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<!DOCTYPE html>
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<title>[.mmkin. mkin 0.9.42</title>
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  Johannes Ranke
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      <h1>Subsetting method for mmkin objects</h1>

<div class="row">
  <div class="span8">
    <h2>Usage</h2>
    <pre><div>"["(x, i, j, ..., drop&nbsp;=&nbsp;FALSE)</div></pre>
    
    <h2>Arguments</h2>
    <dl>
      <dt>x</dt>
      <dd>An <code><a href='mmkin.html'>mmkin</a> object</code></dd>
      <dt>i</dt>
      <dd>Row index selecting the fits for specific models</dd>
      <dt>j</dt>
      <dd>Column index selecting the fits to specific datasets</dd>
      <dt>...</dt>
      <dd>Not used, only there to satisfy the generic method definition</dd>
      <dt>drop</dt>
      <dd>If FALSE, the method always returns an mmkin object, otherwise either 
  a list of mkinfit objects or a single mkinfit object.</dd>
    </dl>
    
    <div class="Description">
      <h2>Description</h2>

      <p>Subsetting method for mmkin objects.</p>
  
    </div>

    <div class="Value">
      <h2>Value</h2>

      <p><dl>
  An object of class <code><a href='mmkin.html'>mmkin</a></code>.
</dl></p>
  
    </div>
    
    <h2 id="examples">Examples</h2>
    <pre class="examples"><div class='input'>  # Only use one core, to pass R CMD check --as-cran
  fits &lt;- mmkin(c(&quot;SFO&quot;, &quot;FOMC&quot;), list(B = FOCUS_2006_B, C = FOCUS_2006_C), 
                cores = 1, quiet = TRUE)
  fits[&quot;FOMC&quot;, ]
</div>
<div class='output'>      dataset
model  B       C      
  FOMC List,42 List,42
attr(,&quot;class&quot;)
[1] &quot;mmkin&quot;
</div>
<div class='input'>  fits[, &quot;B&quot;]
</div>
<div class='output'>      dataset
model  B      
  SFO  List,42
  FOMC List,42
attr(,&quot;class&quot;)
[1] &quot;mmkin&quot;
</div>
<div class='input'>  fits[, &quot;B&quot;, drop = TRUE]$FOMC
</div>
<div class='output'>$par
 parent_0 log_alpha  log_beta 
99.666193  2.549849  5.050586 

$ssr
[1] 28.58291

$convergence
[1] 0

$iterations
[1] 21

$evaluations
function gradient 
      25       78 

$counts
[1] &quot;both X-convergence and relative convergence (5)&quot;

$hessian
            parent_0   log_alpha    log_beta
parent_0    4.123033   -95.69983    93.17699
log_alpha -95.699832  6618.85833 -6352.46648
log_beta   93.176993 -6352.46648  6101.23483

$residuals
      parent       parent       parent       parent       parent       parent       parent       parent 
 1.046192647 -3.322396479  3.655156669 -1.705316770  0.406306255 -0.123734689 -0.036886982 -0.006240458 

$ms
[1] 3.572863

$var_ms
  parent 
3.572863 

$var_ms_unscaled
  parent 
3.572863 

$var_ms_unweighted
  parent 
3.572863 

$rank
[1] 3

$df.residual
[1] 5

$solution_type
[1] &quot;analytical&quot;

$transform_rates
[1] TRUE

$transform_fractions
[1] TRUE

$method.modFit
[1] &quot;Port&quot;

$maxit.modFit
[1] &quot;auto&quot;

$calls
[1] 111

$time
   user  system elapsed 
  0.288   0.000   0.288 

$mkinmod
&lt;mkinmod&gt; model generated with
Use of formation fractions $use_of_ff: min 
Specification $spec:
$parent
$type: FOMC; $sink: TRUE

$observed
    name time value
1 parent    0 98.62
2 parent    3 81.43
3 parent    7 53.18
4 parent   14 34.89
5 parent   30 10.09
6 parent   62  1.50
7 parent   90  0.33
8 parent  118  0.08

$obs_vars
[1] &quot;parent&quot;

$predicted
      name       time       value
1   parent   0.000000 99.66619265
2   parent   1.191919 90.41690342
3   parent   2.383838 82.08630014
4   parent   3.000000 78.10760352
5   parent   3.575758 74.57722848
6   parent   4.767677 67.80342415
7   parent   5.959596 61.68822425
8   parent   7.000000 56.83515667
9   parent   7.151515 56.16343898
10  parent   8.343434 51.16836285
11  parent   9.535354 46.64890734
12  parent  10.727273 42.55683931
13  parent  11.919192 38.84911158
14  parent  13.111111 35.48727414
15  parent  14.000000 33.18468323
16  parent  14.303030 32.43695565
17  parent  15.494949 29.66740651
18  parent  16.686869 27.15109578
19  parent  17.878788 24.86335532
20  parent  19.070707 22.78206538
21  parent  20.262626 20.88737647
22  parent  21.454545 19.16146324
23  parent  22.646465 17.58830644
24  parent  23.838384 16.15349953
25  parent  25.030303 14.84407724
26  parent  26.222222 13.64836315
27  parent  27.414141 12.55583436
28  parent  28.606061 11.55700107
29  parent  29.797980 10.64329940
30  parent  30.000000 10.49630626
31  parent  30.989899  9.80699593
32  parent  32.181818  9.04110261
33  parent  33.373737  8.33930082
34  parent  34.565657  7.69587362
35  parent  35.757576  7.10564515
36  parent  36.949495  6.56392657
37  parent  38.141414  6.06646759
38  parent  39.333333  5.60941311
39  parent  40.525253  5.18926438
40  parent  41.717172  4.80284421
41  parent  42.909091  4.44726569
42  parent  44.101010  4.11990420
43  parent  45.292929  3.81837216
44  parent  46.484848  3.54049644
45  parent  47.676768  3.28429799
46  parent  48.868687  3.04797350
47  parent  50.060606  2.82987892
48  parent  51.252525  2.62851456
49  parent  52.444444  2.44251172
50  parent  53.636364  2.27062056
51  parent  54.828283  2.11169922
52  parent  56.020202  1.96470393
53  parent  57.212121  1.82868009
54  parent  58.404040  1.70275424
55  parent  59.595960  1.58612677
56  parent  60.787879  1.47806529
57  parent  61.979798  1.37789865
58  parent  62.000000  1.37626531
59  parent  63.171717  1.28501157
60  parent  64.363636  1.19883967
61  parent  65.555556  1.11886504
62  parent  66.747475  1.04461220
63  parent  67.939394  0.97564441
64  parent  69.131313  0.91156031
65  parent  70.323232  0.85199096
66  parent  71.515152  0.79659697
67  parent  72.707071  0.74506609
68  parent  73.898990  0.69711084
69  parent  75.090909  0.65246649
70  parent  76.282828  0.61088912
71  parent  77.474747  0.57215389
72  parent  78.666667  0.53605348
73  parent  79.858586  0.50239663
74  parent  81.050505  0.47100683
75  parent  82.242424  0.44172111
76  parent  83.434343  0.41438896
77  parent  84.626263  0.38887128
78  parent  85.818182  0.36503953
79  parent  87.010101  0.34277481
80  parent  88.202020  0.32196716
81  parent  89.393939  0.30251479
82  parent  90.000000  0.29311302
83  parent  90.585859  0.28432347
84  parent  91.777778  0.26730596
85  parent  92.969697  0.25138141
86  parent  94.161616  0.23647487
87  parent  95.353535  0.22251689
88  parent  96.545455  0.20944302
89  parent  97.737374  0.19719349
90  parent  98.929293  0.18571281
91  parent 100.121212  0.17494947
92  parent 101.313131  0.16485560
93  parent 102.505051  0.15538676
94  parent 103.696970  0.14650163
95  parent 104.888889  0.13816179
96  parent 106.080808  0.13033150
97  parent 107.272727  0.12297753
98  parent 108.464646  0.11606895
99  parent 109.656566  0.10957695
100 parent 110.848485  0.10347470
101 parent 112.040404  0.09773723
102 parent 113.232323  0.09234125
103 parent 114.424242  0.08726506
104 parent 115.616162  0.08248842
105 parent 116.808081  0.07799245
106 parent 118.000000  0.07375954

$cost
function (P) 
{
    assign(&quot;calls&quot;, calls + 1, inherits = TRUE)
    if (trace_parms) 
        cat(P, &quot;\n&quot;)
    if (length(state.ini.optim) &gt; 0) {
        odeini &lt;- c(P[1:length(state.ini.optim)], state.ini.fixed)
        names(odeini) &lt;- c(state.ini.optim.boxnames, state.ini.fixed.boxnames)
    }
    else {
        odeini &lt;- state.ini.fixed
        names(odeini) &lt;- state.ini.fixed.boxnames
    }
    odeparms &lt;- c(P[(length(state.ini.optim) + 1):length(P)], 
        transparms.fixed)
    parms &lt;- backtransform_odeparms(odeparms, mkinmod, transform_rates = transform_rates, 
        transform_fractions = transform_fractions)
    out &lt;- mkinpredict(mkinmod, parms, odeini, outtimes, solution_type = solution_type, 
        use_compiled = use_compiled, method.ode = method.ode, 
        atol = atol, rtol = rtol, ...)
    assign(&quot;out_predicted&quot;, out, inherits = TRUE)
    mC &lt;- modCost(out, observed, y = &quot;value&quot;, err = err, weight = weight, 
        scaleVar = scaleVar)
    if (mC$model &lt; cost.old) {
        if (!quiet) 
            cat(&quot;Model cost at call &quot;, calls, &quot;: &quot;, mC$model, 
                &quot;\n&quot;)
        if (plot) {
            outtimes_plot = seq(min(observed$time), max(observed$time), 
                length.out = 100)
            out_plot &lt;- mkinpredict(mkinmod, parms, odeini, outtimes_plot, 
                solution_type = solution_type, use_compiled = use_compiled, 
                method.ode = method.ode, atol = atol, rtol = rtol, 
                ...)
            plot(0, type = &quot;n&quot;, xlim = range(observed$time), 
                ylim = c(0, max(observed$value, na.rm = TRUE)), 
                xlab = &quot;Time&quot;, ylab = &quot;Observed&quot;)
            col_obs &lt;- pch_obs &lt;- 1:length(obs_vars)
            lty_obs &lt;- rep(1, length(obs_vars))
            names(col_obs) &lt;- names(pch_obs) &lt;- names(lty_obs) &lt;- obs_vars
            for (obs_var in obs_vars) {
                points(subset(observed, name == obs_var, c(time, 
                  value)), pch = pch_obs[obs_var], col = col_obs[obs_var])
            }
            matlines(out_plot$time, out_plot[-1], col = col_obs, 
                lty = lty_obs)
            legend(&quot;topright&quot;, inset = c(0.05, 0.05), legend = obs_vars, 
                col = col_obs, pch = pch_obs, lty = 1:length(pch_obs))
        }
        assign(&quot;cost.old&quot;, mC$model, inherits = TRUE)
    }
    return(mC)
}
&lt;environment: 0x23d9ae8&gt;

$cost_notrans
function (P) 
{
    if (length(state.ini.optim) &gt; 0) {
        odeini &lt;- c(P[1:length(state.ini.optim)], state.ini.fixed)
        names(odeini) &lt;- c(state.ini.optim.boxnames, state.ini.fixed.boxnames)
    }
    else {
        odeini &lt;- state.ini.fixed
        names(odeini) &lt;- state.ini.fixed.boxnames
    }
    odeparms &lt;- c(P[(length(state.ini.optim) + 1):length(P)], 
        parms.fixed)
    out &lt;- mkinpredict(mkinmod, odeparms, odeini, outtimes, solution_type = solution_type, 
        use_compiled = use_compiled, method.ode = method.ode, 
        atol = atol, rtol = rtol, ...)
    mC &lt;- modCost(out, observed, y = &quot;value&quot;, err = err, weight = weight, 
        scaleVar = scaleVar)
    return(mC)
}
&lt;environment: 0x23d9ae8&gt;

$hessian_notrans
           parent_0     alpha       beta
parent_0  4.1230329 -7.473531  0.5968527
alpha    -7.4735307 40.365690 -3.1777189
beta      0.5968527 -3.177719  0.2503425

$start
         value   type
parent_0 98.62  state
alpha     1.00 deparm
beta     10.00 deparm

$start_transformed
              value lower upper
parent_0  98.620000  -Inf   Inf
log_alpha  0.000000  -Inf   Inf
log_beta   2.302585  -Inf   Inf

$fixed
[1] value type 
&lt;0 rows&gt; (or 0-length row.names)

$data
  time variable observed   predicted     residual
1    0   parent    98.62 99.66619265 -1.046192647
2    3   parent    81.43 78.10760352  3.322396479
3    7   parent    53.18 56.83515667 -3.655156669
4   14   parent    34.89 33.18468323  1.705316770
5   30   parent    10.09 10.49630626 -0.406306255
6   62   parent     1.50  1.37626531  0.123734689
7   90   parent     0.33  0.29311302  0.036886982
8  118   parent     0.08  0.07375954  0.006240458

$atol
[1] 1e-08

$rtol
[1] 1e-10

$weight.ini
[1] &quot;none&quot;

$reweight.tol
[1] 1e-08

$reweight.max.iter
[1] 10

$bparms.optim
 parent_0     alpha      beta 
 99.66619  12.80517 156.11390 

$bparms.fixed
numeric(0)

$bparms.ode
    alpha      beta 
 12.80517 156.11390 

$bparms.state
  parent 
99.66619 

$date
[1] &quot;Wed Mar 23 18:10:37 2016&quot;

attr(,&quot;class&quot;)
[1] &quot;mkinfit&quot; &quot;modFit&quot; 
</div>
<div class='input'>  fits[&quot;SFO&quot;, &quot;B&quot;]
</div>
<div class='output'>     dataset
model B      
  SFO List,42
attr(,&quot;class&quot;)
[1] &quot;mmkin&quot;
</div>
<div class='input'>  fits[[&quot;SFO&quot;, &quot;B&quot;]] # This is equivalent to
</div>
<div class='output'>$par
         parent_0 log_k_parent_sink 
        99.174072         -2.549028 

$ssr
[1] 30.65564

$convergence
[1] 0

$iterations
[1] 5

$evaluations
function gradient 
       8       15 

$counts
[1] &quot;relative convergence (4)&quot;

$hessian
                    parent_0 log_k_parent_sink
parent_0            4.163631         -94.09343
log_k_parent_sink -94.093431        6311.34610

$residuals
     parent      parent      parent      parent      parent      parent      parent      parent 
 0.55407218 -2.98452128  4.20445742 -1.68599939 -0.58185357 -0.72033730 -0.24260405 -0.07020339 

$ms
[1] 3.831956

$var_ms
  parent 
3.831956 

$var_ms_unscaled
  parent 
3.831956 

$var_ms_unweighted
  parent 
3.831956 

$rank
[1] 2

$df.residual
[1] 6

$solution_type
[1] &quot;analytical&quot;

$transform_rates
[1] TRUE

$transform_fractions
[1] TRUE

$method.modFit
[1] &quot;Port&quot;

$maxit.modFit
[1] &quot;auto&quot;

$calls
[1] 29

$time
   user  system elapsed 
  0.092   0.004   0.096 

$mkinmod
&lt;mkinmod&gt; model generated with
Use of formation fractions $use_of_ff: min 
Specification $spec:
$parent
$type: SFO; $sink: TRUE
Coefficient matrix $coefmat available

$observed
    name time value
1 parent    0 98.62
2 parent    3 81.43
3 parent    7 53.18
4 parent   14 34.89
5 parent   30 10.09
6 parent   62  1.50
7 parent   90  0.33
8 parent  118  0.08

$obs_vars
[1] &quot;parent&quot;

$predicted
      name       time       value
1   parent   0.000000 99.17407218
2   parent   1.191919 90.35253561
3   parent   2.383838 82.31567498
4   parent   3.000000 78.44547872
5   parent   3.575758 74.99369333
6   parent   4.767677 68.32300215
7   parent   5.959596 62.24566915
8   parent   7.000000 57.38445742
9   parent   7.151515 56.70891509
10  parent   8.343434 51.66465547
11  parent   9.535354 47.06908288
12  parent  10.727273 42.88228661
13  parent  11.919192 39.06790599
14  parent  13.111111 35.59281463
15  parent  14.000000 33.20400061
16  parent  14.303030 32.42683275
17  parent  15.494949 29.54246504
18  parent  16.686869 26.91466193
19  parent  17.878788 24.52060198
20  parent  19.070707 22.33949373
21  parent  20.262626 20.35239512
22  parent  21.454545 18.54204899
23  parent  22.646465 16.89273320
24  parent  23.838384 15.39012410
25  parent  25.030303 14.02117212
26  parent  26.222222 12.77398846
27  parent  27.414141 11.63774182
28  parent  28.606061 10.60256435
29  parent  29.797980  9.65946594
30  parent  30.000000  9.50814643
31  parent  30.989899  8.80025617
32  parent  32.181818  8.01747313
33  parent  33.373737  7.30431867
34  parent  34.565657  6.65459931
35  parent  35.757576  6.06267251
36  parent  36.949495  5.52339762
37  parent  38.141414  5.03209124
38  parent  39.333333  4.58448658
39  parent  40.525253  4.17669637
40  parent  41.717172  3.80517911
41  parent  42.909091  3.46670832
42  parent  44.101010  3.15834451
43  parent  45.292929  2.87740968
44  parent  46.484848  2.62146400
45  parent  47.676768  2.38828471
46  parent  48.868687  2.17584671
47  parent  50.060606  1.98230508
48  parent  51.252525  1.80597899
49  parent  52.444444  1.64533711
50  parent  53.636364  1.49898432
51  parent  54.828283  1.36564963
52  parent  56.020202  1.24417505
53  parent  57.212121  1.13350565
54  parent  58.404040  1.03268029
55  parent  59.595960  0.94082335
56  parent  60.787879  0.85713708
57  parent  61.979798  0.78089471
58  parent  62.000000  0.77966270
59  parent  63.171717  0.71143411
60  parent  64.363636  0.64815202
61  parent  65.555556  0.59049888
62  parent  66.747475  0.53797399
63  parent  67.939394  0.49012119
64  parent  69.131313  0.44652489
65  parent  70.323232  0.40680649
66  parent  71.515152  0.37062104
67  parent  72.707071  0.33765429
68  parent  73.898990  0.30761993
69  parent  75.090909  0.28025713
70  parent  76.282828  0.25532825
71  parent  77.474747  0.23261679
72  parent  78.666667  0.21192552
73  parent  79.858586  0.19307474
74  parent  81.050505  0.17590074
75  parent  82.242424  0.16025436
76  parent  83.434343  0.14599973
77  parent  84.626263  0.13301305
78  parent  85.818182  0.12118154
79  parent  87.010101  0.11040244
80  parent  88.202020  0.10058214
81  parent  89.393939  0.09163535
82  parent  90.000000  0.08739595
83  parent  90.585859  0.08348439
84  parent  91.777778  0.07605845
85  parent  92.969697  0.06929305
86  parent  94.161616  0.06312943
87  parent  95.353535  0.05751406
88  parent  96.545455  0.05239819
89  parent  97.737374  0.04773737
90  parent  98.929293  0.04349113
91  parent 100.121212  0.03962259
92  parent 101.313131  0.03609816
93  parent 102.505051  0.03288723
94  parent 103.696970  0.02996191
95  parent 104.888889  0.02729679
96  parent 106.080808  0.02486874
97  parent 107.272727  0.02265667
98  parent 108.464646  0.02064136
99  parent 109.656566  0.01880531
100 parent 110.848485  0.01713257
101 parent 112.040404  0.01560863
102 parent 113.232323  0.01422024
103 parent 114.424242  0.01295535
104 parent 115.616162  0.01180297
105 parent 116.808081  0.01075310
106 parent 118.000000  0.00979661

$cost
function (P) 
{
    assign(&quot;calls&quot;, calls + 1, inherits = TRUE)
    if (trace_parms) 
        cat(P, &quot;\n&quot;)
    if (length(state.ini.optim) &gt; 0) {
        odeini &lt;- c(P[1:length(state.ini.optim)], state.ini.fixed)
        names(odeini) &lt;- c(state.ini.optim.boxnames, state.ini.fixed.boxnames)
    }
    else {
        odeini &lt;- state.ini.fixed
        names(odeini) &lt;- state.ini.fixed.boxnames
    }
    odeparms &lt;- c(P[(length(state.ini.optim) + 1):length(P)], 
        transparms.fixed)
    parms &lt;- backtransform_odeparms(odeparms, mkinmod, transform_rates = transform_rates, 
        transform_fractions = transform_fractions)
    out &lt;- mkinpredict(mkinmod, parms, odeini, outtimes, solution_type = solution_type, 
        use_compiled = use_compiled, method.ode = method.ode, 
        atol = atol, rtol = rtol, ...)
    assign(&quot;out_predicted&quot;, out, inherits = TRUE)
    mC &lt;- modCost(out, observed, y = &quot;value&quot;, err = err, weight = weight, 
        scaleVar = scaleVar)
    if (mC$model &lt; cost.old) {
        if (!quiet) 
            cat(&quot;Model cost at call &quot;, calls, &quot;: &quot;, mC$model, 
                &quot;\n&quot;)
        if (plot) {
            outtimes_plot = seq(min(observed$time), max(observed$time), 
                length.out = 100)
            out_plot &lt;- mkinpredict(mkinmod, parms, odeini, outtimes_plot, 
                solution_type = solution_type, use_compiled = use_compiled, 
                method.ode = method.ode, atol = atol, rtol = rtol, 
                ...)
            plot(0, type = &quot;n&quot;, xlim = range(observed$time), 
                ylim = c(0, max(observed$value, na.rm = TRUE)), 
                xlab = &quot;Time&quot;, ylab = &quot;Observed&quot;)
            col_obs &lt;- pch_obs &lt;- 1:length(obs_vars)
            lty_obs &lt;- rep(1, length(obs_vars))
            names(col_obs) &lt;- names(pch_obs) &lt;- names(lty_obs) &lt;- obs_vars
            for (obs_var in obs_vars) {
                points(subset(observed, name == obs_var, c(time, 
                  value)), pch = pch_obs[obs_var], col = col_obs[obs_var])
            }
            matlines(out_plot$time, out_plot[-1], col = col_obs, 
                lty = lty_obs)
            legend(&quot;topright&quot;, inset = c(0.05, 0.05), legend = obs_vars, 
                col = col_obs, pch = pch_obs, lty = 1:length(pch_obs))
        }
        assign(&quot;cost.old&quot;, mC$model, inherits = TRUE)
    }
    return(mC)
}
&lt;environment: 0x4023b58&gt;

$cost_notrans
function (P) 
{
    if (length(state.ini.optim) &gt; 0) {
        odeini &lt;- c(P[1:length(state.ini.optim)], state.ini.fixed)
        names(odeini) &lt;- c(state.ini.optim.boxnames, state.ini.fixed.boxnames)
    }
    else {
        odeini &lt;- state.ini.fixed
        names(odeini) &lt;- state.ini.fixed.boxnames
    }
    odeparms &lt;- c(P[(length(state.ini.optim) + 1):length(P)], 
        parms.fixed)
    out &lt;- mkinpredict(mkinmod, odeparms, odeini, outtimes, solution_type = solution_type, 
        use_compiled = use_compiled, method.ode = method.ode, 
        atol = atol, rtol = rtol, ...)
    mC &lt;- modCost(out, observed, y = &quot;value&quot;, err = err, weight = weight, 
        scaleVar = scaleVar)
    return(mC)
}
&lt;environment: 0x4023b58&gt;

$hessian_notrans
                  parent_0 k_parent_sink
parent_0          4.163631     -1203.894
k_parent_sink -1203.893702   1033188.753

$start
              value   type
parent_0      98.62  state
k_parent_sink  0.10 deparm

$start_transformed
                      value lower upper
parent_0          98.620000  -Inf   Inf
log_k_parent_sink -2.302585  -Inf   Inf

$fixed
[1] value type 
&lt;0 rows&gt; (or 0-length row.names)

$data
  time variable observed   predicted    residual
1    0   parent    98.62 99.17407218 -0.55407218
2    3   parent    81.43 78.44547872  2.98452128
3    7   parent    53.18 57.38445742 -4.20445742
4   14   parent    34.89 33.20400061  1.68599939
5   30   parent    10.09  9.50814643  0.58185357
6   62   parent     1.50  0.77966270  0.72033730
7   90   parent     0.33  0.08739595  0.24260405
8  118   parent     0.08  0.00979661  0.07020339

$atol
[1] 1e-08

$rtol
[1] 1e-10

$weight.ini
[1] &quot;none&quot;

$reweight.tol
[1] 1e-08

$reweight.max.iter
[1] 10

$bparms.optim
     parent_0 k_parent_sink 
  99.17407218    0.07815759 

$bparms.fixed
numeric(0)

$bparms.ode
k_parent_sink 
   0.07815759 

$bparms.state
  parent 
99.17407 

$date
[1] &quot;Wed Mar 23 18:10:36 2016&quot;

attr(,&quot;class&quot;)
[1] &quot;mkinfit&quot; &quot;modFit&quot; 
</div>
<div class='input'>  fits[&quot;SFO&quot;, &quot;B&quot;, drop = TRUE]
</div>
<div class='output'>[[1]]
$par
         parent_0 log_k_parent_sink 
        99.174072         -2.549028 

$ssr
[1] 30.65564

$convergence
[1] 0

$iterations
[1] 5

$evaluations
function gradient 
       8       15 

$counts
[1] &quot;relative convergence (4)&quot;

$hessian
                    parent_0 log_k_parent_sink
parent_0            4.163631         -94.09343
log_k_parent_sink -94.093431        6311.34610

$residuals
     parent      parent      parent      parent      parent      parent      parent      parent 
 0.55407218 -2.98452128  4.20445742 -1.68599939 -0.58185357 -0.72033730 -0.24260405 -0.07020339 

$ms
[1] 3.831956

$var_ms
  parent 
3.831956 

$var_ms_unscaled
  parent 
3.831956 

$var_ms_unweighted
  parent 
3.831956 

$rank
[1] 2

$df.residual
[1] 6

$solution_type
[1] &quot;analytical&quot;

$transform_rates
[1] TRUE

$transform_fractions
[1] TRUE

$method.modFit
[1] &quot;Port&quot;

$maxit.modFit
[1] &quot;auto&quot;

$calls
[1] 29

$time
   user  system elapsed 
  0.092   0.004   0.096 

$mkinmod
&lt;mkinmod&gt; model generated with
Use of formation fractions $use_of_ff: min 
Specification $spec:
$parent
$type: SFO; $sink: TRUE
Coefficient matrix $coefmat available

$observed
    name time value
1 parent    0 98.62
2 parent    3 81.43
3 parent    7 53.18
4 parent   14 34.89
5 parent   30 10.09
6 parent   62  1.50
7 parent   90  0.33
8 parent  118  0.08

$obs_vars
[1] &quot;parent&quot;

$predicted
      name       time       value
1   parent   0.000000 99.17407218
2   parent   1.191919 90.35253561
3   parent   2.383838 82.31567498
4   parent   3.000000 78.44547872
5   parent   3.575758 74.99369333
6   parent   4.767677 68.32300215
7   parent   5.959596 62.24566915
8   parent   7.000000 57.38445742
9   parent   7.151515 56.70891509
10  parent   8.343434 51.66465547
11  parent   9.535354 47.06908288
12  parent  10.727273 42.88228661
13  parent  11.919192 39.06790599
14  parent  13.111111 35.59281463
15  parent  14.000000 33.20400061
16  parent  14.303030 32.42683275
17  parent  15.494949 29.54246504
18  parent  16.686869 26.91466193
19  parent  17.878788 24.52060198
20  parent  19.070707 22.33949373
21  parent  20.262626 20.35239512
22  parent  21.454545 18.54204899
23  parent  22.646465 16.89273320
24  parent  23.838384 15.39012410
25  parent  25.030303 14.02117212
26  parent  26.222222 12.77398846
27  parent  27.414141 11.63774182
28  parent  28.606061 10.60256435
29  parent  29.797980  9.65946594
30  parent  30.000000  9.50814643
31  parent  30.989899  8.80025617
32  parent  32.181818  8.01747313
33  parent  33.373737  7.30431867
34  parent  34.565657  6.65459931
35  parent  35.757576  6.06267251
36  parent  36.949495  5.52339762
37  parent  38.141414  5.03209124
38  parent  39.333333  4.58448658
39  parent  40.525253  4.17669637
40  parent  41.717172  3.80517911
41  parent  42.909091  3.46670832
42  parent  44.101010  3.15834451
43  parent  45.292929  2.87740968
44  parent  46.484848  2.62146400
45  parent  47.676768  2.38828471
46  parent  48.868687  2.17584671
47  parent  50.060606  1.98230508
48  parent  51.252525  1.80597899
49  parent  52.444444  1.64533711
50  parent  53.636364  1.49898432
51  parent  54.828283  1.36564963
52  parent  56.020202  1.24417505
53  parent  57.212121  1.13350565
54  parent  58.404040  1.03268029
55  parent  59.595960  0.94082335
56  parent  60.787879  0.85713708
57  parent  61.979798  0.78089471
58  parent  62.000000  0.77966270
59  parent  63.171717  0.71143411
60  parent  64.363636  0.64815202
61  parent  65.555556  0.59049888
62  parent  66.747475  0.53797399
63  parent  67.939394  0.49012119
64  parent  69.131313  0.44652489
65  parent  70.323232  0.40680649
66  parent  71.515152  0.37062104
67  parent  72.707071  0.33765429
68  parent  73.898990  0.30761993
69  parent  75.090909  0.28025713
70  parent  76.282828  0.25532825
71  parent  77.474747  0.23261679
72  parent  78.666667  0.21192552
73  parent  79.858586  0.19307474
74  parent  81.050505  0.17590074
75  parent  82.242424  0.16025436
76  parent  83.434343  0.14599973
77  parent  84.626263  0.13301305
78  parent  85.818182  0.12118154
79  parent  87.010101  0.11040244
80  parent  88.202020  0.10058214
81  parent  89.393939  0.09163535
82  parent  90.000000  0.08739595
83  parent  90.585859  0.08348439
84  parent  91.777778  0.07605845
85  parent  92.969697  0.06929305
86  parent  94.161616  0.06312943
87  parent  95.353535  0.05751406
88  parent  96.545455  0.05239819
89  parent  97.737374  0.04773737
90  parent  98.929293  0.04349113
91  parent 100.121212  0.03962259
92  parent 101.313131  0.03609816
93  parent 102.505051  0.03288723
94  parent 103.696970  0.02996191
95  parent 104.888889  0.02729679
96  parent 106.080808  0.02486874
97  parent 107.272727  0.02265667
98  parent 108.464646  0.02064136
99  parent 109.656566  0.01880531
100 parent 110.848485  0.01713257
101 parent 112.040404  0.01560863
102 parent 113.232323  0.01422024
103 parent 114.424242  0.01295535
104 parent 115.616162  0.01180297
105 parent 116.808081  0.01075310
106 parent 118.000000  0.00979661

$cost
function (P) 
{
    assign(&quot;calls&quot;, calls + 1, inherits = TRUE)
    if (trace_parms) 
        cat(P, &quot;\n&quot;)
    if (length(state.ini.optim) &gt; 0) {
        odeini &lt;- c(P[1:length(state.ini.optim)], state.ini.fixed)
        names(odeini) &lt;- c(state.ini.optim.boxnames, state.ini.fixed.boxnames)
    }
    else {
        odeini &lt;- state.ini.fixed
        names(odeini) &lt;- state.ini.fixed.boxnames
    }
    odeparms &lt;- c(P[(length(state.ini.optim) + 1):length(P)], 
        transparms.fixed)
    parms &lt;- backtransform_odeparms(odeparms, mkinmod, transform_rates = transform_rates, 
        transform_fractions = transform_fractions)
    out &lt;- mkinpredict(mkinmod, parms, odeini, outtimes, solution_type = solution_type, 
        use_compiled = use_compiled, method.ode = method.ode, 
        atol = atol, rtol = rtol, ...)
    assign(&quot;out_predicted&quot;, out, inherits = TRUE)
    mC &lt;- modCost(out, observed, y = &quot;value&quot;, err = err, weight = weight, 
        scaleVar = scaleVar)
    if (mC$model &lt; cost.old) {
        if (!quiet) 
            cat(&quot;Model cost at call &quot;, calls, &quot;: &quot;, mC$model, 
                &quot;\n&quot;)
        if (plot) {
            outtimes_plot = seq(min(observed$time), max(observed$time), 
                length.out = 100)
            out_plot &lt;- mkinpredict(mkinmod, parms, odeini, outtimes_plot, 
                solution_type = solution_type, use_compiled = use_compiled, 
                method.ode = method.ode, atol = atol, rtol = rtol, 
                ...)
            plot(0, type = &quot;n&quot;, xlim = range(observed$time), 
                ylim = c(0, max(observed$value, na.rm = TRUE)), 
                xlab = &quot;Time&quot;, ylab = &quot;Observed&quot;)
            col_obs &lt;- pch_obs &lt;- 1:length(obs_vars)
            lty_obs &lt;- rep(1, length(obs_vars))
            names(col_obs) &lt;- names(pch_obs) &lt;- names(lty_obs) &lt;- obs_vars
            for (obs_var in obs_vars) {
                points(subset(observed, name == obs_var, c(time, 
                  value)), pch = pch_obs[obs_var], col = col_obs[obs_var])
            }
            matlines(out_plot$time, out_plot[-1], col = col_obs, 
                lty = lty_obs)
            legend(&quot;topright&quot;, inset = c(0.05, 0.05), legend = obs_vars, 
                col = col_obs, pch = pch_obs, lty = 1:length(pch_obs))
        }
        assign(&quot;cost.old&quot;, mC$model, inherits = TRUE)
    }
    return(mC)
}
&lt;environment: 0x4023b58&gt;

$cost_notrans
function (P) 
{
    if (length(state.ini.optim) &gt; 0) {
        odeini &lt;- c(P[1:length(state.ini.optim)], state.ini.fixed)
        names(odeini) &lt;- c(state.ini.optim.boxnames, state.ini.fixed.boxnames)
    }
    else {
        odeini &lt;- state.ini.fixed
        names(odeini) &lt;- state.ini.fixed.boxnames
    }
    odeparms &lt;- c(P[(length(state.ini.optim) + 1):length(P)], 
        parms.fixed)
    out &lt;- mkinpredict(mkinmod, odeparms, odeini, outtimes, solution_type = solution_type, 
        use_compiled = use_compiled, method.ode = method.ode, 
        atol = atol, rtol = rtol, ...)
    mC &lt;- modCost(out, observed, y = &quot;value&quot;, err = err, weight = weight, 
        scaleVar = scaleVar)
    return(mC)
}
&lt;environment: 0x4023b58&gt;

$hessian_notrans
                  parent_0 k_parent_sink
parent_0          4.163631     -1203.894
k_parent_sink -1203.893702   1033188.753

$start
              value   type
parent_0      98.62  state
k_parent_sink  0.10 deparm

$start_transformed
                      value lower upper
parent_0          98.620000  -Inf   Inf
log_k_parent_sink -2.302585  -Inf   Inf

$fixed
[1] value type 
&lt;0 rows&gt; (or 0-length row.names)

$data
  time variable observed   predicted    residual
1    0   parent    98.62 99.17407218 -0.55407218
2    3   parent    81.43 78.44547872  2.98452128
3    7   parent    53.18 57.38445742 -4.20445742
4   14   parent    34.89 33.20400061  1.68599939
5   30   parent    10.09  9.50814643  0.58185357
6   62   parent     1.50  0.77966270  0.72033730
7   90   parent     0.33  0.08739595  0.24260405
8  118   parent     0.08  0.00979661  0.07020339

$atol
[1] 1e-08

$rtol
[1] 1e-10

$weight.ini
[1] &quot;none&quot;

$reweight.tol
[1] 1e-08

$reweight.max.iter
[1] 10

$bparms.optim
     parent_0 k_parent_sink 
  99.17407218    0.07815759 

$bparms.fixed
numeric(0)

$bparms.ode
k_parent_sink 
   0.07815759 

$bparms.state
  parent 
99.17407 

$date
[1] &quot;Wed Mar 23 18:10:36 2016&quot;

attr(,&quot;class&quot;)
[1] &quot;mkinfit&quot; &quot;modFit&quot; 

</div></pre>
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    <!-- <ul>
      <li>[.mmkin</li>
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    <h2>Author</h2>
    
  Johannes Ranke

    
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