This dataset was used for a comparison of KinGUI and ModelMaker to check the software quality of KinGUI in the original publication (Schäfer et al., 2007). The results from the fitting are also included.
data(schaefer07_complex_case)
The data set is a data frame with 8 observations on the following 6 variables.
timeparentA1B1C1A2The results are a data frame with 14 results for different parameter values
Schäfer D, Mikolasch B, Rainbird P and Harvey B (2007). KinGUI: a new kinetic software tool for evaluations according to FOCUS degradation kinetics. In: Del Re AAM, Capri E, Fragoulis G and Trevisan M (Eds.). Proceedings of the XIII Symposium Pesticide Chemistry, Piacenza, 2007, p. 916-923.
data <- mkin_wide_to_long(schaefer07_complex_case, time = "time") model <- mkinmod( parent = list(type = "SFO", to = c("A1", "B1", "C1"), sink = FALSE), A1 = list(type = "SFO", to = "A2"), B1 = list(type = "SFO"), C1 = list(type = "SFO"), A2 = list(type = "SFO"), use_of_ff = "max")#>#> Model cost at call 1 : 2511.655 #> Model cost at call 2 : 2511.655 #> Model cost at call 11 : 1436.639 #> Model cost at call 12 : 1436.638 #> Model cost at call 13 : 1436.566 #> Model cost at call 21 : 643.6583 #> Model cost at call 22 : 643.6583 #> Model cost at call 23 : 643.6582 #> Model cost at call 29 : 643.6576 #> Model cost at call 31 : 454.0244 #> Model cost at call 32 : 454.0241 #> Model cost at call 34 : 454.0229 #> Model cost at call 43 : 378.1144 #> Model cost at call 45 : 378.1143 #> Model cost at call 53 : 357.245 #> Model cost at call 55 : 357.2449 #> Model cost at call 56 : 357.2447 #> Model cost at call 63 : 354.3415 #> Model cost at call 64 : 354.3415 #> Model cost at call 65 : 354.3413 #> Model cost at call 73 : 332.49 #> Model cost at call 74 : 332.49 #> Model cost at call 81 : 332.4899 #> Model cost at call 83 : 315.2962 #> Model cost at call 84 : 306.3085 #> Model cost at call 86 : 306.3084 #> Model cost at call 87 : 306.3084 #> Model cost at call 92 : 306.3083 #> Model cost at call 94 : 290.6377 #> Model cost at call 96 : 290.6375 #> Model cost at call 98 : 290.6375 #> Model cost at call 101 : 290.6371 #> Model cost at call 105 : 269.09 #> Model cost at call 107 : 269.0899 #> Model cost at call 115 : 259.7551 #> Model cost at call 120 : 259.7549 #> Model cost at call 123 : 259.7547 #> Model cost at call 126 : 253.7973 #> Model cost at call 128 : 253.7972 #> Model cost at call 137 : 251.7358 #> Model cost at call 139 : 251.7358 #> Model cost at call 147 : 250.7394 #> Model cost at call 149 : 250.7393 #> Model cost at call 157 : 249.1148 #> Model cost at call 159 : 249.1148 #> Model cost at call 167 : 246.8768 #> Model cost at call 169 : 246.8768 #> Model cost at call 177 : 244.9758 #> Model cost at call 179 : 244.9758 #> Model cost at call 187 : 243.2914 #> Model cost at call 189 : 243.2914 #> Model cost at call 190 : 243.2914 #> Model cost at call 194 : 243.2914 #> Model cost at call 199 : 242.9202 #> Model cost at call 201 : 242.9202 #> Model cost at call 202 : 242.9202 #> Model cost at call 209 : 242.7695 #> Model cost at call 211 : 242.7695 #> Model cost at call 216 : 242.7695 #> Model cost at call 219 : 242.5771 #> Model cost at call 221 : 242.5771 #> Model cost at call 229 : 242.4402 #> Model cost at call 231 : 242.4402 #> Model cost at call 239 : 242.1878 #> Model cost at call 241 : 242.1878 #> Model cost at call 249 : 242.0553 #> Model cost at call 251 : 242.0553 #> Model cost at call 256 : 242.0553 #> Model cost at call 259 : 241.8761 #> Model cost at call 260 : 241.7412 #> Model cost at call 261 : 241.6954 #> Model cost at call 264 : 241.6954 #> Model cost at call 275 : 241.5982 #> Model cost at call 277 : 241.5982 #> Model cost at call 285 : 241.5459 #> Model cost at call 287 : 241.5459 #> Model cost at call 295 : 241.4837 #> Model cost at call 297 : 241.4837 #> Model cost at call 305 : 241.3882 #> Model cost at call 306 : 241.3161 #> Model cost at call 307 : 241.2315 #> Model cost at call 309 : 241.2315 #> Model cost at call 314 : 241.2315 #> Model cost at call 317 : 240.9738 #> Model cost at call 322 : 240.9738 #> Model cost at call 327 : 240.8244 #> Model cost at call 329 : 240.8244 #> Model cost at call 337 : 240.7005 #> Model cost at call 339 : 240.7005 #> Model cost at call 342 : 240.7005 #> Model cost at call 347 : 240.629 #> Model cost at call 350 : 240.629 #> Model cost at call 357 : 240.6193 #> Model cost at call 358 : 240.6193 #> Model cost at call 364 : 240.6193 #> Model cost at call 367 : 240.6193 #> Model cost at call 369 : 240.5873 #> Model cost at call 374 : 240.5873 #> Model cost at call 380 : 240.578 #> Model cost at call 382 : 240.578 #> Model cost at call 390 : 240.5723 #> Model cost at call 393 : 240.5723 #> Model cost at call 403 : 240.569 #> Model cost at call 404 : 240.569 #> Model cost at call 413 : 240.569 #> Model cost at call 415 : 240.5688 #> Model cost at call 416 : 240.5688 #> Model cost at call 417 : 240.5688 #> Model cost at call 431 : 240.5686 #> Model cost at call 432 : 240.5686 #> Model cost at call 434 : 240.5686 #> Model cost at call 443 : 240.5686 #> Model cost at call 444 : 240.5686 #> Model cost at call 447 : 240.5686 #> Model cost at call 449 : 240.5686 #> Model cost at call 450 : 240.5686 #> Model cost at call 466 : 240.5686 #> Model cost at call 470 : 240.5686 #> Model cost at call 485 : 240.5686 #> Model cost at call 509 : 240.5686 #> Optimisation by method Port successfully terminated.#> $par #> parent_0 log_k_parent log_k_A1 log_k_B1 log_k_C1 #> 91.9181598 -3.0020485 -4.2735924 -3.9846764 -2.7852180 #> log_k_A2 f_parent_ilr_1 f_parent_ilr_2 f_A1_ilr_1 #> -3.7166415 0.4718588 -0.3589948 -0.1477244 #> #> $ssr #> [1] 240.5686 #> #> $convergence #> [1] 0 #> #> $iterations #> [1] 43 #> #> $evaluations #> function gradient #> 62 441 #> #> $counts #> [1] "relative convergence (4)" #> #> $hessian #> parent_0 log_k_parent log_k_A1 log_k_B1 #> parent_0 7.3650812 -92.141920 -1.001134e+01 -2.432415e+00 #> log_k_parent -92.1419204 6632.673492 -4.316240e+01 -1.320833e+01 #> log_k_A1 -10.0113364 -43.162398 6.071628e+02 0.000000e+00 #> log_k_B1 -2.4324147 -13.208329 0.000000e+00 1.572303e+02 #> log_k_C1 -4.7153201 -118.288037 -5.878291e-05 -3.073041e-06 #> log_k_A2 -0.4360727 -5.304259 -1.977980e+01 0.000000e+00 #> f_parent_ilr_1 10.5460899 271.145438 -5.299954e+02 1.874235e+02 #> f_parent_ilr_2 11.6409409 222.570696 -4.773816e+02 -1.159875e+02 #> f_A1_ilr_1 0.5572072 10.374810 2.850173e+01 0.000000e+00 #> log_k_C1 log_k_A2 f_parent_ilr_1 f_parent_ilr_2 #> parent_0 -4.715320e+00 -4.360727e-01 10.54609 11.64094 #> log_k_parent -1.182880e+02 -5.304259e+00 271.14544 222.57070 #> log_k_A1 -5.878291e-05 -1.977980e+01 -529.99537 -477.38164 #> log_k_B1 -3.073041e-06 0.000000e+00 187.42348 -115.98754 #> log_k_C1 3.372749e+02 -2.395674e-06 56.85184 305.98862 #> log_k_A2 -2.395674e-06 2.749192e+01 -23.08549 -20.79373 #> f_parent_ilr_1 5.685184e+01 -2.308549e+01 1256.24941 632.09769 #> f_parent_ilr_2 3.059886e+02 -2.079373e+01 632.09769 1250.65147 #> f_A1_ilr_1 3.158891e-06 -3.129286e+01 29.49830 26.56991 #> f_A1_ilr_1 #> parent_0 5.572072e-01 #> log_k_parent 1.037481e+01 #> log_k_A1 2.850173e+01 #> log_k_B1 0.000000e+00 #> log_k_C1 3.158891e-06 #> log_k_A2 -3.129286e+01 #> f_parent_ilr_1 2.949830e+01 #> f_parent_ilr_2 2.656991e+01 #> f_A1_ilr_1 3.998554e+01 #> #> $residuals #> parent parent parent parent parent parent parent #> -1.2818402 -1.9372115 -0.5105519 3.8165318 -2.3531716 4.8043342 -2.2775432 #> parent A1 A1 A1 A1 A1 A1 #> -5.3608524 4.1967522 2.9032987 -1.3124875 -0.6021093 2.5092324 -1.8861396 #> B1 B1 B1 B1 B1 C1 C1 #> 4.3801768 5.5002481 -5.7917184 1.3852658 0.5313301 1.2796458 1.7105311 #> C1 C1 C1 C1 C1 A2 A2 #> 3.7116712 -0.1182953 0.5228429 -0.8570298 -3.5476556 -0.5447276 -1.3652404 #> A2 A2 A2 A2 A2 #> -0.3330261 -0.5802059 0.1285850 0.2119280 -0.1381990 #> #> $ms #> [1] 7.289956 #> #> $var_ms #> parent A1 B1 C1 A2 #> 10.3459333 6.3301336 17.0367907 4.5639474 0.3841002 #> #> $var_ms_unscaled #> parent A1 B1 C1 A2 #> 10.3459333 6.3301336 17.0367907 4.5639474 0.3841002 #> #> $var_ms_unweighted #> parent A1 B1 C1 A2 #> 10.3459333 6.3301336 17.0367907 4.5639474 0.3841002 #> #> $rank #> [1] 9 #> #> $df.residual #> [1] 24 #> #> $solution_type #> [1] "deSolve" #> #> $transform_rates #> [1] TRUE #> #> $transform_fractions #> [1] TRUE #> #> $method.modFit #> [1] "Port" #> #> $maxit.modFit #> [1] "auto" #> #> $calls #> [1] 523 #> #> $time #> user system elapsed #> 5.004 0.000 5.004 #> #> $mkinmod #> <mkinmod> model generated with #> Use of formation fractions $use_of_ff: max #> Specification $spec: #> $parent #> $type: SFO; $to: A1, B1, C1; $sink: FALSE #> $A1 #> $type: SFO; $to: A2; $sink: TRUE #> $B1 #> $type: SFO; $sink: TRUE #> $C1 #> $type: SFO; $sink: TRUE #> $A2 #> $type: SFO; $sink: TRUE #> Coefficient matrix $coefmat available #> Compiled model $cf available #> #> $observed #> name time value #> 1 parent 0 93.20 #> 2 parent 1 89.40 #> 3 parent 3 79.70 #> 4 parent 7 61.10 #> 5 parent 14 48.20 #> 6 parent 30 15.90 #> 7 parent 62 6.50 #> 8 parent 100 6.00 #> 9 A1 0 NA #> 10 A1 1 NA #> 11 A1 3 0.55 #> 12 A1 7 6.87 #> 13 A1 14 17.08 #> 14 A1 30 21.68 #> 15 A1 62 15.77 #> 16 A1 100 13.63 #> 17 B1 0 NA #> 18 B1 1 NA #> 19 B1 3 NA #> 20 B1 7 0.55 #> 21 B1 14 2.31 #> 22 B1 30 15.76 #> 23 B1 62 6.36 #> 24 B1 100 3.74 #> 25 C1 0 NA #> 26 C1 1 0.55 #> 27 C1 3 3.20 #> 28 C1 7 5.46 #> 29 C1 14 12.55 #> 30 C1 30 10.45 #> 31 C1 62 4.74 #> 32 C1 100 4.33 #> 33 A2 0 NA #> 34 A2 1 0.55 #> 35 A2 3 1.41 #> 36 A2 7 0.55 #> 37 A2 14 1.29 #> 38 A2 30 1.95 #> 39 A2 62 3.54 #> 40 A2 100 3.86 #> #> $obs_vars #> [1] "parent" "A1" "B1" "C1" "A2" #> #> $predicted #> name time value #> 1 parent 0.000000 91.918159794 #> 2 parent 1.000000 87.462788491 #> 3 parent 1.010101 87.418904506 #> 4 parent 2.020202 83.139880984 #> 5 parent 3.000000 79.189448055 #> 6 parent 3.030303 79.070309209 #> 7 parent 4.040404 75.199936833 #> 8 parent 5.050505 71.519013349 #> 9 parent 6.060606 68.018265517 #> 10 parent 7.000000 64.916531757 #> 11 parent 7.070707 64.688874011 #> 12 parent 8.080808 61.522451197 #> 13 parent 9.090909 58.511020005 #> 14 parent 10.101010 55.646993828 #> 15 parent 11.111111 52.923157412 #> 16 parent 12.121212 50.332648680 #> 17 parent 13.131313 47.868941444 #> 18 parent 14.000000 45.846828365 #> 19 parent 14.141414 45.525828960 #> 20 parent 15.151515 43.297408299 #> 21 parent 16.161616 41.178065468 #> 22 parent 17.171717 39.162461272 #> 23 parent 18.181818 37.245517861 #> 24 parent 19.191919 35.422405939 #> 25 parent 20.202020 33.688532595 #> 26 parent 21.212121 32.039529737 #> 27 parent 22.222222 30.471243081 #> 28 parent 23.232323 28.979721692 #> 29 parent 24.242424 27.561208025 #> 30 parent 25.252525 26.212128463 #> 31 parent 26.262626 24.929084310 #> 32 parent 27.272727 23.708843233 #> 33 parent 28.282828 22.548331117 #> 34 parent 29.292929 21.444624318 #> 35 parent 30.000000 20.704334210 #> 36 parent 30.303030 20.394942302 #> 37 parent 31.313131 19.396640638 #> 38 parent 32.323232 18.447204335 #> 39 parent 33.333333 17.544241506 #> 40 parent 34.343434 16.685477346 #> 41 parent 35.353535 15.868748397 #> 42 parent 36.363636 15.091997098 #> 43 parent 37.373737 14.353266603 #> 44 parent 38.383838 13.650695852 #> 45 parent 39.393939 12.982514879 #> 46 parent 40.404040 12.347040357 #> 47 parent 41.414141 11.742671354 #> 48 parent 42.424242 11.167885303 #> 49 parent 43.434343 10.621234162 #> 50 parent 44.444444 10.101340770 #> 51 parent 45.454545 9.606895375 #> 52 parent 46.464646 9.136652336 #> 53 parent 47.474747 8.689426985 #> 54 parent 48.484848 8.264092640 #> 55 parent 49.494949 7.859577770 #> 56 parent 50.505051 7.474863293 #> 57 parent 51.515152 7.108980009 #> 58 parent 52.525253 6.761006160 #> 59 parent 53.535354 6.430065106 #> 60 parent 54.545455 6.115323117 #> 61 parent 55.555556 5.815987274 #> 62 parent 56.565657 5.531303470 #> 63 parent 57.575758 5.260554508 #> 64 parent 58.585859 5.003058299 #> 65 parent 59.595960 4.758166141 #> 66 parent 60.606061 4.525261085 #> 67 parent 61.616162 4.303756381 #> 68 parent 62.000000 4.222456793 #> 69 parent 62.626263 4.093093997 #> 70 parent 63.636364 3.892743220 #> 71 parent 64.646465 3.702199310 #> 72 parent 65.656566 3.520982238 #> 73 parent 66.666667 3.348635468 #> 74 parent 67.676768 3.184724813 #> 75 parent 68.686869 3.028837337 #> 76 parent 69.696970 2.880580317 #> 77 parent 70.707071 2.739580256 #> 78 parent 71.717172 2.605481934 #> 79 parent 72.727273 2.477947523 #> 80 parent 73.737374 2.356655730 #> 81 parent 74.747475 2.241300986 #> 82 parent 75.757576 2.131592683 #> 83 parent 76.767677 2.027254437 #> 84 parent 77.777778 1.928023390 #> 85 parent 78.787879 1.833649553 #> 86 parent 79.797980 1.743895173 #> 87 parent 80.808081 1.658534134 #> 88 parent 81.818182 1.577351390 #> 89 parent 82.828283 1.500142419 #> 90 parent 83.838384 1.426712710 #> 91 parent 84.848485 1.356877275 #> 92 parent 85.858586 1.290460179 #> 93 parent 86.868687 1.227294099 #> 94 parent 87.878788 1.167219904 #> 95 parent 88.888889 1.110086250 #> 96 parent 89.898990 1.055749203 #> 97 parent 90.909091 1.004071872 #> 98 parent 91.919192 0.954924068 #> 99 parent 92.929293 0.908181975 #> 100 parent 93.939394 0.863727837 #> 101 parent 94.949495 0.821449662 #> 102 parent 95.959596 0.781240940 #> 103 parent 96.969697 0.743000375 #> 104 parent 97.979798 0.706631627 #> 105 parent 98.989899 0.672043075 #> 106 parent 100.000000 0.639147580 #> 107 A1 0.000000 0.000000000 #> 108 A1 1.000000 1.685461006 #> 109 A1 1.010101 1.701940789 #> 110 A1 2.020202 3.296791533 #> 111 A1 3.000000 4.746752202 #> 112 A1 3.030303 4.790126465 #> 113 A1 4.040404 6.187242320 #> 114 A1 5.050505 7.493171988 #> 115 A1 6.060606 8.712697491 #> 116 A1 7.000000 9.773298725 #> 117 A1 7.070707 9.850362326 #> 118 A1 8.080808 10.910483202 #> 119 A1 9.090909 11.897161206 #> 120 A1 10.101010 12.814292412 #> 121 A1 11.111111 13.665577981 #> 122 A1 12.121212 14.454533757 #> 123 A1 13.131313 15.184499397 #> 124 A1 14.000000 15.767512526 #> 125 A1 14.141414 15.858647054 #> 126 A1 15.151515 16.479989628 #> 127 A1 16.161616 17.051388624 #> 128 A1 17.171717 17.575561608 #> 129 A1 18.181818 18.055089316 #> 130 A1 19.191919 18.492422399 #> 131 A1 20.202020 18.889887843 #> 132 A1 21.212121 19.249695079 #> 133 A1 22.222222 19.573941783 #> 134 A1 23.232323 19.864619397 #> 135 A1 24.242424 20.123618383 #> 136 A1 25.252525 20.352733211 #> 137 A1 26.262626 20.553667106 #> 138 A1 27.272727 20.728036563 #> 139 A1 28.282828 20.877375640 #> 140 A1 29.292929 21.003140039 #> 141 A1 30.000000 21.077890710 #> 142 A1 30.303030 21.106710984 #> 143 A1 31.313131 21.189398917 #> 144 A1 32.323232 21.252447002 #> 145 A1 33.333333 21.297034466 #> 146 A1 34.343434 21.324279770 #> 147 A1 35.353535 21.335243623 #> 148 A1 36.363636 21.330931858 #> 149 A1 37.373737 21.312298151 #> 150 A1 38.383838 21.280246621 #> 151 A1 39.393939 21.235634295 #> 152 A1 40.404040 21.179273450 #> 153 A1 41.414141 21.111933845 #> 154 A1 42.424242 21.034344838 #> 155 A1 43.434343 20.947197407 #> 156 A1 44.444444 20.851146060 #> 157 A1 45.454545 20.746810660 #> 158 A1 46.464646 20.634778158 #> 159 A1 47.474747 20.515604239 #> 160 A1 48.484848 20.389814887 #> 161 A1 49.494949 20.257907875 #> 162 A1 50.505051 20.120354180 #> 163 A1 51.515152 19.977599327 #> 164 A1 52.525253 19.830064674 #> 165 A1 53.535354 19.678148618 #> 166 A1 54.545455 19.522227762 #> 167 A1 55.555556 19.362658007 #> 168 A1 56.565657 19.199775600 #> 169 A1 57.575758 19.033898126 #> 170 A1 58.585859 18.865325451 #> 171 A1 59.595960 18.694340625 #> 172 A1 60.606061 18.521210729 #> 173 A1 61.616162 18.346187688 #> 174 A1 62.000000 18.279232408 #> 175 A1 62.626263 18.169509043 #> 176 A1 63.636364 17.991398686 #> 177 A1 64.646465 17.812067549 #> 178 A1 65.656566 17.631714275 #> 179 A1 66.666667 17.450525840 #> 180 A1 67.676768 17.268678156 #> 181 A1 68.686869 17.086336636 #> 182 A1 69.696970 16.903656738 #> 183 A1 70.707071 16.720784474 #> 184 A1 71.717172 16.537856901 #> 185 A1 72.727273 16.355002582 #> 186 A1 73.737374 16.172342031 #> 187 A1 74.747475 15.989988127 #> 188 A1 75.757576 15.808046514 #> 189 A1 76.767677 15.626615980 #> 190 A1 77.777778 15.445788814 #> 191 A1 78.787879 15.265651148 #> 192 A1 79.797980 15.086283284 #> 193 A1 80.808081 14.907759996 #> 194 A1 81.818182 14.730150830 #> 195 A1 82.828283 14.553520376 #> 196 A1 83.838384 14.377928535 #> 197 A1 84.848485 14.203430771 #> 198 A1 85.858586 14.030078345 #> 199 A1 86.868687 13.857918547 #> 200 A1 87.878788 13.686994907 #> 201 A1 88.888889 13.517347398 #> 202 A1 89.898990 13.349012635 #> 203 A1 90.909091 13.182024056 #> 204 A1 91.919192 13.016412097 #> 205 A1 92.929293 12.852204356 #> 206 A1 93.939394 12.689425755 #> 207 A1 94.949495 12.528098688 #> 208 A1 95.959596 12.368243159 #> 209 A1 96.969697 12.209876925 #> 210 A1 97.979798 12.053015616 #> 211 A1 98.989899 11.897672861 #> 212 A1 100.000000 11.743860400 #> 213 B1 0.000000 0.000000000 #> 214 B1 1.000000 0.862762059 #> 215 B1 1.010101 0.871177048 #> 216 B1 2.020202 1.683497848 #> 217 B1 3.000000 2.418226457 #> 218 B1 3.030303 2.440145075 #> 219 B1 4.040404 3.144139999 #> 220 B1 5.050505 3.798350490 #> 221 B1 6.060606 4.405498633 #> 222 B1 7.000000 4.930176837 #> 223 B1 7.070707 4.968167964 #> 224 B1 8.080808 5.488810347 #> 225 B1 9.090909 5.969752521 #> 226 B1 10.101010 6.413202316 #> 227 B1 11.111111 6.821254568 #> 228 B1 12.121212 7.195896744 #> 229 B1 13.131313 7.539014282 #> 230 B1 14.000000 7.810248132 #> 231 B1 14.141414 7.852395679 #> 232 B1 15.151515 8.137737320 #> 233 B1 16.161616 8.396648072 #> 234 B1 17.171717 8.630653651 #> 235 B1 18.181818 8.841200774 #> 236 B1 19.191919 9.029661109 #> 237 B1 20.202020 9.197335022 #> 238 B1 21.212121 9.345455150 #> 239 B1 22.222222 9.475189788 #> 240 B1 23.232323 9.587646116 #> 241 B1 24.242424 9.683873262 #> 242 B1 25.252525 9.764865214 #> 243 B1 26.262626 9.831563593 #> 244 B1 27.272727 9.884860284 #> 245 B1 28.282828 9.925599936 #> 246 B1 29.292929 9.954582344 #> 247 B1 30.000000 9.968281596 #> 248 B1 30.303030 9.972564708 #> 249 B1 31.313131 9.980263783 #> 250 B1 32.323232 9.978357919 #> 251 B1 33.333333 9.967489009 #> 252 B1 34.343434 9.948264327 #> 253 B1 35.353535 9.921258285 #> 254 B1 36.363636 9.887014102 #> 255 B1 37.373737 9.846045383 #> 256 B1 38.383838 9.798837632 #> 257 B1 39.393939 9.745849674 #> 258 B1 40.404040 9.687515023 #> 259 B1 41.414141 9.624243169 #> 260 B1 42.424242 9.556420809 #> 261 B1 43.434343 9.484413012 #> 262 B1 44.444444 9.408564328 #> 263 B1 45.454545 9.329199843 #> 264 B1 46.464646 9.246626179 #> 265 B1 47.474747 9.161132446 #> 266 B1 48.484848 9.072991146 #> 267 B1 49.494949 8.982459028 #> 268 B1 50.505051 8.889777910 #> 269 B1 51.515152 8.795175451 #> 270 B1 52.525253 8.698865886 #> 271 B1 53.535354 8.601050726 #> 272 B1 54.545455 8.501919425 #> 273 B1 55.555556 8.401650008 #> 274 B1 56.565657 8.300409672 #> 275 B1 57.575758 8.198355355 #> 276 B1 58.585859 8.095634277 #> 277 B1 59.595960 7.992384454 #> 278 B1 60.606061 7.888735183 #> 279 B1 61.616162 7.784807509 #> 280 B1 62.000000 7.745265792 #> 281 B1 62.626263 7.680714664 #> 282 B1 63.636364 7.576562482 #> 283 B1 64.646465 7.472449799 #> 284 B1 65.656566 7.368468826 #> 285 B1 66.666667 7.264705509 #> 286 B1 67.676768 7.161239868 #> 287 B1 68.686869 7.058146319 #> 288 B1 69.696970 6.955493978 #> 289 B1 70.707071 6.853346953 #> 290 B1 71.717172 6.751764620 #> 291 B1 72.727273 6.650801882 #> 292 B1 73.737374 6.550509419 #> 293 B1 74.747475 6.450933922 #> 294 B1 75.757576 6.352118318 #> 295 B1 76.767677 6.254101979 #> 296 B1 77.777778 6.156920928 #> 297 B1 78.787879 6.060608023 #> 298 B1 79.797980 5.965193142 #> 299 B1 80.808081 5.870703355 #> 300 B1 81.818182 5.777163083 #> 301 B1 82.828283 5.684594257 #> 302 B1 83.838384 5.593016458 #> 303 B1 84.848485 5.502447062 #> 304 B1 85.858586 5.412901366 #> 305 B1 86.868687 5.324392718 #> 306 B1 87.878788 5.236932630 #> 307 B1 88.888889 5.150530889 #> 308 B1 89.898990 5.065195670 #> 309 B1 90.909091 4.980933628 #> 310 B1 91.919192 4.897749999 #> 311 B1 92.929293 4.815648688 #> 312 B1 93.939394 4.734632351 #> 313 B1 94.949495 4.654702481 #> 314 B1 95.959596 4.575859481 #> 315 B1 96.969697 4.498102737 #> 316 B1 97.979798 4.421430686 #> 317 B1 98.989899 4.345840882 #> 318 B1 100.000000 4.271330056 #> 319 C1 0.000000 0.000000000 #> 320 C1 1.000000 1.829645786 #> 321 C1 1.010101 1.847087763 #> 322 C1 2.020202 3.492133303 #> 323 C1 3.000000 4.910531064 #> 324 C1 3.030303 4.951772742 #> 325 C1 4.040404 6.241420142 #> 326 C1 5.050505 7.375351980 #> 327 C1 6.060606 8.366785999 #> 328 C1 7.000000 9.171671206 #> 329 C1 7.070707 9.227954769 #> 330 C1 8.080808 9.970174354 #> 331 C1 9.090909 10.603908370 #> 332 C1 10.101010 11.138827767 #> 333 C1 11.111111 11.583866567 #> 334 C1 12.121212 11.947273869 #> 335 C1 13.131313 12.236662337 #> 336 C1 14.000000 12.431704739 #> 337 C1 14.141414 12.459053419 #> 338 C1 15.151515 12.620919488 #> 339 C1 16.161616 12.728223141 #> 340 C1 17.171717 12.786453805 #> 341 C1 18.181818 12.800661859 #> 342 C1 19.191919 12.775490422 #> 343 C1 20.202020 12.715204956 #> 344 C1 21.212121 12.623720845 #> 345 C1 22.222222 12.504629065 #> 346 C1 23.232323 12.361220091 #> 347 C1 24.242424 12.196506142 #> 348 C1 25.252525 12.013241882 #> 349 C1 26.262626 11.813943686 #> 350 C1 27.272727 11.600907551 #> 351 C1 28.282828 11.376225763 #> 352 C1 29.292929 11.141802382 #> 353 C1 30.000000 10.972842888 #> 354 C1 30.303030 10.899367648 #> 355 C1 31.313131 10.650491354 #> 356 C1 32.323232 10.396595286 #> 357 C1 33.333333 10.138964763 #> 358 C1 34.343434 9.878759358 #> 359 C1 35.353535 9.617022857 #> 360 C1 36.363636 9.354692485 #> 361 C1 37.373737 9.092607481 #> 362 C1 38.383838 8.831517041 #> 363 C1 39.393939 8.572087685 #> 364 C1 40.404040 8.314910084 #> 365 C1 41.414141 8.060505385 #> 366 C1 42.424242 7.809331068 #> 367 C1 43.434343 7.561786371 #> 368 C1 44.444444 7.318217302 #> 369 C1 45.454545 7.078921287 #> 370 C1 46.464646 6.844151456 #> 371 C1 47.474747 6.614120611 #> 372 C1 48.484848 6.389004885 #> 373 C1 49.494949 6.168947129 #> 374 C1 50.505051 5.954060026 #> 375 C1 51.515152 5.744428970 #> 376 C1 52.525253 5.540114721 #> 377 C1 53.535354 5.341155842 #> 378 C1 54.545455 5.147570951 #> 379 C1 55.555556 4.959360784 #> 380 C1 56.565657 4.776510102 #> 381 C1 57.575758 4.598989433 #> 382 C1 58.585859 4.426756673 #> 383 C1 59.595960 4.259758556 #> 384 C1 60.606061 4.097932000 #> 385 C1 61.616162 3.941205338 #> 386 C1 62.000000 3.882970158 #> 387 C1 62.626263 3.789499444 #> 388 C1 63.636364 3.642728760 #> 389 C1 64.646465 3.500802233 #> 390 C1 65.656566 3.363624171 #> 391 C1 66.666667 3.231095021 #> 392 C1 67.676768 3.103112069 #> 393 C1 68.686869 2.979570086 #> 394 C1 69.696970 2.860361903 #> 395 C1 70.707071 2.745378939 #> 396 C1 71.717172 2.634511667 #> 397 C1 72.727273 2.527650041 #> 398 C1 73.737374 2.424683880 #> 399 C1 74.747475 2.325503203 #> 400 C1 75.757576 2.229998536 #> 401 C1 76.767677 2.138061182 #> 402 C1 77.777778 2.049583458 #> 403 C1 78.787879 1.964458908 #> 404 C1 79.797980 1.882582485 #> 405 C1 80.808081 1.803850715 #> 406 C1 81.818182 1.728161832 #> 407 C1 82.828283 1.655415900 #> 408 C1 83.838384 1.585514911 #> 409 C1 84.848485 1.518362874 #> 410 C1 85.858586 1.453865880 #> 411 C1 86.868687 1.391932162 #> 412 C1 87.878788 1.332472134 #> 413 C1 88.888889 1.275398429 #> 414 C1 89.898990 1.220625918 #> 415 C1 90.909091 1.168071723 #> 416 C1 91.919192 1.117655227 #> 417 C1 92.929293 1.069298066 #> 418 C1 93.939394 1.022924125 #> 419 C1 94.949495 0.978459525 #> 420 C1 95.959596 0.935832597 #> 421 C1 96.969697 0.894973866 #> 422 C1 97.979798 0.855816021 #> 423 C1 98.989899 0.818293881 #> 424 C1 100.000000 0.782344364 #> 425 A2 0.000000 0.000000000 #> 426 A2 1.000000 0.005272357 #> 427 A2 1.010101 0.005377817 #> 428 A2 2.020202 0.020885524 #> 429 A2 3.000000 0.044759575 #> 430 A2 3.030303 0.045628064 #> 431 A2 4.040404 0.078765936 #> 432 A2 5.050505 0.119512155 #> 433 A2 6.060606 0.167129381 #> 434 A2 7.000000 0.216973934 #> 435 A2 7.070707 0.220927189 #> 436 A2 8.080808 0.280259484 #> 437 A2 9.090909 0.344522046 #> 438 A2 10.101010 0.413150206 #> 439 A2 11.111111 0.485616641 #> 440 A2 12.121212 0.561429288 #> 441 A2 13.131313 0.640129357 #> 442 A2 14.000000 0.709794102 #> 443 A2 14.141414 0.721289460 #> 444 A2 15.151515 0.804511827 #> 445 A2 16.161616 0.889426625 #> 446 A2 17.171717 0.975690359 #> 447 A2 18.181818 1.062984358 #> 448 A2 19.191919 1.151013342 #> 449 A2 20.202020 1.239504068 #> 450 A2 21.212121 1.328204041 #> 451 A2 22.222222 1.416880297 #> 452 A2 23.232323 1.505318253 #> 453 A2 24.242424 1.593320615 #> 454 A2 25.252525 1.680706344 #> 455 A2 26.262626 1.767309680 #> 456 A2 27.272727 1.852979219 #> 457 A2 28.282828 1.937577034 #> 458 A2 29.292929 2.020977853 #> 459 A2 30.000000 2.078585030 #> 460 A2 30.303030 2.103068270 #> 461 A2 31.313131 2.183746011 #> 462 A2 32.323232 2.262919231 #> 463 A2 33.333333 2.340505852 #> 464 A2 34.343434 2.416432940 #> 465 A2 35.353535 2.490636111 #> 466 A2 36.363636 2.563058979 #> 467 A2 37.373737 2.633652622 #> 468 A2 38.383838 2.702375089 #> 469 A2 39.393939 2.769190926 #> 470 A2 40.404040 2.834070737 #> 471 A2 41.414141 2.896990764 #> 472 A2 42.424242 2.957932489 #> 473 A2 43.434343 3.016882265 #> 474 A2 44.444444 3.073830964 #> 475 A2 45.454545 3.128773647 #> 476 A2 46.464646 3.181709250 #> 477 A2 47.474747 3.232640290 #> 478 A2 48.484848 3.281572591 #> 479 A2 49.494949 3.328515022 #> 480 A2 50.505051 3.373479253 #> 481 A2 51.515152 3.416479521 #> 482 A2 52.525253 3.457532417 #> 483 A2 53.535354 3.496656681 #> 484 A2 54.545455 3.533873012 #> 485 A2 55.555556 3.569203883 #> 486 A2 56.565657 3.602673379 #> 487 A2 57.575758 3.634307034 #> 488 A2 58.585859 3.664131686 #> 489 A2 59.595960 3.692175334 #> 490 A2 60.606061 3.718467012 #> 491 A2 61.616162 3.743036663 #> 492 A2 62.000000 3.751927986 #> 493 A2 62.626263 3.765915028 #> 494 A2 63.636364 3.787133539 #> 495 A2 64.646465 3.806724217 #> 496 A2 65.656566 3.824719582 #> 497 A2 66.666667 3.841152565 #> 498 A2 67.676768 3.856056426 #> 499 A2 68.686869 3.869464684 #> 500 A2 69.696970 3.881411040 #> 501 A2 70.707071 3.891929316 #> 502 A2 71.717172 3.901053396 #> 503 A2 72.727273 3.908817168 #> 504 A2 73.737374 3.915254472 #> 505 A2 74.747475 3.920399054 #> 506 A2 75.757576 3.924284521 #> 507 A2 76.767677 3.926944303 #> 508 A2 77.777778 3.928411610 #> 509 A2 78.787879 3.928719404 #> 510 A2 79.797980 3.927900364 #> 511 A2 80.808081 3.925986861 #> 512 A2 81.818182 3.923010926 #> 513 A2 82.828283 3.919004234 #> 514 A2 83.838384 3.913998077 #> 515 A2 84.848485 3.908023347 #> 516 A2 85.858586 3.901110518 #> 517 A2 86.868687 3.893289633 #> 518 A2 87.878788 3.884590288 #> 519 A2 88.888889 3.875041619 #> 520 A2 89.898990 3.864672297 #> 521 A2 90.909091 3.853510511 #> 522 A2 91.919192 3.841583970 #> 523 A2 92.929293 3.828919886 #> 524 A2 93.939394 3.815544978 #> 525 A2 94.949495 3.801485462 #> 526 A2 95.959596 3.786767051 #> 527 A2 96.969697 3.771414951 #> 528 A2 97.979798 3.755453860 #> 529 A2 98.989899 3.738907968 #> 530 A2 100.000000 3.721800959 #> #> $cost #> function (P) #> { #> assign("calls", calls + 1, inherits = TRUE) #> if (trace_parms) #> cat(P, "\n") #> if (length(state.ini.optim) > 0) { #> odeini <- c(P[1:length(state.ini.optim)], state.ini.fixed) #> names(odeini) <- c(state.ini.optim.boxnames, state.ini.fixed.boxnames) #> } #> else { #> odeini <- state.ini.fixed #> names(odeini) <- state.ini.fixed.boxnames #> } #> odeparms <- c(P[(length(state.ini.optim) + 1):length(P)], #> transparms.fixed) #> parms <- backtransform_odeparms(odeparms, mkinmod, transform_rates = transform_rates, #> transform_fractions = transform_fractions) #> out <- mkinpredict(mkinmod, parms, odeini, outtimes, solution_type = solution_type, #> use_compiled = use_compiled, method.ode = method.ode, #> atol = atol, rtol = rtol, ...) #> assign("out_predicted", out, inherits = TRUE) #> mC <- modCost(out, observed, y = "value", err = err, weight = weight, #> scaleVar = scaleVar) #> if (mC$model < cost.old) { #> if (!quiet) #> cat("Model cost at call ", calls, ": ", mC$model, #> "\n") #> if (plot) { #> outtimes_plot = seq(min(observed$time), max(observed$time), #> length.out = 100) #> out_plot <- mkinpredict(mkinmod, parms, odeini, outtimes_plot, #> solution_type = solution_type, use_compiled = use_compiled, #> method.ode = method.ode, atol = atol, rtol = rtol, #> ...) #> plot(0, type = "n", xlim = range(observed$time), #> ylim = c(0, max(observed$value, na.rm = TRUE)), #> xlab = "Time", ylab = "Observed") #> col_obs <- pch_obs <- 1:length(obs_vars) #> lty_obs <- rep(1, length(obs_vars)) #> names(col_obs) <- names(pch_obs) <- names(lty_obs) <- obs_vars #> for (obs_var in obs_vars) { #> points(subset(observed, name == obs_var, c(time, #> value)), pch = pch_obs[obs_var], col = col_obs[obs_var]) #> } #> matlines(out_plot$time, out_plot[-1], col = col_obs, #> lty = lty_obs) #> legend("topright", inset = c(0.05, 0.05), legend = obs_vars, #> col = col_obs, pch = pch_obs, lty = 1:length(pch_obs)) #> } #> assign("cost.old", mC$model, inherits = TRUE) #> } #> return(mC) #> } #> <environment: 0x36a83b0> #> #> $cost_notrans #> function (P) #> { #> if (length(state.ini.optim) > 0) { #> odeini <- c(P[1:length(state.ini.optim)], state.ini.fixed) #> names(odeini) <- c(state.ini.optim.boxnames, state.ini.fixed.boxnames) #> } #> else { #> odeini <- state.ini.fixed #> names(odeini) <- state.ini.fixed.boxnames #> } #> odeparms <- c(P[(length(state.ini.optim) + 1):length(P)], #> parms.fixed) #> out <- mkinpredict(mkinmod, odeparms, odeini, outtimes, solution_type = solution_type, #> use_compiled = use_compiled, method.ode = method.ode, #> atol = atol, rtol = rtol, ...) #> mC <- modCost(out, observed, y = "value", err = err, weight = weight, #> scaleVar = scaleVar) #> return(mC) #> } #> <environment: 0x36a83b0> #> #> $hessian_notrans #> parent_0 k_parent k_A1 k_B1 #> parent_0 7.365081 -1854.5113 -7.186039e+02 -1.307858e+02 #> k_parent -1854.511330 2686790.7676 -6.235542e+04 -1.429363e+04 #> k_A1 -718.603865 -62355.4211 3.128242e+06 0.000000e+00 #> k_B1 -130.785796 -14293.6348 0.000000e+00 4.545506e+05 #> k_C1 -76.404274 -38575.9391 1.190516e-02 -9.422820e-04 #> k_A2 -17.933942 -4390.5079 -5.838973e+04 0.000000e+00 #> f_parent_to_A1 75.150866 43257.2599 -1.733841e+05 0.000000e+00 #> f_parent_to_B1 29.265575 17940.1132 0.000000e+00 -6.150198e+04 #> f_parent_to_C1 20.661354 19692.5582 -6.146186e-05 -1.990817e-03 #> f_A1_to_A2 1.593279 597.0744 5.849840e+03 0.000000e+00 #> k_C1 k_A2 f_parent_to_A1 f_parent_to_B1 #> parent_0 -7.640427e+01 -1.793394e+01 7.515087e+01 2.926558e+01 #> k_parent -3.857594e+04 -4.390508e+03 4.325726e+04 1.794011e+04 #> k_A1 1.190516e-02 -5.838973e+04 -1.733841e+05 0.000000e+00 #> k_B1 -9.422820e-04 0.000000e+00 0.000000e+00 -6.150198e+04 #> k_C1 8.855106e+04 4.105787e-04 -1.354551e-03 5.852620e-04 #> k_A2 4.105787e-04 4.649850e+04 -4.327086e+03 0.000000e+00 #> f_parent_to_A1 -1.354551e-03 -4.327086e+03 1.813234e+04 0.000000e+00 #> f_parent_to_B1 5.852620e-04 0.000000e+00 0.000000e+00 1.376213e+04 #> f_parent_to_C1 -1.658031e+04 2.903794e-04 1.946385e-03 1.325258e-03 #> f_A1_to_A2 -4.367402e-05 -3.679910e+03 3.844249e+02 0.000000e+00 #> f_parent_to_C1 f_A1_to_A2 #> parent_0 2.066135e+01 1.593279e+00 #> k_parent 1.969256e+04 5.970744e+02 #> k_A1 -6.146186e-05 5.849840e+03 #> k_B1 -1.990817e-03 0.000000e+00 #> k_C1 -1.658031e+04 -4.367402e-05 #> k_A2 2.903794e-04 -3.679910e+03 #> f_parent_to_A1 1.946385e-03 3.844249e+02 #> f_parent_to_B1 1.325258e-03 0.000000e+00 #> f_parent_to_C1 4.483759e+03 -3.796730e-05 #> f_A1_to_A2 -3.796730e-05 3.269288e+02 #> #> $start #> value type #> parent_0 93.2000000 state #> k_parent 0.1000000 deparm #> k_A1 0.1001000 deparm #> k_B1 0.1002000 deparm #> k_C1 0.1003000 deparm #> k_A2 0.1004000 deparm #> f_parent_to_A1 0.3333333 deparm #> f_parent_to_B1 0.3333333 deparm #> f_parent_to_C1 0.3333333 deparm #> f_A1_to_A2 0.5000000 deparm #> #> $start_transformed #> value lower upper #> parent_0 93.200000 -Inf Inf #> log_k_parent -2.302585 -Inf Inf #> log_k_A1 -2.301586 -Inf Inf #> log_k_B1 -2.300587 -Inf Inf #> log_k_C1 -2.299590 -Inf Inf #> log_k_A2 -2.298593 -Inf Inf #> f_parent_ilr_1 0.000000 -Inf Inf #> f_parent_ilr_2 0.000000 -Inf Inf #> f_A1_ilr_1 0.000000 -Inf Inf #> #> $fixed #> value type #> A1_0 0 state #> B1_0 0 state #> C1_0 0 state #> A2_0 0 state #> #> $data #> time variable observed predicted residual #> 1 0 parent 93.20 91.918159794 1.2818402 #> 2 1 parent 89.40 87.462788491 1.9372115 #> 3 3 parent 79.70 79.189448055 0.5105519 #> 4 7 parent 61.10 64.916531757 -3.8165318 #> 5 14 parent 48.20 45.846828365 2.3531716 #> 6 30 parent 15.90 20.704334210 -4.8043342 #> 7 62 parent 6.50 4.222456793 2.2775432 #> 8 100 parent 6.00 0.639147580 5.3608524 #> 9 0 A1 NA 0.000000000 NA #> 10 1 A1 NA 1.685461006 NA #> 11 3 A1 0.55 4.746752202 -4.1967522 #> 12 7 A1 6.87 9.773298725 -2.9032987 #> 13 14 A1 17.08 15.767512526 1.3124875 #> 14 30 A1 21.68 21.077890710 0.6021093 #> 15 62 A1 15.77 18.279232408 -2.5092324 #> 16 100 A1 13.63 11.743860400 1.8861396 #> 17 0 B1 NA 0.000000000 NA #> 18 1 B1 NA 0.862762059 NA #> 19 3 B1 NA 2.418226457 NA #> 20 7 B1 0.55 4.930176837 -4.3801768 #> 21 14 B1 2.31 7.810248132 -5.5002481 #> 22 30 B1 15.76 9.968281596 5.7917184 #> 23 62 B1 6.36 7.745265792 -1.3852658 #> 24 100 B1 3.74 4.271330056 -0.5313301 #> 25 0 C1 NA 0.000000000 NA #> 26 1 C1 0.55 1.829645786 -1.2796458 #> 27 3 C1 3.20 4.910531064 -1.7105311 #> 28 7 C1 5.46 9.171671206 -3.7116712 #> 29 14 C1 12.55 12.431704739 0.1182953 #> 30 30 C1 10.45 10.972842888 -0.5228429 #> 31 62 C1 4.74 3.882970158 0.8570298 #> 32 100 C1 4.33 0.782344364 3.5476556 #> 33 0 A2 NA 0.000000000 NA #> 34 1 A2 0.55 0.005272357 0.5447276 #> 35 3 A2 1.41 0.044759575 1.3652404 #> 36 7 A2 0.55 0.216973934 0.3330261 #> 37 14 A2 1.29 0.709794102 0.5802059 #> 38 30 A2 1.95 2.078585030 -0.1285850 #> 39 62 A2 3.54 3.751927986 -0.2119280 #> 40 100 A2 3.86 3.721800959 0.1381990 #> #> $atol #> [1] 1e-08 #> #> $rtol #> [1] 1e-10 #> #> $weight.ini #> [1] "none" #> #> $reweight.tol #> [1] 1e-08 #> #> $reweight.max.iter #> [1] 10 #> #> $bparms.optim #> parent_0 k_parent k_A1 k_B1 k_C1 #> 91.91815979 0.04968519 0.01393165 0.01859846 0.06171564 #> k_A2 f_parent_to_A1 f_parent_to_B1 f_parent_to_C1 f_A1_to_A2 #> 0.02431549 0.38096192 0.19546676 0.42357132 0.44796066 #> #> $bparms.fixed #> A1_0 B1_0 C1_0 A2_0 #> 0 0 0 0 #> #> $bparms.ode #> k_parent f_parent_to_A1 f_parent_to_B1 f_parent_to_C1 k_A1 #> 0.04968519 0.38096192 0.19546676 0.42357132 0.01393165 #> f_A1_to_A2 k_B1 k_C1 k_A2 #> 0.44796066 0.01859846 0.06171564 0.02431549 #> #> $bparms.state #> parent A1 B1 C1 A2 #> 91.91816 0.00000 0.00000 0.00000 0.00000 #> #> $date #> [1] "Fri Nov 18 15:20:45 2016" #> #> attr(,"class") #> [1] "mkinfit" "modFit"