 0.4491 0.0045 -7.093 -6.176 -5.325 10001 90000 resid[2,6] -5.415 0.4059 0.003784 -6.246 -5.4 -4.653 10001 90000 resid[2,7] -4.539 0.3342 0.003378 -5.221 -4.529 -3.906 10001 90000 resid[2,8] -3.533 0.2975 0.00443 -4.125 -3.53 -2.959 10001 90000 resid[2,9] -2.798 0.3002 0.005515 -3.387 -2.8 -2.212 10001 90000 resid[2,10] -2.127 0.3131 0.00641 -2.743 -2.128 -1.517 10001 90000 resid[2,11] -1.105 0.2322 0.004959 -1.596 -1.091 -0.6924 10001 90000 resid[3,2] -2.87 0.4941 0.004415 -3.936 -2.837 -2.004 10001 90000 resid[3,3] -4.836 0.4898 0.003193 -5.819 -4.827 -3.903 10001 90000 resid[3,4] -5.957 0.4113 0.002843 -6.786 -5.949 -5.17 10001 90000 resid[3,5] -6.152 0.4577 0.005037 -7.061 -6.15 -5.269 10001 90000 resid[3,6] -5.235 0.396 0.003772 -6.036 -5.226 -4.486 10001 90000 resid[3,7] -4.567 0.3295 0.003095 -5.23 -4.562 -3.945 10001 90000 resid[3,8] -3.712 0.2902 0.003917 -4.292 -3.711 -3.158 10001 90000 resid[3,9] -2.965 0.2881 0.005019 -3.54 -2.964 -2.406 10001 90000 resid[3,10] -2.164 0.3024 0.00619 -2.77 -2.16 -1.58 10001 90000 resid[3,11] -1.197 0.2542 0.005645 -1.748 -1.181 -0.7479 10001 90000 resid[4,2] -2.72 0.3504 0.003585 -3.462 -2.701 -2.09 10001 90000 resid[4,3] -3.44 0.4328 0.004006 -4.332 -3.425 -2.636 10001 90000 resid[4,4] -4.684 0.4385 0.003169 -5.557 -4.679 -3.834 10001 90000 resid[4,5] -6.07 0.4024 0.003627 -6.879 -6.064 -5.302 10001 90000 resid[4,6] -5.91 0.4274 0.004609 -6.776 -5.903 -5.095 10001 90000 resid[4,7] -5.166 0.3927 0.003835 -5.974 -5.155 -4.432 10001 90000 resid[4,8] -4.231 0.3349 0.003731 -4.914 -4.224 -3.594 10001 90000 resid[4,9] -3.355 0.3138 0.004931 -3.986 -3.352 -2.748 10001 90000 resid[4,10] -2.451 0.3204 0.006515 -3.092 -2.45 -1.826 10001 90000 resid[4,11] -1.16 0.2713 0.006413 -1.743 -1.147 -0.6693 10001 90000 resid[5,2] -3.384 0.377 0.003623 -4.175 -3.367 -2.693 10001 90000 resid[5,3] -4.339 0.4638 0.004031 -5.286 -4.328 -3.464 10001 90000 resid[5,4] -6.108 0.4596 0.003125 -7.019 -6.106 -5.219 10001 90000 resid[5,5] -7.655 0.4456 0.004395 -8.551 -7.648 -6.786 10001 90000 resid[5,6] -7.08 0.456 0.004674 -8.006 -7.072 -6.208 10001 90000 resid[5,7] -6.142 0.3954 0.003439 -6.949 -6.131 -5.398 10001 90000 resid[5,8] -4.896 0.3393 0.003599 -5.59 -4.888 -4.25 10001 90000 resid[5,9] -3.932 0.3294 0.004965 -4.597 -3.929 -3.298 10001 90000 resid[5,10] -2.948 0.3427 0.006409 -3.63 -2.946 -2.286 10001 90000 resid[5,11] -1.057 0.2861 0.006137 -1.653 -1.045 -0.5363 10001 90000 resid[6,2] -1.802 0.2785 0.002795 -2.409 -1.781 -1.316 10001 90000 resid[6,3] -2.524 0.3933 0.003525 -3.35 -2.506 -1.805 10001 90000 resid[6,4] -3.38 0.3933 0.00267 -4.172 -3.373 -2.625 10001 90000 resid[6,5] -4.168 0.36 0.002946 -4.896 -4.16 -3.481 10001 90000 resid[6,6] -4.048 0.3719 0.003812 -4.805 -4.039 -3.337 10001 90000 resid[6,7] -3.548 0.3406 0.003279 -4.251 -3.538 -2.909 10001 90000 resid[6,8] -2.913 0.2938 0.003319 -3.516 -2.903 -2.359 10001 90000 resid[6,9] -2.272 0.2781 0.004383 -2.833 -2.265 -1.739 10001 90000 resid[6,10] -1.678 0.2835 0.005489 -2.246 -1.673 -1.134 10001 90000 resid[6,11] -1.021 0.2327 0.005116 -1.521 -1.005 -0.6127 10001 90000 resid[7,2] -1.707 0.1969 0.002309 -2.138 -1.691 -1.371 10001 90000 resid[7,3] -1.931 0.3153 0.003496 -2.609 -1.911 -1.379 10001 90000 resid[7,4] -3.009 0.4111 0.003883 -3.856 -2.995 -2.251 10001 90000 resid[7,5] -4.46 0.3928 0.002642 -5.248 -4.455 -3.705 10001 90000 resid[7,6] -4.858 0.3786 0.003637 -5.619 -4.853 -4.128 10001 90000 resid[7,7] -4.497 0.3834 0.003939 -5.276 -4.491 -3.762 10001 90000 resid[7,8] -3.861 0.3535 0.003486 -4.581 -3.851 -3.195 10001 90000 resid[7,9] -3.162 0.3265 0.004376 -3.826 -3.154 -2.543 10001 90000 resid[7,10] -2.275 0.3268 0.006573 -2.93 -2.268 -1.649 10001 90000 resid[7,11] -1.058 0.2945 0.007502 -1.685 -1.04 -0.5329 10001 90000 resid[8,2] -1.529 0.3204 0.00274 -2.218 -1.506 -0.9625 10001 90000 resid[8,3] -3.407 0.4314 0.003242 -4.3 -3.389 -2.608 10001 90000 resid[8,4] -4.296 0.4233 0.002511 -5.143 -4.293 -3.479 10001 90000 resid[8,5] -5.276 0.4013 0.003757 -6.086 -5.269 -4.507 10001 90000 resid[8,6] -4.918 0.4066 0.003938 -5.744 -4.909 -4.148 10001 90000 resid[8,7] -4.233 0.3532 0.003045 -4.951 -4.224 -3.567 10001 90000 resid[8,8] -3.442 0.302 0.003543 -4.051 -3.435 -2.865 10001 90000 resid[8,9] -2.699 0.2922 0.004698 -3.284 -2.695 -2.128 10001 90000 resid[8,10] -2.134 0.3008 0.005982 -2.735 -2.131 -1.544 10001 90000 resid[8,11] -0.916 0.2502 0.005585 -1.451 -0.9026 -0.4639 10001 90000 resid[9,2] -4.807 0.6309 0.005329 -6.038 -4.806 -3.589 10001 90000 resid[9,3] -5.54 0.412 0.00415 -6.37 -5.533 -4.755 10001 90000 resid[9,4] -5.703 0.4202 0.004708 -6.555 -5.693 -4.905 10001 90000 resid[9,5] -5.216 0.3667 0.003276 -5.967 -5.204 -4.529 10001 90000 resid[9,6] -4.434 0.2951 0.002383 -5.033 -4.427 -3.872 10001 90000 resid[9,7] -3.663 0.27 0.0036 -4.202 -3.66 -3.138 10001 90000 resid[9,8] -3.011 0.2779 0.005468 -3.557 -3.011 -2.464 10001 90000 resid[9,9] -2.441 0.293 0.006462 -3.017 -2.441 -1.859 10001 90000 resid[9,10] -1.856 0.311 0.007406 -2.478 -1.855 -1.248 10001 90000 resid[9,11] -0.8639 0.2398 0.006013 -1.39 -0.8464 -0.4464 10001 90000 resid[10,2] -1.823 0.277 0.003433 -2.412 -1.807 -1.326 10001 90000 resid[10,3] -3.495 0.408 0.00445 -4.338 -3.481 -2.741 10001 90000 resid[10,4] -4.357 0.4379 0.004283 -5.246 -4.344 -3.533 10001 90000 resid[10,5] -6.672 0.418 0.002515 -7.512 -6.666 -5.867 10001 90000 resid[10,6] -7.231 0.4254 0.005014 -8.075 -7.228 -6.4 10001 90000 resid[10,7] -6.92 0.4499 0.005737 -7.815 -6.919 -6.043 10001 90000 resid[10,8] -5.989 0.4184 0.004502 -6.837 -5.983 -5.189 10001 90000 resid[10,9] -4.864 0.3745 0.004693 -5.623 -4.86 -4.143 10001 90000 resid[10,10] -3.819 0.3706 0.007157 -4.566 -3.815 -3.112 10001 90000 resid[10,11] -1.415 0.3973 0.01067 -2.245 -1.398 -0.6853 10001 90000 resid[11,2] -3.314 0.562 0.005017 -4.513 -3.28 -2.312 10001 90000 resid[11,3] -4.794 0.4664 0.003136 -5.713 -4.791 -3.884 10001 90000 resid[11,4] -5.569 0.4319 0.005169 -6.435 -5.564 -4.728 10001 90000 resid[11,5] -5.327 0.4482 0.005365 -6.253 -5.316 -4.482 10001 90000 resid[11,6] -4.439 0.3462 0.003121 -5.153 -4.429 -3.789 10001 90000 resid[11,7] -3.761 0.2969 0.003336 -4.361 -3.753 -3.198 10001 90000 resid[11,8] -2.967 0.2836 0.004991 -3.534 -2.963 -2.418 10001 90000 resid[11,9] -2.394 0.295 0.006291 -2.981 -2.393 -1.821 10001 90000 resid[11,10] -1.75 0.309 0.007281 -2.371 -1.747 -1.147 10001 90000 resid[11,11] -1.037 0.2291 0.005689 -1.54 -1.018 -0.6375 10001 90000 resid[12,2] -2.246 0.2537 0.002967 -2.783 -2.23 -1.789 10001 90000 resid[12,3] -3.004 0.3921 0.004223 -3.816 -2.986 -2.28 10001 90000 resid[12,4] -4.912 0.4788 0.004078 -5.871 -4.903 -3.998 10001 90000 resid[12,5] -6.918 0.4464 0.003246 -7.803 -6.914 -6.057 10001 90000 resid[12,6] -7.13 0.4584 0.0054 -8.042 -7.124 -6.241 10001 90000 resid[12,7] -6.363 0.4388 0.004773 -7.247 -6.352 -5.525 10001 90000 resid[12,8] -5.259 0.3764 0.00382 -6.027 -5.251 -4.545 10001 90000 resid[12,9] -4.134 0.35 0.005291 -4.836 -4.127 -3.465 10001 90000 resid[12,10] -2.938 0.3653 0.007859 -3.666 -2.935 -2.237 10001 90000 resid[12,11] -1.265 0.3149 0.008007 -1.938 -1.247 -0.7005 10001 90000 sdD[1] 0.2257 0.06088 9.156E-4 0.1369 0.2158 0.3717 4001 96000 sdD[2] 0.6895 0.1811 0.001575 0.4253 0.6598 1.126 4001 96000 sdD[3] 0.2922 0.07246 9.076E-4 0.1847 0.2805 0.4659 4001 96000 sigma0 0.2536 0.05885 3.6E-4 0.1682 0.2439 0.3953 4001 96000 sigma1 0.4022 0.1209 0.002433 0.1996 0.3891 0.664 4001 96000 node mean sd MC error 2.5% median 97.5% start sample # Power model model { for( i in 1 : N ) { for( j in 1 : T ) { eps.tau[i,j] <- 1/(sigma0*sigma0 + sigma1*sigma1*pow(mu[i,j],gamma)) Y[i , j] ~ dnorm(mu[i , j],eps.tau[i,j]) mu[i , j] <- Dose[i]*exp(theta[i,1] + theta[i,2] - theta[i,3]) * (exp(-exp(theta[i,1])*time[i,j]) - exp(-exp(theta[i,2])*time[i,j]) )/(exp(theta[i,2])-exp(theta[i,1])) fitted[i,j] <- mu[i,j] resid[i,j] <- log(Y[i,j])-fitted[i,j] } theta[i, 1:3] ~ dmnorm(beta[1:3], Dinv[1:3, 1:3]) ke[i] <- exp(theta[i,1]) ka[i] <- exp(theta[i,2]) Cl[i] <- exp(theta[i,3]) } sigma0 ~ dunif(0,2) sigma1 ~ dunif(0,2) gamma ~ dunif(0,2) beta[1:3] ~ dmnorm(mean[1:3], prec[1:3, 1:3]) kemed <- exp(beta[1]) kamed <- exp(beta[2]) Clmed <- exp(beta[3]) Dinv[1:3, 1:3] ~ dwish(R[1:3, 1:3], 3) D[1:3, 1:3] <- inverse(Dinv[1:3, 1:3]) for (i in 1 : 3) {sdD[i] <- sqrt(D[i, i]) } } list( N = 12, T = 11,Dose=c(4.02,4.4,4.53,4.4,5.86,4,4.95,4.53,3.1,5.5,4.92,5.3), Y = structure( .Data = c(0.74, 2.84, 6.57, 10.50, 9.66, 8.58, 8.36, 7.47, 6.89, 5.94, 3.28, 0.00, 1.72, 7.91, 8.31 ,8.33, 6.85, 6.08, 5.40, 4.55, 3.01, 0.90, 0.00, 4.40, 6.90, 8.20, 7.80, 7.50, 6.20, 5.30, 4.90, 3.70, 1.05, 0.00, 1.89, 4.60,8.60, 8.38, 7.54, 6.88, 5.78, 5.33, 4.19, 1.15, 0.00, 2.02, 5.63, 11.40, 9.33, 8.74, 7.56, 7.09, 5.90, 4.37, 1.57, 0.00, 1.29, 3.08, 6.44, 6.32, 5.53, 4.94, 4.02, 3.46, 2.78, 0.92, 0.15, 0.85, 2.35, 5.02, 6.58, 7.09, 6.66, 5.25, 4.39, 3.53, 1.15, 0.00, 3.05, 3.05, 7.31, 7.56, 6.59, 5.88,4.73, 4.57, 3.00, 1.25, 0.00, 7.37, 9.03, 7.14 , 6.33 , 5.66 , 5.67, 4.24,4.11, 3.16, 1.12, 0.24 , 2.89 , 5.22, 6.41, 7.83 ,10.21, 9.18, 8.02, 7.14, 5.68, 2.42, 0.00, 4.86, 7.24, 8.00, 6.81, 5.87, 5.22 , 4.45, 3.62 , 2.69, 0.86 , 0.00, 1.25 , 3.96 , 7.82 , 9.72 , 9.75 , 8.57, 6.59, 6.11, 4.57 , 1.17), .Dim = c(12,11)), time = structure( .Data = c(0.00, 0.25 , 0.57 , 1.12 , 2.02, 3.82 , 5.10 , 7.03 , 9.05, 12.12 ,24.37 , 0.00 , 0.27, 0.52, 1.00, 1.92, 3.50, 5.02, 7.03, 9.00, 12.00, 24.30, 0.00, 0.27, 0.58 , 1.02, 2.02 , 3.62 , 5.08, 7.07, 9.00 ,12.15, 24.17 , 0.00 , 0.35 , 0.60 , 1.07 , 2.13 , 3.50 , 5.02 , 7.02 , 9.02 ,11.98 ,24.65 , 0.00 ,0.30 , 0.52 , 1.00, 2.02 , 3.50, 5.02, 7.02 , 9.10, 12.00 ,24.35, 0.00, 0.27, 0.58, 1.15, 2.03, 3.57, 5.00 , 7.00, 9.22, 12.10 ,23.85 , 0.00 , 0.25, 0.50 , 1.02 , 2.02 , 3.48, 5.00, 6.98 , 9.00 ,12.05 ,24.22 , 0.00 , 0.25 , 0.52 , 0.98 , 2.02 , 3.53 , 5.05, 7.15, 9.07, 12.10 ,24.12 , 0.00 ,0.30 , 0.63 , 1.05 , 2.02 , 3.53 , 5.02 , 7.17 , 8.80 ,11.60 ,24.43 , 0.00 , 0.37 , 0.77 , 1.02 , 2.05 , 3.55 , 5.05 , 7.08 , 9.38 , 12.10, 23.70, 0.00, 0.25 , 0.50 , 0.98 , 1.98 , 3.60 , 5.02, 7.03, 9.03 ,12.12 , 24.08 , 0.00 , 0.25 , 0.50 , 1.00 , 2.00, 3.52 , 5.07 , 7.07, 9.03, 12.05 ,24.15 ), .Dim = c(12,11)),mean = c(0, 0, 0),R = structure(.Data = c(0.2, 0, 0, 0, 0.2, 0, 0, 0, 0.2), .Dim = c(3, 3)), prec = structure(.Data = c(1.0E-6, 0, 0, 0, 1.0E-6, 0, 0, 0, 1.0E-6), .Dim = c(3, 3))) # Power model Initial points list(theta = structure( .Data = c(-2.2,0,3,-2.2,0,3,-2.2,0,3,-2.2,0,3,-2.2,0,3,-2.2,0,3,-2.2,0,3,-2.2,0,3,-2.2,0,3,-2.2,0,3,-2.2,0,3,-2.2,0,3), .Dim = c(12, 3)), beta = c(-2, .1, -3), Dinv = structure(.Data = c(1, 0, 0, 0, 1, 0, 0, 0, 1), .Dim = c(3, 3)), sigma0 = .5,sigma1 = .2,gamma=1)