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<title>Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case • mkin</title>

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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'>&lt;-</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'>&lt;-</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'>#&gt; <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'>#&gt; Model cost at call  1 :  2511.655 
#&gt; Model cost at call  2 :  2511.655 
#&gt; Model cost at call  11 :  1436.639 
#&gt; Model cost at call  12 :  1436.638 
#&gt; Model cost at call  13 :  1436.566 
#&gt; Model cost at call  21 :  643.6583 
#&gt; Model cost at call  22 :  643.6583 
#&gt; Model cost at call  23 :  643.6582 
#&gt; Model cost at call  29 :  643.6576 
#&gt; Model cost at call  31 :  454.0244 
#&gt; Model cost at call  32 :  454.0241 
#&gt; Model cost at call  34 :  454.0229 
#&gt; Model cost at call  43 :  378.1144 
#&gt; Model cost at call  45 :  378.1143 
#&gt; Model cost at call  53 :  357.245 
#&gt; Model cost at call  55 :  357.2449 
#&gt; Model cost at call  56 :  357.2447 
#&gt; Model cost at call  63 :  354.3415 
#&gt; Model cost at call  64 :  354.3415 
#&gt; Model cost at call  65 :  354.3413 
#&gt; Model cost at call  73 :  332.49 
#&gt; Model cost at call  74 :  332.49 
#&gt; Model cost at call  81 :  332.4899 
#&gt; Model cost at call  83 :  315.2962 
#&gt; Model cost at call  84 :  306.3085 
#&gt; Model cost at call  86 :  306.3084 
#&gt; Model cost at call  87 :  306.3084 
#&gt; Model cost at call  92 :  306.3083 
#&gt; Model cost at call  94 :  290.6377 
#&gt; Model cost at call  96 :  290.6375 
#&gt; Model cost at call  98 :  290.6375 
#&gt; Model cost at call  101 :  290.6371 
#&gt; Model cost at call  105 :  269.09 
#&gt; Model cost at call  107 :  269.0899 
#&gt; Model cost at call  115 :  259.7551 
#&gt; Model cost at call  120 :  259.7549 
#&gt; Model cost at call  123 :  259.7547 
#&gt; Model cost at call  126 :  253.7973 
#&gt; Model cost at call  128 :  253.7972 
#&gt; Model cost at call  137 :  251.7358 
#&gt; Model cost at call  139 :  251.7358 
#&gt; Model cost at call  147 :  250.7394 
#&gt; Model cost at call  149 :  250.7393 
#&gt; Model cost at call  157 :  249.1148 
#&gt; Model cost at call  159 :  249.1148 
#&gt; Model cost at call  167 :  246.8768 
#&gt; Model cost at call  169 :  246.8768 
#&gt; Model cost at call  177 :  244.9758 
#&gt; Model cost at call  179 :  244.9758 
#&gt; Model cost at call  187 :  243.2914 
#&gt; Model cost at call  189 :  243.2914 
#&gt; Model cost at call  190 :  243.2914 
#&gt; Model cost at call  194 :  243.2914 
#&gt; Model cost at call  199 :  242.9202 
#&gt; Model cost at call  201 :  242.9202 
#&gt; Model cost at call  202 :  242.9202 
#&gt; Model cost at call  209 :  242.7695 
#&gt; Model cost at call  211 :  242.7695 
#&gt; Model cost at call  216 :  242.7695 
#&gt; Model cost at call  219 :  242.5771 
#&gt; Model cost at call  221 :  242.5771 
#&gt; Model cost at call  229 :  242.4402 
#&gt; Model cost at call  231 :  242.4402 
#&gt; Model cost at call  239 :  242.1878 
#&gt; Model cost at call  241 :  242.1878 
#&gt; Model cost at call  249 :  242.0553 
#&gt; Model cost at call  251 :  242.0553 
#&gt; Model cost at call  256 :  242.0553 
#&gt; Model cost at call  259 :  241.8761 
#&gt; Model cost at call  260 :  241.7412 
#&gt; Model cost at call  261 :  241.6954 
#&gt; Model cost at call  264 :  241.6954 
#&gt; Model cost at call  275 :  241.5982 
#&gt; Model cost at call  277 :  241.5982 
#&gt; Model cost at call  285 :  241.5459 
#&gt; Model cost at call  287 :  241.5459 
#&gt; Model cost at call  295 :  241.4837 
#&gt; Model cost at call  297 :  241.4837 
#&gt; Model cost at call  305 :  241.3882 
#&gt; Model cost at call  306 :  241.3161 
#&gt; Model cost at call  307 :  241.2315 
#&gt; Model cost at call  309 :  241.2315 
#&gt; Model cost at call  314 :  241.2315 
#&gt; Model cost at call  317 :  240.9738 
#&gt; Model cost at call  322 :  240.9738 
#&gt; Model cost at call  327 :  240.8244 
#&gt; Model cost at call  329 :  240.8244 
#&gt; Model cost at call  337 :  240.7005 
#&gt; Model cost at call  339 :  240.7005 
#&gt; Model cost at call  342 :  240.7005 
#&gt; Model cost at call  347 :  240.629 
#&gt; Model cost at call  350 :  240.629 
#&gt; Model cost at call  357 :  240.6193 
#&gt; Model cost at call  358 :  240.6193 
#&gt; Model cost at call  364 :  240.6193 
#&gt; Model cost at call  367 :  240.6193 
#&gt; Model cost at call  369 :  240.5873 
#&gt; Model cost at call  374 :  240.5873 
#&gt; Model cost at call  380 :  240.578 
#&gt; Model cost at call  382 :  240.578 
#&gt; Model cost at call  390 :  240.5723 
#&gt; Model cost at call  393 :  240.5723 
#&gt; Model cost at call  403 :  240.569 
#&gt; Model cost at call  404 :  240.569 
#&gt; Model cost at call  413 :  240.569 
#&gt; Model cost at call  415 :  240.5688 
#&gt; Model cost at call  416 :  240.5688 
#&gt; Model cost at call  417 :  240.5688 
#&gt; Model cost at call  431 :  240.5686 
#&gt; Model cost at call  432 :  240.5686 
#&gt; Model cost at call  434 :  240.5686 
#&gt; Model cost at call  443 :  240.5686 
#&gt; Model cost at call  444 :  240.5686 
#&gt; Model cost at call  447 :  240.5686 
#&gt; Model cost at call  449 :  240.5686 
#&gt; Model cost at call  450 :  240.5686 
#&gt; Model cost at call  466 :  240.5686 
#&gt; Model cost at call  470 :  240.5686 
#&gt; Model cost at call  485 :  240.5686 
#&gt; Model cost at call  509 :  240.5686 
#&gt; Optimisation by method Port successfully terminated.</div><div class='output co'>#&gt; $par
#&gt;       parent_0   log_k_parent       log_k_A1       log_k_B1       log_k_C1 
#&gt;     91.9181598     -3.0020485     -4.2735924     -3.9846764     -2.7852180 
#&gt;       log_k_A2 f_parent_ilr_1 f_parent_ilr_2     f_A1_ilr_1 
#&gt;     -3.7166415      0.4718588     -0.3589948     -0.1477244 
#&gt; 
#&gt; $ssr
#&gt; [1] 240.5686
#&gt; 
#&gt; $convergence
#&gt; [1] 0
#&gt; 
#&gt; $iterations
#&gt; [1] 43
#&gt; 
#&gt; $evaluations
#&gt; function gradient 
#&gt;       62      441 
#&gt; 
#&gt; $counts
#&gt; [1] &quot;relative convergence (4)&quot;
#&gt; 
#&gt; $hessian
#&gt;                   parent_0 log_k_parent      log_k_A1      log_k_B1
#&gt; parent_0         7.3650812   -92.141920 -1.001134e+01 -2.432415e+00
#&gt; log_k_parent   -92.1419204  6632.673492 -4.316240e+01 -1.320833e+01
#&gt; log_k_A1       -10.0113364   -43.162398  6.071628e+02  0.000000e+00
#&gt; log_k_B1        -2.4324147   -13.208329  0.000000e+00  1.572303e+02
#&gt; log_k_C1        -4.7153201  -118.288037 -5.878291e-05 -3.073041e-06
#&gt; log_k_A2        -0.4360727    -5.304259 -1.977980e+01  0.000000e+00
#&gt; f_parent_ilr_1  10.5460899   271.145438 -5.299954e+02  1.874235e+02
#&gt; f_parent_ilr_2  11.6409409   222.570696 -4.773816e+02 -1.159875e+02
#&gt; f_A1_ilr_1       0.5572072    10.374810  2.850173e+01  0.000000e+00
#&gt;                     log_k_C1      log_k_A2 f_parent_ilr_1 f_parent_ilr_2
#&gt; parent_0       -4.715320e+00 -4.360727e-01       10.54609       11.64094
#&gt; log_k_parent   -1.182880e+02 -5.304259e+00      271.14544      222.57070
#&gt; log_k_A1       -5.878291e-05 -1.977980e+01     -529.99537     -477.38164
#&gt; log_k_B1       -3.073041e-06  0.000000e+00      187.42348     -115.98754
#&gt; log_k_C1        3.372749e+02 -2.395674e-06       56.85184      305.98862
#&gt; log_k_A2       -2.395674e-06  2.749192e+01      -23.08549      -20.79373
#&gt; f_parent_ilr_1  5.685184e+01 -2.308549e+01     1256.24941      632.09769
#&gt; f_parent_ilr_2  3.059886e+02 -2.079373e+01      632.09769     1250.65147
#&gt; f_A1_ilr_1      3.158891e-06 -3.129286e+01       29.49830       26.56991
#&gt;                   f_A1_ilr_1
#&gt; parent_0        5.572072e-01
#&gt; log_k_parent    1.037481e+01
#&gt; log_k_A1        2.850173e+01
#&gt; log_k_B1        0.000000e+00
#&gt; log_k_C1        3.158891e-06
#&gt; log_k_A2       -3.129286e+01
#&gt; f_parent_ilr_1  2.949830e+01
#&gt; f_parent_ilr_2  2.656991e+01
#&gt; f_A1_ilr_1      3.998554e+01
#&gt; 
#&gt; $residuals
#&gt;     parent     parent     parent     parent     parent     parent     parent 
#&gt; -1.2818402 -1.9372115 -0.5105519  3.8165318 -2.3531716  4.8043342 -2.2775432 
#&gt;     parent         A1         A1         A1         A1         A1         A1 
#&gt; -5.3608524  4.1967522  2.9032987 -1.3124875 -0.6021093  2.5092324 -1.8861396 
#&gt;         B1         B1         B1         B1         B1         C1         C1 
#&gt;  4.3801768  5.5002481 -5.7917184  1.3852658  0.5313301  1.2796458  1.7105311 
#&gt;         C1         C1         C1         C1         C1         A2         A2 
#&gt;  3.7116712 -0.1182953  0.5228429 -0.8570298 -3.5476556 -0.5447276 -1.3652404 
#&gt;         A2         A2         A2         A2         A2 
#&gt; -0.3330261 -0.5802059  0.1285850  0.2119280 -0.1381990 
#&gt; 
#&gt; $ms
#&gt; [1] 7.289956
#&gt; 
#&gt; $var_ms
#&gt;     parent         A1         B1         C1         A2 
#&gt; 10.3459333  6.3301336 17.0367907  4.5639474  0.3841002 
#&gt; 
#&gt; $var_ms_unscaled
#&gt;     parent         A1         B1         C1         A2 
#&gt; 10.3459333  6.3301336 17.0367907  4.5639474  0.3841002 
#&gt; 
#&gt; $var_ms_unweighted
#&gt;     parent         A1         B1         C1         A2 
#&gt; 10.3459333  6.3301336 17.0367907  4.5639474  0.3841002 
#&gt; 
#&gt; $rank
#&gt; [1] 9
#&gt; 
#&gt; $df.residual
#&gt; [1] 24
#&gt; 
#&gt; $solution_type
#&gt; [1] &quot;deSolve&quot;
#&gt; 
#&gt; $transform_rates
#&gt; [1] TRUE
#&gt; 
#&gt; $transform_fractions
#&gt; [1] TRUE
#&gt; 
#&gt; $method.modFit
#&gt; [1] &quot;Port&quot;
#&gt; 
#&gt; $maxit.modFit
#&gt; [1] &quot;auto&quot;
#&gt; 
#&gt; $calls
#&gt; [1] 523
#&gt; 
#&gt; $time
#&gt;    user  system elapsed 
#&gt;   5.004   0.000   5.004 
#&gt; 
#&gt; $mkinmod
#&gt; &lt;mkinmod&gt; model generated with
#&gt; Use of formation fractions $use_of_ff: max 
#&gt; Specification $spec:
#&gt; $parent
#&gt; $type: SFO; $to: A1, B1, C1; $sink: FALSE
#&gt; $A1
#&gt; $type: SFO; $to: A2; $sink: TRUE
#&gt; $B1
#&gt; $type: SFO; $sink: TRUE
#&gt; $C1
#&gt; $type: SFO; $sink: TRUE
#&gt; $A2
#&gt; $type: SFO; $sink: TRUE
#&gt; Coefficient matrix $coefmat available
#&gt; Compiled model $cf available
#&gt; 
#&gt; $observed
#&gt;      name time value
#&gt; 1  parent    0 93.20
#&gt; 2  parent    1 89.40
#&gt; 3  parent    3 79.70
#&gt; 4  parent    7 61.10
#&gt; 5  parent   14 48.20
#&gt; 6  parent   30 15.90
#&gt; 7  parent   62  6.50
#&gt; 8  parent  100  6.00
#&gt; 9      A1    0    NA
#&gt; 10     A1    1    NA
#&gt; 11     A1    3  0.55
#&gt; 12     A1    7  6.87
#&gt; 13     A1   14 17.08
#&gt; 14     A1   30 21.68
#&gt; 15     A1   62 15.77
#&gt; 16     A1  100 13.63
#&gt; 17     B1    0    NA
#&gt; 18     B1    1    NA
#&gt; 19     B1    3    NA
#&gt; 20     B1    7  0.55
#&gt; 21     B1   14  2.31
#&gt; 22     B1   30 15.76
#&gt; 23     B1   62  6.36
#&gt; 24     B1  100  3.74
#&gt; 25     C1    0    NA
#&gt; 26     C1    1  0.55
#&gt; 27     C1    3  3.20
#&gt; 28     C1    7  5.46
#&gt; 29     C1   14 12.55
#&gt; 30     C1   30 10.45
#&gt; 31     C1   62  4.74
#&gt; 32     C1  100  4.33
#&gt; 33     A2    0    NA
#&gt; 34     A2    1  0.55
#&gt; 35     A2    3  1.41
#&gt; 36     A2    7  0.55
#&gt; 37     A2   14  1.29
#&gt; 38     A2   30  1.95
#&gt; 39     A2   62  3.54
#&gt; 40     A2  100  3.86
#&gt; 
#&gt; $obs_vars
#&gt; [1] &quot;parent&quot; &quot;A1&quot;     &quot;B1&quot;     &quot;C1&quot;     &quot;A2&quot;    
#&gt; 
#&gt; $predicted
#&gt;       name       time        value
#&gt; 1   parent   0.000000 91.918159794
#&gt; 2   parent   1.000000 87.462788491
#&gt; 3   parent   1.010101 87.418904506
#&gt; 4   parent   2.020202 83.139880984
#&gt; 5   parent   3.000000 79.189448055
#&gt; 6   parent   3.030303 79.070309209
#&gt; 7   parent   4.040404 75.199936833
#&gt; 8   parent   5.050505 71.519013349
#&gt; 9   parent   6.060606 68.018265517
#&gt; 10  parent   7.000000 64.916531757
#&gt; 11  parent   7.070707 64.688874011
#&gt; 12  parent   8.080808 61.522451197
#&gt; 13  parent   9.090909 58.511020005
#&gt; 14  parent  10.101010 55.646993828
#&gt; 15  parent  11.111111 52.923157412
#&gt; 16  parent  12.121212 50.332648680
#&gt; 17  parent  13.131313 47.868941444
#&gt; 18  parent  14.000000 45.846828365
#&gt; 19  parent  14.141414 45.525828960
#&gt; 20  parent  15.151515 43.297408299
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#&gt; 395     C1  70.707071  2.745378939
#&gt; 396     C1  71.717172  2.634511667
#&gt; 397     C1  72.727273  2.527650041
#&gt; 398     C1  73.737374  2.424683880
#&gt; 399     C1  74.747475  2.325503203
#&gt; 400     C1  75.757576  2.229998536
#&gt; 401     C1  76.767677  2.138061182
#&gt; 402     C1  77.777778  2.049583458
#&gt; 403     C1  78.787879  1.964458908
#&gt; 404     C1  79.797980  1.882582485
#&gt; 405     C1  80.808081  1.803850715
#&gt; 406     C1  81.818182  1.728161832
#&gt; 407     C1  82.828283  1.655415900
#&gt; 408     C1  83.838384  1.585514911
#&gt; 409     C1  84.848485  1.518362874
#&gt; 410     C1  85.858586  1.453865880
#&gt; 411     C1  86.868687  1.391932162
#&gt; 412     C1  87.878788  1.332472134
#&gt; 413     C1  88.888889  1.275398429
#&gt; 414     C1  89.898990  1.220625918
#&gt; 415     C1  90.909091  1.168071723
#&gt; 416     C1  91.919192  1.117655227
#&gt; 417     C1  92.929293  1.069298066
#&gt; 418     C1  93.939394  1.022924125
#&gt; 419     C1  94.949495  0.978459525
#&gt; 420     C1  95.959596  0.935832597
#&gt; 421     C1  96.969697  0.894973866
#&gt; 422     C1  97.979798  0.855816021
#&gt; 423     C1  98.989899  0.818293881
#&gt; 424     C1 100.000000  0.782344364
#&gt; 425     A2   0.000000  0.000000000
#&gt; 426     A2   1.000000  0.005272357
#&gt; 427     A2   1.010101  0.005377817
#&gt; 428     A2   2.020202  0.020885524
#&gt; 429     A2   3.000000  0.044759575
#&gt; 430     A2   3.030303  0.045628064
#&gt; 431     A2   4.040404  0.078765936
#&gt; 432     A2   5.050505  0.119512155
#&gt; 433     A2   6.060606  0.167129381
#&gt; 434     A2   7.000000  0.216973934
#&gt; 435     A2   7.070707  0.220927189
#&gt; 436     A2   8.080808  0.280259484
#&gt; 437     A2   9.090909  0.344522046
#&gt; 438     A2  10.101010  0.413150206
#&gt; 439     A2  11.111111  0.485616641
#&gt; 440     A2  12.121212  0.561429288
#&gt; 441     A2  13.131313  0.640129357
#&gt; 442     A2  14.000000  0.709794102
#&gt; 443     A2  14.141414  0.721289460
#&gt; 444     A2  15.151515  0.804511827
#&gt; 445     A2  16.161616  0.889426625
#&gt; 446     A2  17.171717  0.975690359
#&gt; 447     A2  18.181818  1.062984358
#&gt; 448     A2  19.191919  1.151013342
#&gt; 449     A2  20.202020  1.239504068
#&gt; 450     A2  21.212121  1.328204041
#&gt; 451     A2  22.222222  1.416880297
#&gt; 452     A2  23.232323  1.505318253
#&gt; 453     A2  24.242424  1.593320615
#&gt; 454     A2  25.252525  1.680706344
#&gt; 455     A2  26.262626  1.767309680
#&gt; 456     A2  27.272727  1.852979219
#&gt; 457     A2  28.282828  1.937577034
#&gt; 458     A2  29.292929  2.020977853
#&gt; 459     A2  30.000000  2.078585030
#&gt; 460     A2  30.303030  2.103068270
#&gt; 461     A2  31.313131  2.183746011
#&gt; 462     A2  32.323232  2.262919231
#&gt; 463     A2  33.333333  2.340505852
#&gt; 464     A2  34.343434  2.416432940
#&gt; 465     A2  35.353535  2.490636111
#&gt; 466     A2  36.363636  2.563058979
#&gt; 467     A2  37.373737  2.633652622
#&gt; 468     A2  38.383838  2.702375089
#&gt; 469     A2  39.393939  2.769190926
#&gt; 470     A2  40.404040  2.834070737
#&gt; 471     A2  41.414141  2.896990764
#&gt; 472     A2  42.424242  2.957932489
#&gt; 473     A2  43.434343  3.016882265
#&gt; 474     A2  44.444444  3.073830964
#&gt; 475     A2  45.454545  3.128773647
#&gt; 476     A2  46.464646  3.181709250
#&gt; 477     A2  47.474747  3.232640290
#&gt; 478     A2  48.484848  3.281572591
#&gt; 479     A2  49.494949  3.328515022
#&gt; 480     A2  50.505051  3.373479253
#&gt; 481     A2  51.515152  3.416479521
#&gt; 482     A2  52.525253  3.457532417
#&gt; 483     A2  53.535354  3.496656681
#&gt; 484     A2  54.545455  3.533873012
#&gt; 485     A2  55.555556  3.569203883
#&gt; 486     A2  56.565657  3.602673379
#&gt; 487     A2  57.575758  3.634307034
#&gt; 488     A2  58.585859  3.664131686
#&gt; 489     A2  59.595960  3.692175334
#&gt; 490     A2  60.606061  3.718467012
#&gt; 491     A2  61.616162  3.743036663
#&gt; 492     A2  62.000000  3.751927986
#&gt; 493     A2  62.626263  3.765915028
#&gt; 494     A2  63.636364  3.787133539
#&gt; 495     A2  64.646465  3.806724217
#&gt; 496     A2  65.656566  3.824719582
#&gt; 497     A2  66.666667  3.841152565
#&gt; 498     A2  67.676768  3.856056426
#&gt; 499     A2  68.686869  3.869464684
#&gt; 500     A2  69.696970  3.881411040
#&gt; 501     A2  70.707071  3.891929316
#&gt; 502     A2  71.717172  3.901053396
#&gt; 503     A2  72.727273  3.908817168
#&gt; 504     A2  73.737374  3.915254472
#&gt; 505     A2  74.747475  3.920399054
#&gt; 506     A2  75.757576  3.924284521
#&gt; 507     A2  76.767677  3.926944303
#&gt; 508     A2  77.777778  3.928411610
#&gt; 509     A2  78.787879  3.928719404
#&gt; 510     A2  79.797980  3.927900364
#&gt; 511     A2  80.808081  3.925986861
#&gt; 512     A2  81.818182  3.923010926
#&gt; 513     A2  82.828283  3.919004234
#&gt; 514     A2  83.838384  3.913998077
#&gt; 515     A2  84.848485  3.908023347
#&gt; 516     A2  85.858586  3.901110518
#&gt; 517     A2  86.868687  3.893289633
#&gt; 518     A2  87.878788  3.884590288
#&gt; 519     A2  88.888889  3.875041619
#&gt; 520     A2  89.898990  3.864672297
#&gt; 521     A2  90.909091  3.853510511
#&gt; 522     A2  91.919192  3.841583970
#&gt; 523     A2  92.929293  3.828919886
#&gt; 524     A2  93.939394  3.815544978
#&gt; 525     A2  94.949495  3.801485462
#&gt; 526     A2  95.959596  3.786767051
#&gt; 527     A2  96.969697  3.771414951
#&gt; 528     A2  97.979798  3.755453860
#&gt; 529     A2  98.989899  3.738907968
#&gt; 530     A2 100.000000  3.721800959
#&gt; 
#&gt; $cost
#&gt; function (P) 
#&gt; {
#&gt;     assign(&quot;calls&quot;, calls + 1, inherits = TRUE)
#&gt;     if (trace_parms) 
#&gt;         cat(P, &quot;\n&quot;)
#&gt;     if (length(state.ini.optim) &gt; 0) {
#&gt;         odeini &lt;- c(P[1:length(state.ini.optim)], state.ini.fixed)
#&gt;         names(odeini) &lt;- c(state.ini.optim.boxnames, state.ini.fixed.boxnames)
#&gt;     }
#&gt;     else {
#&gt;         odeini &lt;- state.ini.fixed
#&gt;         names(odeini) &lt;- state.ini.fixed.boxnames
#&gt;     }
#&gt;     odeparms &lt;- c(P[(length(state.ini.optim) + 1):length(P)], 
#&gt;         transparms.fixed)
#&gt;     parms &lt;- backtransform_odeparms(odeparms, mkinmod, transform_rates = transform_rates, 
#&gt;         transform_fractions = transform_fractions)
#&gt;     out &lt;- mkinpredict(mkinmod, parms, odeini, outtimes, solution_type = solution_type, 
#&gt;         use_compiled = use_compiled, method.ode = method.ode, 
#&gt;         atol = atol, rtol = rtol, ...)
#&gt;     assign(&quot;out_predicted&quot;, out, inherits = TRUE)
#&gt;     mC &lt;- modCost(out, observed, y = &quot;value&quot;, err = err, weight = weight, 
#&gt;         scaleVar = scaleVar)
#&gt;     if (mC$model &lt; cost.old) {
#&gt;         if (!quiet) 
#&gt;             cat(&quot;Model cost at call &quot;, calls, &quot;: &quot;, mC$model, 
#&gt;                 &quot;\n&quot;)
#&gt;         if (plot) {
#&gt;             outtimes_plot = seq(min(observed$time), max(observed$time), 
#&gt;                 length.out = 100)
#&gt;             out_plot &lt;- mkinpredict(mkinmod, parms, odeini, outtimes_plot, 
#&gt;                 solution_type = solution_type, use_compiled = use_compiled, 
#&gt;                 method.ode = method.ode, atol = atol, rtol = rtol, 
#&gt;                 ...)
#&gt;             plot(0, type = &quot;n&quot;, xlim = range(observed$time), 
#&gt;                 ylim = c(0, max(observed$value, na.rm = TRUE)), 
#&gt;                 xlab = &quot;Time&quot;, ylab = &quot;Observed&quot;)
#&gt;             col_obs &lt;- pch_obs &lt;- 1:length(obs_vars)
#&gt;             lty_obs &lt;- rep(1, length(obs_vars))
#&gt;             names(col_obs) &lt;- names(pch_obs) &lt;- names(lty_obs) &lt;- obs_vars
#&gt;             for (obs_var in obs_vars) {
#&gt;                 points(subset(observed, name == obs_var, c(time, 
#&gt;                   value)), pch = pch_obs[obs_var], col = col_obs[obs_var])
#&gt;             }
#&gt;             matlines(out_plot$time, out_plot[-1], col = col_obs, 
#&gt;                 lty = lty_obs)
#&gt;             legend(&quot;topright&quot;, inset = c(0.05, 0.05), legend = obs_vars, 
#&gt;                 col = col_obs, pch = pch_obs, lty = 1:length(pch_obs))
#&gt;         }
#&gt;         assign(&quot;cost.old&quot;, mC$model, inherits = TRUE)
#&gt;     }
#&gt;     return(mC)
#&gt; }
#&gt; &lt;environment: 0x36a83b0&gt;
#&gt; 
#&gt; $cost_notrans
#&gt; function (P) 
#&gt; {
#&gt;     if (length(state.ini.optim) &gt; 0) {
#&gt;         odeini &lt;- c(P[1:length(state.ini.optim)], state.ini.fixed)
#&gt;         names(odeini) &lt;- c(state.ini.optim.boxnames, state.ini.fixed.boxnames)
#&gt;     }
#&gt;     else {
#&gt;         odeini &lt;- state.ini.fixed
#&gt;         names(odeini) &lt;- state.ini.fixed.boxnames
#&gt;     }
#&gt;     odeparms &lt;- c(P[(length(state.ini.optim) + 1):length(P)], 
#&gt;         parms.fixed)
#&gt;     out &lt;- mkinpredict(mkinmod, odeparms, odeini, outtimes, solution_type = solution_type, 
#&gt;         use_compiled = use_compiled, method.ode = method.ode, 
#&gt;         atol = atol, rtol = rtol, ...)
#&gt;     mC &lt;- modCost(out, observed, y = &quot;value&quot;, err = err, weight = weight, 
#&gt;         scaleVar = scaleVar)
#&gt;     return(mC)
#&gt; }
#&gt; &lt;environment: 0x36a83b0&gt;
#&gt; 
#&gt; $hessian_notrans
#&gt;                    parent_0     k_parent          k_A1          k_B1
#&gt; parent_0           7.365081   -1854.5113 -7.186039e+02 -1.307858e+02
#&gt; k_parent       -1854.511330 2686790.7676 -6.235542e+04 -1.429363e+04
#&gt; k_A1            -718.603865  -62355.4211  3.128242e+06  0.000000e+00
#&gt; k_B1            -130.785796  -14293.6348  0.000000e+00  4.545506e+05
#&gt; k_C1             -76.404274  -38575.9391  1.190516e-02 -9.422820e-04
#&gt; k_A2             -17.933942   -4390.5079 -5.838973e+04  0.000000e+00
#&gt; f_parent_to_A1    75.150866   43257.2599 -1.733841e+05  0.000000e+00
#&gt; f_parent_to_B1    29.265575   17940.1132  0.000000e+00 -6.150198e+04
#&gt; f_parent_to_C1    20.661354   19692.5582 -6.146186e-05 -1.990817e-03
#&gt; f_A1_to_A2         1.593279     597.0744  5.849840e+03  0.000000e+00
#&gt;                         k_C1          k_A2 f_parent_to_A1 f_parent_to_B1
#&gt; parent_0       -7.640427e+01 -1.793394e+01   7.515087e+01   2.926558e+01
#&gt; k_parent       -3.857594e+04 -4.390508e+03   4.325726e+04   1.794011e+04
#&gt; k_A1            1.190516e-02 -5.838973e+04  -1.733841e+05   0.000000e+00
#&gt; k_B1           -9.422820e-04  0.000000e+00   0.000000e+00  -6.150198e+04
#&gt; k_C1            8.855106e+04  4.105787e-04  -1.354551e-03   5.852620e-04
#&gt; k_A2            4.105787e-04  4.649850e+04  -4.327086e+03   0.000000e+00
#&gt; f_parent_to_A1 -1.354551e-03 -4.327086e+03   1.813234e+04   0.000000e+00
#&gt; f_parent_to_B1  5.852620e-04  0.000000e+00   0.000000e+00   1.376213e+04
#&gt; f_parent_to_C1 -1.658031e+04  2.903794e-04   1.946385e-03   1.325258e-03
#&gt; f_A1_to_A2     -4.367402e-05 -3.679910e+03   3.844249e+02   0.000000e+00
#&gt;                f_parent_to_C1    f_A1_to_A2
#&gt; parent_0         2.066135e+01  1.593279e+00
#&gt; k_parent         1.969256e+04  5.970744e+02
#&gt; k_A1            -6.146186e-05  5.849840e+03
#&gt; k_B1            -1.990817e-03  0.000000e+00
#&gt; k_C1            -1.658031e+04 -4.367402e-05
#&gt; k_A2             2.903794e-04 -3.679910e+03
#&gt; f_parent_to_A1   1.946385e-03  3.844249e+02
#&gt; f_parent_to_B1   1.325258e-03  0.000000e+00
#&gt; f_parent_to_C1   4.483759e+03 -3.796730e-05
#&gt; f_A1_to_A2      -3.796730e-05  3.269288e+02
#&gt; 
#&gt; $start
#&gt;                     value   type
#&gt; parent_0       93.2000000  state
#&gt; k_parent        0.1000000 deparm
#&gt; k_A1            0.1001000 deparm
#&gt; k_B1            0.1002000 deparm
#&gt; k_C1            0.1003000 deparm
#&gt; k_A2            0.1004000 deparm
#&gt; f_parent_to_A1  0.3333333 deparm
#&gt; f_parent_to_B1  0.3333333 deparm
#&gt; f_parent_to_C1  0.3333333 deparm
#&gt; f_A1_to_A2      0.5000000 deparm
#&gt; 
#&gt; $start_transformed
#&gt;                    value lower upper
#&gt; parent_0       93.200000  -Inf   Inf
#&gt; log_k_parent   -2.302585  -Inf   Inf
#&gt; log_k_A1       -2.301586  -Inf   Inf
#&gt; log_k_B1       -2.300587  -Inf   Inf
#&gt; log_k_C1       -2.299590  -Inf   Inf
#&gt; log_k_A2       -2.298593  -Inf   Inf
#&gt; f_parent_ilr_1  0.000000  -Inf   Inf
#&gt; f_parent_ilr_2  0.000000  -Inf   Inf
#&gt; f_A1_ilr_1      0.000000  -Inf   Inf
#&gt; 
#&gt; $fixed
#&gt;      value  type
#&gt; A1_0     0 state
#&gt; B1_0     0 state
#&gt; C1_0     0 state
#&gt; A2_0     0 state
#&gt; 
#&gt; $data
#&gt;    time variable observed    predicted   residual
#&gt; 1     0   parent    93.20 91.918159794  1.2818402
#&gt; 2     1   parent    89.40 87.462788491  1.9372115
#&gt; 3     3   parent    79.70 79.189448055  0.5105519
#&gt; 4     7   parent    61.10 64.916531757 -3.8165318
#&gt; 5    14   parent    48.20 45.846828365  2.3531716
#&gt; 6    30   parent    15.90 20.704334210 -4.8043342
#&gt; 7    62   parent     6.50  4.222456793  2.2775432
#&gt; 8   100   parent     6.00  0.639147580  5.3608524
#&gt; 9     0       A1       NA  0.000000000         NA
#&gt; 10    1       A1       NA  1.685461006         NA
#&gt; 11    3       A1     0.55  4.746752202 -4.1967522
#&gt; 12    7       A1     6.87  9.773298725 -2.9032987
#&gt; 13   14       A1    17.08 15.767512526  1.3124875
#&gt; 14   30       A1    21.68 21.077890710  0.6021093
#&gt; 15   62       A1    15.77 18.279232408 -2.5092324
#&gt; 16  100       A1    13.63 11.743860400  1.8861396
#&gt; 17    0       B1       NA  0.000000000         NA
#&gt; 18    1       B1       NA  0.862762059         NA
#&gt; 19    3       B1       NA  2.418226457         NA
#&gt; 20    7       B1     0.55  4.930176837 -4.3801768
#&gt; 21   14       B1     2.31  7.810248132 -5.5002481
#&gt; 22   30       B1    15.76  9.968281596  5.7917184
#&gt; 23   62       B1     6.36  7.745265792 -1.3852658
#&gt; 24  100       B1     3.74  4.271330056 -0.5313301
#&gt; 25    0       C1       NA  0.000000000         NA
#&gt; 26    1       C1     0.55  1.829645786 -1.2796458
#&gt; 27    3       C1     3.20  4.910531064 -1.7105311
#&gt; 28    7       C1     5.46  9.171671206 -3.7116712
#&gt; 29   14       C1    12.55 12.431704739  0.1182953
#&gt; 30   30       C1    10.45 10.972842888 -0.5228429
#&gt; 31   62       C1     4.74  3.882970158  0.8570298
#&gt; 32  100       C1     4.33  0.782344364  3.5476556
#&gt; 33    0       A2       NA  0.000000000         NA
#&gt; 34    1       A2     0.55  0.005272357  0.5447276
#&gt; 35    3       A2     1.41  0.044759575  1.3652404
#&gt; 36    7       A2     0.55  0.216973934  0.3330261
#&gt; 37   14       A2     1.29  0.709794102  0.5802059
#&gt; 38   30       A2     1.95  2.078585030 -0.1285850
#&gt; 39   62       A2     3.54  3.751927986 -0.2119280
#&gt; 40  100       A2     3.86  3.721800959  0.1381990
#&gt; 
#&gt; $atol
#&gt; [1] 1e-08
#&gt; 
#&gt; $rtol
#&gt; [1] 1e-10
#&gt; 
#&gt; $weight.ini
#&gt; [1] &quot;none&quot;
#&gt; 
#&gt; $reweight.tol
#&gt; [1] 1e-08
#&gt; 
#&gt; $reweight.max.iter
#&gt; [1] 10
#&gt; 
#&gt; $bparms.optim
#&gt;       parent_0       k_parent           k_A1           k_B1           k_C1 
#&gt;    91.91815979     0.04968519     0.01393165     0.01859846     0.06171564 
#&gt;           k_A2 f_parent_to_A1 f_parent_to_B1 f_parent_to_C1     f_A1_to_A2 
#&gt;     0.02431549     0.38096192     0.19546676     0.42357132     0.44796066 
#&gt; 
#&gt; $bparms.fixed
#&gt; A1_0 B1_0 C1_0 A2_0 
#&gt;    0    0    0    0 
#&gt; 
#&gt; $bparms.ode
#&gt;       k_parent f_parent_to_A1 f_parent_to_B1 f_parent_to_C1           k_A1 
#&gt;     0.04968519     0.38096192     0.19546676     0.42357132     0.01393165 
#&gt;     f_A1_to_A2           k_B1           k_C1           k_A2 
#&gt;     0.44796066     0.01859846     0.06171564     0.02431549 
#&gt; 
#&gt; $bparms.state
#&gt;   parent       A1       B1       C1       A2 
#&gt; 91.91816  0.00000  0.00000  0.00000  0.00000 
#&gt; 
#&gt; $date
#&gt; [1] &quot;Fri Nov 18 15:20:45 2016&quot;
#&gt; 
#&gt; attr(,&quot;class&quot;)
#&gt; [1] &quot;mkinfit&quot; &quot;modFit&quot; </div></pre>
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