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    <h1>Subsetting method for mmkin objects</h1>
    
    <div class="hidden name"><code>Extract.mmkin.Rd</code></div>
    </div>

    <div class="ref-description">
    
    <p>Subsetting method for mmkin objects.</p>
    
    </div>

    <pre class="usage"># S3 method for mmkin
[(x, i, j, ..., drop = FALSE)</pre>
    
    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
    <table class="ref-arguments">
    <colgroup><col class="name" /><col class="desc" /></colgroup>
    <tr>
      <th>x</th>
      <td><p>An <code><a href='mmkin.html'>mmkin</a> object</code></p></td>
    </tr>
    <tr>
      <th>i</th>
      <td><p>Row index selecting the fits for specific models</p></td>
    </tr>
    <tr>
      <th>j</th>
      <td><p>Column index selecting the fits to specific datasets</p></td>
    </tr>
    <tr>
      <th>...</th>
      <td><p>Not used, only there to satisfy the generic method definition</p></td>
    </tr>
    <tr>
      <th>drop</th>
      <td><p>If FALSE, the method always returns an mmkin object, otherwise either
  a list of mkinfit objects or a single mkinfit object.</p></td>
    </tr>
    </table>
    
    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>

    <p>An object of class <code><a href='mmkin.html'>mmkin</a></code>.</p>
    

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'>  <span class='co'># Only use one core, to pass R CMD check --as-cran</span>
  <span class='no'>fits</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span>(<span class='fu'>c</span>(<span class='st'>"SFO"</span>, <span class='st'>"FOMC"</span>), <span class='fu'>list</span>(<span class='kw'>B</span> <span class='kw'>=</span> <span class='no'>FOCUS_2006_B</span>, <span class='kw'>C</span> <span class='kw'>=</span> <span class='no'>FOCUS_2006_C</span>),
                <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
  <span class='no'>fits</span>[<span class='st'>"FOMC"</span>, ]</div><div class='output co'>#&gt;       dataset
#&gt; model  B       C      
#&gt;   FOMC List,45 List,45
#&gt; attr(,"class")
#&gt; [1] "mmkin"</div><div class='input'>  <span class='no'>fits</span>[, <span class='st'>"B"</span>]</div><div class='output co'>#&gt;       dataset
#&gt; model  B      
#&gt;   SFO  List,45
#&gt;   FOMC List,45
#&gt; attr(,"class")
#&gt; [1] "mmkin"</div><div class='input'>  <span class='no'>fits</span>[<span class='st'>"SFO"</span>, <span class='st'>"B"</span>]</div><div class='output co'>#&gt;      dataset
#&gt; model B      
#&gt;   SFO List,45
#&gt; attr(,"class")
#&gt; [1] "mmkin"</div><div class='input'>
  <span class='fu'>head</span>(
    <span class='co'># This extracts an mkinfit object with lots of components</span>
    <span class='no'>fits</span><span class='kw'>[[</span><span class='st'>"FOMC"</span>, <span class='st'>"B"</span>]]
  )</div><div class='output co'>#&gt; $par
#&gt;  parent_0 log_alpha  log_beta 
#&gt; 99.666193  2.549849  5.050586 
#&gt; 
#&gt; $ssr
#&gt; [1] 28.58291
#&gt; 
#&gt; $convergence
#&gt; [1] 0
#&gt; 
#&gt; $iterations
#&gt; [1] 21
#&gt; 
#&gt; $evaluations
#&gt; function gradient 
#&gt;       25       78 
#&gt; 
#&gt; $counts
#&gt; [1] "relative convergence (4)"
#&gt; </div><div class='input'>
  <span class='fu'>head</span>(
  <span class='co'># The same can be achieved by</span>
    <span class='no'>fits</span>[<span class='st'>"SFO"</span>, <span class='st'>"B"</span>, <span class='kw'>drop</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>]
  )</div><div class='output co'>#&gt; [[1]]
#&gt; $par
#&gt;          parent_0 log_k_parent_sink 
#&gt;         99.174072         -2.549028 
#&gt; 
#&gt; $ssr
#&gt; [1] 30.65564
#&gt; 
#&gt; $convergence
#&gt; [1] 0
#&gt; 
#&gt; $iterations
#&gt; [1] 5
#&gt; 
#&gt; $evaluations
#&gt; function gradient 
#&gt;        8       15 
#&gt; 
#&gt; $counts
#&gt; [1] "relative convergence (4)"
#&gt; 
#&gt; $hessian
#&gt;                     parent_0 log_k_parent_sink
#&gt; parent_0            4.163631         -94.09343
#&gt; log_k_parent_sink -94.093431        6311.34610
#&gt; 
#&gt; $residuals
#&gt;      parent      parent      parent      parent      parent      parent 
#&gt;  0.55407218 -2.98452128  4.20445742 -1.68599939 -0.58185357 -0.72033730 
#&gt;      parent      parent 
#&gt; -0.24260405 -0.07020339 
#&gt; 
#&gt; $ms
#&gt; [1] 3.831956
#&gt; 
#&gt; $var_ms
#&gt;   parent 
#&gt; 3.831956 
#&gt; 
#&gt; $var_ms_unscaled
#&gt;   parent 
#&gt; 3.831956 
#&gt; 
#&gt; $var_ms_unweighted
#&gt;   parent 
#&gt; 3.831956 
#&gt; 
#&gt; $rank
#&gt; [1] 2
#&gt; 
#&gt; $df.residual
#&gt; [1] 6
#&gt; 
#&gt; $solution_type
#&gt; [1] "analytical"
#&gt; 
#&gt; $transform_rates
#&gt; [1] TRUE
#&gt; 
#&gt; $transform_fractions
#&gt; [1] TRUE
#&gt; 
#&gt; $method.modFit
#&gt; [1] "Port"
#&gt; 
#&gt; $maxit.modFit
#&gt; [1] "auto"
#&gt; 
#&gt; $calls
#&gt; [1] 29
#&gt; 
#&gt; $time
#&gt;        User      System verstrichen 
#&gt;       0.179       0.000       0.184 
#&gt; 
#&gt; $mkinmod
#&gt; &lt;mkinmod&gt; model generated with
#&gt; Use of formation fractions $use_of_ff: min 
#&gt; Specification $spec:
#&gt; $parent
#&gt; $type: SFO; $sink: TRUE
#&gt; Coefficient matrix $coefmat available
#&gt; Differential equations:
#&gt; d_parent/dt = - k_parent_sink * parent
#&gt; 
#&gt; $observed
#&gt;     name time value
#&gt; 1 parent    0 98.62
#&gt; 2 parent    3 81.43
#&gt; 3 parent    7 53.18
#&gt; 4 parent   14 34.89
#&gt; 5 parent   30 10.09
#&gt; 6 parent   62  1.50
#&gt; 7 parent   90  0.33
#&gt; 8 parent  118  0.08
#&gt; 
#&gt; $obs_vars
#&gt; [1] "parent"
#&gt; 
#&gt; $predicted
#&gt;       name       time       value
#&gt; 1   parent   0.000000 99.17407218
#&gt; 2   parent   1.191919 90.35253561
#&gt; 3   parent   2.383838 82.31567498
#&gt; 4   parent   3.000000 78.44547872
#&gt; 5   parent   3.575758 74.99369333
#&gt; 6   parent   4.767677 68.32300215
#&gt; 7   parent   5.959596 62.24566915
#&gt; 8   parent   7.000000 57.38445742
#&gt; 9   parent   7.151515 56.70891509
#&gt; 10  parent   8.343434 51.66465547
#&gt; 11  parent   9.535354 47.06908288
#&gt; 12  parent  10.727273 42.88228661
#&gt; 13  parent  11.919192 39.06790599
#&gt; 14  parent  13.111111 35.59281463
#&gt; 15  parent  14.000000 33.20400061
#&gt; 16  parent  14.303030 32.42683275
#&gt; 17  parent  15.494949 29.54246504
#&gt; 18  parent  16.686869 26.91466193
#&gt; 19  parent  17.878788 24.52060198
#&gt; 20  parent  19.070707 22.33949373
#&gt; 21  parent  20.262626 20.35239512
#&gt; 22  parent  21.454545 18.54204899
#&gt; 23  parent  22.646465 16.89273320
#&gt; 24  parent  23.838384 15.39012410
#&gt; 25  parent  25.030303 14.02117212
#&gt; 26  parent  26.222222 12.77398846
#&gt; 27  parent  27.414141 11.63774182
#&gt; 28  parent  28.606061 10.60256435
#&gt; 29  parent  29.797980  9.65946594
#&gt; 30  parent  30.000000  9.50814643
#&gt; 31  parent  30.989899  8.80025617
#&gt; 32  parent  32.181818  8.01747313
#&gt; 33  parent  33.373737  7.30431867
#&gt; 34  parent  34.565657  6.65459931
#&gt; 35  parent  35.757576  6.06267251
#&gt; 36  parent  36.949495  5.52339762
#&gt; 37  parent  38.141414  5.03209124
#&gt; 38  parent  39.333333  4.58448658
#&gt; 39  parent  40.525253  4.17669637
#&gt; 40  parent  41.717172  3.80517911
#&gt; 41  parent  42.909091  3.46670832
#&gt; 42  parent  44.101010  3.15834451
#&gt; 43  parent  45.292929  2.87740968
#&gt; 44  parent  46.484848  2.62146400
#&gt; 45  parent  47.676768  2.38828471
#&gt; 46  parent  48.868687  2.17584671
#&gt; 47  parent  50.060606  1.98230508
#&gt; 48  parent  51.252525  1.80597899
#&gt; 49  parent  52.444444  1.64533711
#&gt; 50  parent  53.636364  1.49898432
#&gt; 51  parent  54.828283  1.36564963
#&gt; 52  parent  56.020202  1.24417505
#&gt; 53  parent  57.212121  1.13350565
#&gt; 54  parent  58.404040  1.03268029
#&gt; 55  parent  59.595960  0.94082335
#&gt; 56  parent  60.787879  0.85713708
#&gt; 57  parent  61.979798  0.78089471
#&gt; 58  parent  62.000000  0.77966270
#&gt; 59  parent  63.171717  0.71143411
#&gt; 60  parent  64.363636  0.64815202
#&gt; 61  parent  65.555556  0.59049888
#&gt; 62  parent  66.747475  0.53797399
#&gt; 63  parent  67.939394  0.49012119
#&gt; 64  parent  69.131313  0.44652489
#&gt; 65  parent  70.323232  0.40680649
#&gt; 66  parent  71.515152  0.37062104
#&gt; 67  parent  72.707071  0.33765429
#&gt; 68  parent  73.898990  0.30761993
#&gt; 69  parent  75.090909  0.28025713
#&gt; 70  parent  76.282828  0.25532825
#&gt; 71  parent  77.474747  0.23261679
#&gt; 72  parent  78.666667  0.21192552
#&gt; 73  parent  79.858586  0.19307474
#&gt; 74  parent  81.050505  0.17590074
#&gt; 75  parent  82.242424  0.16025436
#&gt; 76  parent  83.434343  0.14599973
#&gt; 77  parent  84.626263  0.13301305
#&gt; 78  parent  85.818182  0.12118154
#&gt; 79  parent  87.010101  0.11040244
#&gt; 80  parent  88.202020  0.10058214
#&gt; 81  parent  89.393939  0.09163535
#&gt; 82  parent  90.000000  0.08739595
#&gt; 83  parent  90.585859  0.08348439
#&gt; 84  parent  91.777778  0.07605845
#&gt; 85  parent  92.969697  0.06929305
#&gt; 86  parent  94.161616  0.06312943
#&gt; 87  parent  95.353535  0.05751406
#&gt; 88  parent  96.545455  0.05239819
#&gt; 89  parent  97.737374  0.04773737
#&gt; 90  parent  98.929293  0.04349113
#&gt; 91  parent 100.121212  0.03962259
#&gt; 92  parent 101.313131  0.03609816
#&gt; 93  parent 102.505051  0.03288723
#&gt; 94  parent 103.696970  0.02996191
#&gt; 95  parent 104.888889  0.02729679
#&gt; 96  parent 106.080808  0.02486874
#&gt; 97  parent 107.272727  0.02265667
#&gt; 98  parent 108.464646  0.02064136
#&gt; 99  parent 109.656566  0.01880531
#&gt; 100 parent 110.848485  0.01713257
#&gt; 101 parent 112.040404  0.01560863
#&gt; 102 parent 113.232323  0.01422024
#&gt; 103 parent 114.424242  0.01295535
#&gt; 104 parent 115.616162  0.01180297
#&gt; 105 parent 116.808081  0.01075310
#&gt; 106 parent 118.000000  0.00979661
#&gt; 
#&gt; $cost
#&gt; function (P) 
#&gt; {
#&gt;     assign("calls", calls + 1, inherits = TRUE)
#&gt;     if (trace_parms) 
#&gt;         cat(P, "\n")
#&gt;     if (length(state.ini.optim) &gt; 0) {
#&gt;         odeini &lt;- c(P[1:length(state.ini.optim)], state.ini.fixed)
#&gt;         names(odeini) &lt;- c(state.ini.optim.boxnames, state.ini.fixed.boxnames)
#&gt;     }
#&gt;     else {
#&gt;         odeini &lt;- state.ini.fixed
#&gt;         names(odeini) &lt;- state.ini.fixed.boxnames
#&gt;     }
#&gt;     odeparms &lt;- c(P[(length(state.ini.optim) + 1):length(P)], 
#&gt;         transparms.fixed)
#&gt;     parms &lt;- backtransform_odeparms(odeparms, mkinmod, transform_rates = transform_rates, 
#&gt;         transform_fractions = transform_fractions)
#&gt;     out &lt;- mkinpredict(mkinmod, parms, odeini, outtimes, solution_type = solution_type, 
#&gt;         use_compiled = use_compiled, method.ode = method.ode, 
#&gt;         atol = atol, rtol = rtol, ...)
#&gt;     assign("out_predicted", out, inherits = TRUE)
#&gt;     mC &lt;- modCost(out, observed, y = "value", err = err, weight = weight, 
#&gt;         scaleVar = scaleVar)
#&gt;     if (mC$model &lt; cost.old) {
#&gt;         if (!quiet) 
#&gt;             cat("Model cost at call ", calls, ": ", mC$model, 
#&gt;                 "\n")
#&gt;         if (plot) {
#&gt;             outtimes_plot = seq(min(observed$time), max(observed$time), 
#&gt;                 length.out = 100)
#&gt;             out_plot &lt;- mkinpredict(mkinmod, parms, odeini, outtimes_plot, 
#&gt;                 solution_type = solution_type, use_compiled = use_compiled, 
#&gt;                 method.ode = method.ode, atol = atol, rtol = rtol, 
#&gt;                 ...)
#&gt;             plot(0, type = "n", xlim = range(observed$time), 
#&gt;                 ylim = c(0, max(observed$value, na.rm = TRUE)), 
#&gt;                 xlab = "Time", ylab = "Observed")
#&gt;             col_obs &lt;- pch_obs &lt;- 1:length(obs_vars)
#&gt;             lty_obs &lt;- rep(1, length(obs_vars))
#&gt;             names(col_obs) &lt;- names(pch_obs) &lt;- names(lty_obs) &lt;- obs_vars
#&gt;             for (obs_var in obs_vars) {
#&gt;                 points(subset(observed, name == obs_var, c(time, 
#&gt;                   value)), pch = pch_obs[obs_var], col = col_obs[obs_var])
#&gt;             }
#&gt;             matlines(out_plot$time, out_plot[-1], col = col_obs, 
#&gt;                 lty = lty_obs)
#&gt;             legend("topright", inset = c(0.05, 0.05), legend = obs_vars, 
#&gt;                 col = col_obs, pch = pch_obs, lty = 1:length(pch_obs))
#&gt;         }
#&gt;         assign("cost.old", mC$model, inherits = TRUE)
#&gt;     }
#&gt;     return(mC)
#&gt; }
#&gt; &lt;bytecode: 0x55555b9b9d58&gt;
#&gt; &lt;environment: 0x55555be59518&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 = "value", err = err, weight = weight, 
#&gt;         scaleVar = scaleVar)
#&gt;     return(mC)
#&gt; }
#&gt; &lt;bytecode: 0x55555c2673f8&gt;
#&gt; &lt;environment: 0x55555be59518&gt;
#&gt; 
#&gt; $hessian_notrans
#&gt;                   parent_0 k_parent_sink
#&gt; parent_0          4.163631     -1203.894
#&gt; k_parent_sink -1203.893702   1033188.753
#&gt; 
#&gt; $start
#&gt;               value   type
#&gt; parent_0      98.62  state
#&gt; k_parent_sink  0.10 deparm
#&gt; 
#&gt; $start_transformed
#&gt;                       value lower upper
#&gt; parent_0          98.620000  -Inf   Inf
#&gt; log_k_parent_sink -2.302585  -Inf   Inf
#&gt; 
#&gt; $fixed
#&gt; [1] value type 
#&gt; &lt;0 Zeilen&gt; (oder row.names mit Länge 0)
#&gt; 
#&gt; $data
#&gt;   time variable observed   predicted    residual
#&gt; 1    0   parent    98.62 99.17407218 -0.55407218
#&gt; 2    3   parent    81.43 78.44547872  2.98452128
#&gt; 3    7   parent    53.18 57.38445742 -4.20445742
#&gt; 4   14   parent    34.89 33.20400061  1.68599939
#&gt; 5   30   parent    10.09  9.50814643  0.58185357
#&gt; 6   62   parent     1.50  0.77966270  0.72033730
#&gt; 7   90   parent     0.33  0.08739595  0.24260405
#&gt; 8  118   parent     0.08  0.00979661  0.07020339
#&gt; 
#&gt; $atol
#&gt; [1] 1e-08
#&gt; 
#&gt; $rtol
#&gt; [1] 1e-10
#&gt; 
#&gt; $weight.ini
#&gt; [1] "none"
#&gt; 
#&gt; $tc.ini
#&gt; sigma_low  rsd_high 
#&gt;      0.50      0.07 
#&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_sink 
#&gt;   99.17407218    0.07815759 
#&gt; 
#&gt; $bparms.fixed
#&gt; numeric(0)
#&gt; 
#&gt; $bparms.ode
#&gt; k_parent_sink 
#&gt;    0.07815759 
#&gt; 
#&gt; $bparms.state
#&gt;   parent 
#&gt; 99.17407 
#&gt; 
#&gt; $date
#&gt; [1] "Wed Jun  6 01:21:24 2018"
#&gt; 
#&gt; $version
#&gt; [1] "0.9.47.1"
#&gt; 
#&gt; $Rversion
#&gt; [1] "3.5.0"
#&gt; 
#&gt; attr(,"class")
#&gt; [1] "mkinfit" "modFit" 
#&gt; </div></pre>
  </div>
  <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
    <h2>Contents</h2>
    <ul class="nav nav-pills nav-stacked">
      <li><a href="#arguments">Arguments</a></li>
      
      <li><a href="#value">Value</a></li>
      
      <li><a href="#examples">Examples</a></li>
    </ul>

    <h2>Author</h2>
    
  Johannes Ranke

  </div>
</div>

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  <p>Developed by Johannes Ranke.</p>
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