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<h1>Metabolism data set used for checking the software quality of KinGUI</h1>
</div>
<p>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.</p>
<pre><span class='fu'>data</span>(<span class='no'>schaefer07_complex_case</span>)</pre>
<h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>
<p>The data set is a data frame with 8 observations on the following 6 variables.
<dl class='dl-horizontal'>
<dt><code>time</code></dt><dd>a numeric vector</dd>
<dt><code>parent</code></dt><dd>a numeric vector</dd>
<dt><code>A1</code></dt><dd>a numeric vector</dd>
<dt><code>B1</code></dt><dd>a numeric vector</dd>
<dt><code>C1</code></dt><dd>a numeric vector</dd>
<dt><code>A2</code></dt><dd>a numeric vector</dd>
</dl></p>
<p>The results are a data frame with 14 results for different parameter values</p>
<h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2>
<p>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.</p>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'><span class='no'>data</span> <span class='kw'><-</span> <span class='fu'><a href='mkin_wide_to_long.html'>mkin_wide_to_long</a></span>(<span class='no'>schaefer07_complex_case</span>, <span class='kw'>time</span> <span class='kw'>=</span> <span class='st'>"time"</span>)
<span class='no'>model</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(
<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"A1"</span>, <span class='st'>"B1"</span>, <span class='st'>"C1"</span>), <span class='kw'>sink</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>),
<span class='kw'>A1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"A2"</span>),
<span class='kw'>B1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>),
<span class='kw'>C1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>),
<span class='kw'>A2</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>model</span>, <span class='no'>data</span>)</div><div class='output co'>#> 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.</div><div class='output co'>#> $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
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#> 330 C1 8.080808 9.970174354
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#> 332 C1 10.101010 11.138827767
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#> 334 C1 12.121212 11.947273869
#> 335 C1 13.131313 12.236662337
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#> 338 C1 15.151515 12.620919488
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#> 345 C1 22.222222 12.504629065
#> 346 C1 23.232323 12.361220091
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#> 348 C1 25.252525 12.013241882
#> 349 C1 26.262626 11.813943686
#> 350 C1 27.272727 11.600907551
#> 351 C1 28.282828 11.376225763
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#> 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
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#> 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" </div></pre>
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<h2>Contents</h2>
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