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    <h1>Three experimental datasets from two water sediment systems and one soil</h1>
    </div>

    
    <p>The datasets were used for the comparative validation of several kinetic evaluation
 software packages (Ranke, 2014).</p>
    

    <pre class="usage"><span class='no'>test_data_from_UBA_2014</span></pre>
        
    <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>

    <p>A list containing three datasets as an R6 class defined by <code><a href='mkinds.html'>mkinds</a></code>.
  Each dataset has, among others, the following components</p><dl class='dl-horizontal'>
    <dt><code>title</code></dt><dd><p>The name of the dataset, e.g. <code>UBA_2014_WS_river</code></p></dd>
    <dt><code>data</code></dt><dd><p>A data frame with the data in the form expected by <code><a href='mkinfit.html'>mkinfit</a></code></p></dd>
  </dl>
    
    <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>

    <p>Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative
  zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452</p>
    

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'>  <span class='co'># This is a level P-II evaluation of the dataset according to the FOCUS kinetics</span>
  <span class='co'># guidance. Due to the strong correlation of the parameter estimates, the</span>
  <span class='co'># covariance matrix is not returned. Note that level P-II evaluations are</span>
  <span class='co'># generally considered deprecated due to the frequent occurrence of such</span>
  <span class='co'># large parameter correlations, among other reasons (e.g. the adequacy of the</span>
  <span class='co'># model).</span>
  <span class='no'>m_ws</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent_w</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"parent_s"</span>),
                  <span class='kw'>parent_s</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"parent_w"</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='no'>f_river</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>m_ws</span>, <span class='no'>test_data_from_UBA_2014</span><span class='kw'>[[</span><span class='fl'>1</span>]]$<span class='no'>data</span>)</div><div class='output co'>#&gt; Model cost at call  1 :  2371.755 
#&gt; Model cost at call  3 :  2371.755 
#&gt; Model cost at call  7 :  457.5705 
#&gt; Model cost at call  10 :  457.5608 
#&gt; Model cost at call  14 :  297.2882 
#&gt; Model cost at call  16 :  297.2882 
#&gt; Model cost at call  18 :  297.2881 
#&gt; Model cost at call  21 :  275.6034 
#&gt; Model cost at call  23 :  275.6034 
#&gt; Model cost at call  27 :  268.2102 
#&gt; Model cost at call  29 :  268.2101 
#&gt; Model cost at call  33 :  255.941 
#&gt; Model cost at call  35 :  255.941 
#&gt; Model cost at call  39 :  246.486 
#&gt; Model cost at call  41 :  246.486 
#&gt; Model cost at call  45 :  231.4275 
#&gt; Model cost at call  47 :  231.4275 
#&gt; Model cost at call  51 :  212.4338 
#&gt; Model cost at call  52 :  212.4338 
#&gt; Model cost at call  53 :  212.4337 
#&gt; Model cost at call  57 :  203.7185 
#&gt; Model cost at call  58 :  203.7185 
#&gt; Model cost at call  63 :  198.3304 
#&gt; Model cost at call  69 :  198.3304 
#&gt; Model cost at call  70 :  195.794 
#&gt; Model cost at call  72 :  195.7939 
#&gt; Model cost at call  76 :  195.2477 
#&gt; Model cost at call  77 :  195.2477 
#&gt; Model cost at call  82 :  191.6184 
#&gt; Model cost at call  84 :  191.6184 
#&gt; Model cost at call  88 :  189.9011 
#&gt; Model cost at call  90 :  189.9011 
#&gt; Model cost at call  91 :  189.9011 
#&gt; Model cost at call  94 :  189.223 
#&gt; Model cost at call  95 :  189.223 
#&gt; Model cost at call  100 :  188.8728 
#&gt; Model cost at call  101 :  188.8728 
#&gt; Model cost at call  103 :  188.8728 
#&gt; Model cost at call  106 :  188.2057 
#&gt; Model cost at call  107 :  188.2057 
#&gt; Model cost at call  112 :  187.8429 
#&gt; Model cost at call  118 :  187.6219 
#&gt; Model cost at call  119 :  187.3931 
#&gt; Model cost at call  121 :  187.3931 
#&gt; Model cost at call  125 :  187.1236 
#&gt; Model cost at call  126 :  187.1236 
#&gt; Model cost at call  131 :  186.9995 
#&gt; Model cost at call  137 :  186.927 
#&gt; Model cost at call  139 :  186.927 
#&gt; Model cost at call  143 :  186.8909 
#&gt; Model cost at call  146 :  186.8909 
#&gt; Model cost at call  150 :  186.8708 
#&gt; Model cost at call  152 :  186.8708 
#&gt; Model cost at call  157 :  186.8606 
#&gt; Model cost at call  159 :  186.8606 
#&gt; Model cost at call  161 :  186.8606 
#&gt; Model cost at call  162 :  186.8606 
#&gt; Model cost at call  167 :  186.8551 
#&gt; Model cost at call  169 :  186.8551 
#&gt; Model cost at call  176 :  186.8519 
#&gt; Model cost at call  177 :  186.8519 
#&gt; Model cost at call  180 :  186.8519 
#&gt; Model cost at call  181 :  186.8519 
#&gt; Model cost at call  186 :  186.8504 
#&gt; Model cost at call  187 :  186.8504 
#&gt; Model cost at call  196 :  186.8496 
#&gt; Model cost at call  197 :  186.8496 
#&gt; Model cost at call  200 :  186.8496 
#&gt; Model cost at call  206 :  186.8493 
#&gt; Model cost at call  211 :  186.8493 
#&gt; Model cost at call  215 :  186.8493 
#&gt; Model cost at call  216 :  186.8491 
#&gt; Model cost at call  225 :  186.8491 
#&gt; Model cost at call  226 :  186.849 
#&gt; Model cost at call  236 :  186.8489 
#&gt; Model cost at call  246 :  186.8489 
#&gt; Model cost at call  254 :  186.8489 
#&gt; Model cost at call  257 :  186.8489 
#&gt; Model cost at call  265 :  186.8489 
#&gt; Model cost at call  268 :  186.8489 
#&gt; Model cost at call  276 :  186.8489 
#&gt; Model cost at call  279 :  186.8489 
#&gt; Model cost at call  287 :  186.8489 
#&gt; Model cost at call  290 :  186.8489 
#&gt; Model cost at call  298 :  186.8489 
#&gt; Model cost at call  301 :  186.8489 
#&gt; Model cost at call  309 :  186.8489 
#&gt; Model cost at call  312 :  186.8489 
#&gt; Model cost at call  320 :  186.8489 
#&gt; Model cost at call  323 :  186.8489 
#&gt; Model cost at call  331 :  186.8489 
#&gt; Model cost at call  334 :  186.8489 
#&gt; Model cost at call  342 :  186.8489 
#&gt; Model cost at call  345 :  186.8489 
#&gt; Model cost at call  353 :  186.8489 
#&gt; Model cost at call  356 :  186.8489 
#&gt; Model cost at call  367 :  186.8489 
#&gt; Model cost at call  374 :  186.8489 
#&gt; Model cost at call  375 :  186.8489 
#&gt; Model cost at call  380 :  186.8489 
#&gt; Model cost at call  382 :  186.8489 
#&gt; Model cost at call  383 :  186.8489 
#&gt; Optimisation by method Port successfully terminated.</div><div class='input'>  <span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span>(<span class='no'>f_river</span>)</div><img src='test_data_from_UBA_2014-6.png' alt='' width='540' height='400' /><div class='input'>
  <span class='fu'>summary</span>(<span class='no'>f_river</span>)$<span class='no'>bpar</span></div><div class='output co'>#&gt;                         Estimate se_notrans      t value       Pr(&gt;t) Lower
#&gt; parent_w_0          9.598567e+01 2.33959810 4.102657e+01 9.568973e-19    NA
#&gt; k_parent_w_sink     3.603743e-01 0.03497716 1.030313e+01 4.988281e-09    NA
#&gt; k_parent_w_parent_s 6.031370e-02 0.01746026 3.454342e+00 1.514738e-03    NA
#&gt; k_parent_s_sink     5.099834e-11 0.10381939 4.912217e-10 5.000000e-01    NA
#&gt; k_parent_s_parent_w 7.419672e-02 0.11338174 6.543974e-01 2.608057e-01    NA
#&gt;                     Upper
#&gt; parent_w_0             NA
#&gt; k_parent_w_sink        NA
#&gt; k_parent_w_parent_s    NA
#&gt; k_parent_s_sink        NA
#&gt; k_parent_s_parent_w    NA</div><div class='input'>  <span class='fu'><a href='mkinerrmin.html'>mkinerrmin</a></span>(<span class='no'>f_river</span>)</div><div class='output co'>#&gt;             err.min n.optim df
#&gt; All data 0.09246946       5  6
#&gt; parent_w 0.06377096       3  3
#&gt; parent_s 0.20882324       2  3</div><div class='input'>
  <span class='co'># This is the evaluation used for the validation of software packages</span>
  <span class='co'># in the expertise from 2014</span>
  <span class='no'>m_soil</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'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='fu'>c</span>(<span class='st'>"M1"</span>, <span class='st'>"M2"</span>)),
                    <span class='kw'>M1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"M3"</span>),
                    <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"M3"</span>),
                    <span class='kw'>M3</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></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='no'>f_soil</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>m_soil</span>, <span class='no'>test_data_from_UBA_2014</span><span class='kw'>[[</span><span class='fl'>3</span>]]$<span class='no'>data</span>)</div><div class='output co'>#&gt; Model cost at call  1 :  340.115 
#&gt; Model cost at call  3 :  340.115 
#&gt; Model cost at call  12 :  278.5521 
#&gt; Model cost at call  14 :  278.5499 
#&gt; Model cost at call  22 :  244.4153 
#&gt; Model cost at call  24 :  244.4152 
#&gt; Model cost at call  32 :  211.0249 
#&gt; Model cost at call  34 :  211.0247 
#&gt; Model cost at call  42 :  151.2576 
#&gt; Model cost at call  44 :  151.2575 
#&gt; Model cost at call  46 :  151.2574 
#&gt; Model cost at call  52 :  109.7633 
#&gt; Model cost at call  53 :  100.8415 
#&gt; Model cost at call  58 :  100.8412 
#&gt; Model cost at call  61 :  100.8412 
#&gt; Model cost at call  62 :  100.8411 
#&gt; Model cost at call  64 :  70.07576 
#&gt; Model cost at call  66 :  70.07568 
#&gt; Model cost at call  76 :  64.29488 
#&gt; Model cost at call  78 :  64.29487 
#&gt; Model cost at call  86 :  61.39756 
#&gt; Model cost at call  88 :  61.39755 
#&gt; Model cost at call  96 :  57.47933 
#&gt; Model cost at call  98 :  57.47932 
#&gt; Model cost at call  106 :  52.46647 
#&gt; Model cost at call  108 :  52.46646 
#&gt; Model cost at call  116 :  48.17301 
#&gt; Model cost at call  118 :  48.173 
#&gt; Model cost at call  126 :  45.15666 
#&gt; Model cost at call  128 :  45.15665 
#&gt; Model cost at call  137 :  44.55574 
#&gt; Model cost at call  139 :  44.55573 
#&gt; Model cost at call  143 :  44.55573 
#&gt; Model cost at call  147 :  43.13847 
#&gt; Model cost at call  149 :  43.13847 
#&gt; Model cost at call  151 :  43.13847 
#&gt; Model cost at call  158 :  43.11922 
#&gt; Model cost at call  160 :  43.11922 
#&gt; Model cost at call  168 :  42.29535 
#&gt; Model cost at call  170 :  42.29535 
#&gt; Model cost at call  178 :  42.03979 
#&gt; Model cost at call  180 :  42.03979 
#&gt; Model cost at call  189 :  41.56286 
#&gt; Model cost at call  190 :  41.12187 
#&gt; Model cost at call  191 :  39.73019 
#&gt; Model cost at call  192 :  39.31762 
#&gt; Model cost at call  193 :  39.31762 
#&gt; Model cost at call  196 :  39.31762 
#&gt; Model cost at call  203 :  38.6346 
#&gt; Model cost at call  204 :  38.6346 
#&gt; Model cost at call  205 :  38.63459 
#&gt; Model cost at call  209 :  38.63459 
#&gt; Model cost at call  210 :  38.63459 
#&gt; Model cost at call  211 :  38.63459 
#&gt; Model cost at call  213 :  38.12767 
#&gt; Model cost at call  215 :  38.12767 
#&gt; Model cost at call  223 :  38.04349 
#&gt; Model cost at call  225 :  38.04349 
#&gt; Model cost at call  229 :  38.04349 
#&gt; Model cost at call  233 :  37.93963 
#&gt; Model cost at call  236 :  37.93963 
#&gt; Model cost at call  243 :  37.87645 
#&gt; Model cost at call  244 :  37.87645 
#&gt; Model cost at call  254 :  37.79144 
#&gt; Model cost at call  256 :  37.79144 
#&gt; Model cost at call  264 :  37.7493 
#&gt; Model cost at call  269 :  37.7493 
#&gt; Model cost at call  274 :  37.72466 
#&gt; Model cost at call  275 :  37.72466 
#&gt; Model cost at call  279 :  37.72466 
#&gt; Model cost at call  283 :  37.72466 
#&gt; Model cost at call  284 :  37.71402 
#&gt; Model cost at call  285 :  37.71402 
#&gt; Model cost at call  287 :  37.71402 
#&gt; Model cost at call  294 :  37.70366 
#&gt; Model cost at call  295 :  37.70366 
#&gt; Model cost at call  296 :  37.70366 
#&gt; Model cost at call  305 :  37.69553 
#&gt; Model cost at call  306 :  37.69553 
#&gt; Model cost at call  307 :  37.69553 
#&gt; Model cost at call  315 :  37.6936 
#&gt; Model cost at call  319 :  37.6936 
#&gt; Model cost at call  321 :  37.6936 
#&gt; Model cost at call  326 :  37.6924 
#&gt; Model cost at call  328 :  37.6924 
#&gt; Model cost at call  329 :  37.6924 
#&gt; Model cost at call  336 :  37.69198 
#&gt; Model cost at call  338 :  37.69198 
#&gt; Model cost at call  344 :  37.69198 
#&gt; Model cost at call  348 :  37.69197 
#&gt; Model cost at call  350 :  37.69197 
#&gt; Model cost at call  363 :  37.69197 
#&gt; Model cost at call  364 :  37.69197 
#&gt; Model cost at call  367 :  37.69197 
#&gt; Model cost at call  382 :  37.69197 
#&gt; Model cost at call  384 :  37.69197 
#&gt; Model cost at call  387 :  37.69197 
#&gt; Model cost at call  401 :  37.69197 
#&gt; Model cost at call  420 :  37.69197 
#&gt; Model cost at call  441 :  37.69197 
#&gt; Optimisation by method Port successfully terminated.</div><div class='input'>  <span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span>(<span class='no'>f_soil</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"topright"</span>, <span class='st'>"topright"</span>, <span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>))</div><img src='test_data_from_UBA_2014-16.png' alt='' width='540' height='400' /><div class='input'>  <span class='fu'>summary</span>(<span class='no'>f_soil</span>)$<span class='no'>bpar</span></div><div class='output co'>#&gt;                   Estimate  se_notrans    t value       Pr(&gt;t)        Lower
#&gt; parent_0       76.55425583 0.943443834 81.1434164 4.422340e-30 74.602593306
#&gt; k_parent        0.12081956 0.004815515 25.0896457 1.639665e-18  0.111257526
#&gt; k_M1            0.84258650 0.930121206  0.9058889 1.871937e-01  0.085876305
#&gt; k_M2            0.04210878 0.013729902  3.0669396 2.729137e-03  0.021450631
#&gt; k_M3            0.01122919 0.008044866  1.3958205 8.804914e-02  0.002550985
#&gt; f_parent_to_M1  0.32240199 0.278620411  1.1571370 1.295466e-01           NA
#&gt; f_parent_to_M2  0.16099854 0.030548889  5.2701930 1.196191e-05           NA
#&gt; f_M1_to_M3      0.27921500 0.314732717  0.8871496 1.920907e-01  0.015016888
#&gt; f_M2_to_M3      0.55641332 0.650247079  0.8556952 2.004966e-01  0.005360551
#&gt;                      Upper
#&gt; parent_0       78.50591836
#&gt; k_parent        0.13120340
#&gt; k_M1            8.26714671
#&gt; k_M2            0.08266187
#&gt; k_M3            0.04942980
#&gt; f_parent_to_M1          NA
#&gt; f_parent_to_M2          NA
#&gt; f_M1_to_M3      0.90777217
#&gt; f_M2_to_M3      0.99658634</div><div class='input'>  <span class='fu'><a href='mkinerrmin.html'>mkinerrmin</a></span>(<span class='no'>f_soil</span>)</div><div class='output co'>#&gt;             err.min n.optim df
#&gt; All data 0.09649963       9 20
#&gt; parent   0.04721283       2  6
#&gt; M1       0.26551209       2  5
#&gt; M2       0.20327575       2  5
#&gt; M3       0.05196549       3  4</div></pre>
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