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<meta property="og:title" content="Summary method for class "nlme.mmkin" — summary.nlme.mmkin" />
<meta property="og:description" content="Lists model equations, initial parameter values, optimised parameters
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population mean) and residual error model, as well as the resulting
endpoints such as formation fractions and DT50 values. Optionally
(default is FALSE), the data are listed in full." />




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    <h1>Summary method for class "nlme.mmkin"</h1>
    <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/summary.nlme.mmkin.R'><code>R/summary.nlme.mmkin.R</code></a></small>
    <div class="hidden name"><code>summary.nlme.mmkin.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Lists model equations, initial parameter values, optimised parameters
for fixed effects (population), random effects (deviations from the
population mean) and residual error model, as well as the resulting
endpoints such as formation fractions and DT50 values. Optionally
(default is FALSE), the data are listed in full.</p>
    </div>

    <pre class="usage"><span class='co'># S3 method for nlme.mmkin</span>
<span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span>
  <span class='va'>object</span>,
  data <span class='op'>=</span> <span class='cn'>FALSE</span>,
  verbose <span class='op'>=</span> <span class='cn'>FALSE</span>,
  distimes <span class='op'>=</span> <span class='cn'>TRUE</span>,
  alpha <span class='op'>=</span> <span class='fl'>0.05</span>,
  <span class='va'>...</span>
<span class='op'>)</span>

<span class='co'># S3 method for summary.nlme.mmkin</span>
<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>x</span>, digits <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/Extremes.html'>max</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fu'><a href='https://rdrr.io/r/base/options.html'>getOption</a></span><span class='op'>(</span><span class='st'>"digits"</span><span class='op'>)</span> <span class='op'>-</span> <span class='fl'>3</span><span class='op'>)</span>, verbose <span class='op'>=</span> <span class='va'>x</span><span class='op'>$</span><span class='va'>verbose</span>, <span class='va'>...</span><span class='op'>)</span></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>object</th>
      <td><p>an object of class <a href='nlme.mmkin.html'>nlme.mmkin</a></p></td>
    </tr>
    <tr>
      <th>data</th>
      <td><p>logical, indicating whether the full data should be included in
the summary.</p></td>
    </tr>
    <tr>
      <th>verbose</th>
      <td><p>Should the summary be verbose?</p></td>
    </tr>
    <tr>
      <th>distimes</th>
      <td><p>logical, indicating whether DT50 and DT90 values should be
included.</p></td>
    </tr>
    <tr>
      <th>alpha</th>
      <td><p>error level for confidence interval estimation from the t
distribution</p></td>
    </tr>
    <tr>
      <th>...</th>
      <td><p>optional arguments passed to methods like <code>print</code>.</p></td>
    </tr>
    <tr>
      <th>x</th>
      <td><p>an object of class summary.nlme.mmkin</p></td>
    </tr>
    <tr>
      <th>digits</th>
      <td><p>Number of digits to use for printing</p></td>
    </tr>
    </table>

    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>

    <p>The summary function returns a list based on the <a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a> object
obtained in the fit, with at least the following additional components</p>
<dt>nlmeversion, mkinversion, Rversion</dt><dd><p>The nlme, mkin and R versions used</p></dd>
<dt>date.fit, date.summary</dt><dd><p>The dates where the fit and the summary were
produced</p></dd>
<dt>diffs</dt><dd><p>The differential equations used in the degradation model</p></dd>
<dt>use_of_ff</dt><dd><p>Was maximum or minimum use made of formation fractions</p></dd>
<dt>data</dt><dd><p>The data</p></dd>
<dt>confint_trans</dt><dd><p>Transformed parameters as used in the optimisation, with confidence intervals</p></dd>
<dt>confint_back</dt><dd><p>Backtransformed parameters, with confidence intervals if available</p></dd>
<dt>ff</dt><dd><p>The estimated formation fractions derived from the fitted
model.</p></dd>
<dt>distimes</dt><dd><p>The DT50 and DT90 values for each observed variable.</p></dd>
<dt>SFORB</dt><dd><p>If applicable, eigenvalues of SFORB components of the model.</p></dd>
The print method is called for its side effect, i.e. printing the summary.

    <h2 class="hasAnchor" id="author"><a class="anchor" href="#author"></a>Author</h2>

    <p>Johannes Ranke for the mkin specific parts
José Pinheiro and Douglas Bates for the components inherited from nlme</p>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'>
<span class='co'># Generate five datasets following SFO kinetics</span>
<span class='va'>sampling_times</span> <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>3</span>, <span class='fl'>7</span>, <span class='fl'>14</span>, <span class='fl'>28</span>, <span class='fl'>60</span>, <span class='fl'>90</span>, <span class='fl'>120</span><span class='op'>)</span>
<span class='va'>dt50_sfo_in_pop</span> <span class='op'>&lt;-</span> <span class='fl'>50</span>
<span class='va'>k_in_pop</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/Log.html'>log</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span> <span class='op'>/</span> <span class='va'>dt50_sfo_in_pop</span>
<span class='fu'><a href='https://rdrr.io/r/base/Random.html'>set.seed</a></span><span class='op'>(</span><span class='fl'>1234</span><span class='op'>)</span>
<span class='va'>k_in</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/stats/Lognormal.html'>rlnorm</a></span><span class='op'>(</span><span class='fl'>5</span>, <span class='fu'><a href='https://rdrr.io/r/base/Log.html'>log</a></span><span class='op'>(</span><span class='va'>k_in_pop</span><span class='op'>)</span>, <span class='fl'>0.5</span><span class='op'>)</span>
<span class='va'>SFO</span> <span class='op'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span><span class='op'>)</span>

<span class='va'>pred_sfo</span> <span class='op'>&lt;-</span> <span class='kw'>function</span><span class='op'>(</span><span class='va'>k</span><span class='op'>)</span> <span class='op'>{</span>
  <span class='fu'><a href='mkinpredict.html'>mkinpredict</a></span><span class='op'>(</span><span class='va'>SFO</span>,
    <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>k_parent <span class='op'>=</span> <span class='va'>k</span><span class='op'>)</span>,
    <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fl'>100</span><span class='op'>)</span>,
    <span class='va'>sampling_times</span><span class='op'>)</span>
<span class='op'>}</span>

<span class='va'>ds_sfo_mean</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='va'>k_in</span>, <span class='va'>pred_sfo</span><span class='op'>)</span>
<span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span><span class='op'>(</span><span class='va'>ds_sfo_mean</span><span class='op'>)</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste</a></span><span class='op'>(</span><span class='st'>"ds"</span>, <span class='fl'>1</span><span class='op'>:</span><span class='fl'>5</span><span class='op'>)</span>

<span class='fu'><a href='https://rdrr.io/r/base/Random.html'>set.seed</a></span><span class='op'>(</span><span class='fl'>12345</span><span class='op'>)</span>
<span class='va'>ds_sfo_syn</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='va'>ds_sfo_mean</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>ds</span><span class='op'>)</span> <span class='op'>{</span>
  <span class='fu'><a href='add_err.html'>add_err</a></span><span class='op'>(</span><span class='va'>ds</span>,
    sdfunc <span class='op'>=</span> <span class='kw'>function</span><span class='op'>(</span><span class='va'>value</span><span class='op'>)</span> <span class='fu'><a href='https://rdrr.io/r/base/MathFun.html'>sqrt</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>^</span><span class='fl'>2</span> <span class='op'>+</span> <span class='va'>value</span><span class='op'>^</span><span class='fl'>2</span> <span class='op'>*</span> <span class='fl'>0.07</span><span class='op'>^</span><span class='fl'>2</span><span class='op'>)</span>,
    n <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span>
<span class='op'>}</span><span class='op'>)</span>

<span class='co'># Evaluate using mmkin and nlme</span>
<span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://svn.r-project.org/R-packages/trunk/nlme/'>nlme</a></span><span class='op'>)</span>
<span class='va'>f_mmkin</span> <span class='op'>&lt;-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='va'>ds_sfo_syn</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span>, cores <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span>
</div><div class='output co'>#&gt; <span class='warning'>Warning: Optimisation did not converge:</span>
#&gt; <span class='warning'>iteration limit reached without convergence (10)</span></div><div class='input'><span class='va'>f_nlme</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_mmkin</span><span class='op'>)</span>
</div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 4, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>f_nlme</span>, data <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span>
</div><div class='output co'>#&gt; nlme version used for fitting:      3.1.152 
#&gt; mkin version used for pre-fitting:  1.0.4 
#&gt; R version used for fitting:         4.0.4 
#&gt; Date of fit:     Wed Mar 31 19:18:24 2021 
#&gt; Date of summary: Wed Mar 31 19:18:24 2021 
#&gt; 
#&gt; Equations:
#&gt; d_parent/dt = - k_parent * parent
#&gt; 
#&gt; Data:
#&gt; 90 observations of 1 variable(s) grouped in 5 datasets
#&gt; 
#&gt; Model predictions using solution type analytical 
#&gt; 
#&gt; Fitted in 0.537 s using 4 iterations
#&gt; 
#&gt; Variance model: Two-component variance function 
#&gt; 
#&gt; Mean of starting values for individual parameters:
#&gt;     parent_0 log_k_parent 
#&gt;      101.569       -4.454 
#&gt; 
#&gt; Fixed degradation parameter values:
#&gt; None
#&gt; 
#&gt; Results:
#&gt; 
#&gt;     AIC   BIC logLik
#&gt;   584.5 599.5 -286.2
#&gt; 
#&gt; Optimised, transformed parameters with symmetric confidence intervals:
#&gt;               lower    est.   upper
#&gt; parent_0     99.371 101.592 103.814
#&gt; log_k_parent -4.973  -4.449  -3.926
#&gt; 
#&gt; Correlation: 
#&gt;              prnt_0
#&gt; log_k_parent 0.051 
#&gt; 
#&gt; Random effects:
#&gt;  Formula: list(parent_0 ~ 1, log_k_parent ~ 1)
#&gt;  Level: ds
#&gt;  Structure: Diagonal
#&gt;          parent_0 log_k_parent Residual
#&gt; StdDev: 6.924e-05       0.5863        1
#&gt; 
#&gt; Variance function:
#&gt;  Structure: Constant plus proportion of variance covariate
#&gt;  Formula: ~fitted(.) 
#&gt;  Parameter estimates:
#&gt;        const         prop 
#&gt; 0.0001208853 0.0789968036 
#&gt; 
#&gt; Backtransformed parameters with asymmetric confidence intervals:
#&gt;              lower      est.     upper
#&gt; parent_0 99.370882 101.59243 103.81398
#&gt; k_parent  0.006923   0.01168   0.01972
#&gt; 
#&gt; Estimated disappearance times:
#&gt;         DT50  DT90
#&gt; parent 59.32 197.1
#&gt; 
#&gt; Data:
#&gt;    ds   name time observed predicted  residual    std standardized
#&gt;  ds 1 parent    0    104.1   101.592   2.50757 8.0255     0.312451
#&gt;  ds 1 parent    0    105.0   101.592   3.40757 8.0255     0.424594
#&gt;  ds 1 parent    1     98.5   100.796  -2.29571 7.9625    -0.288313
#&gt;  ds 1 parent    1     96.1   100.796  -4.69571 7.9625    -0.589725
#&gt;  ds 1 parent    3    101.9    99.221   2.67904 7.8381     0.341796
#&gt;  ds 1 parent    3     85.2    99.221 -14.02096 7.8381    -1.788812
#&gt;  ds 1 parent    7     99.1    96.145   2.95512 7.5951     0.389081
#&gt;  ds 1 parent    7     93.0    96.145  -3.14488 7.5951    -0.414065
#&gt;  ds 1 parent   14     88.1    90.989  -2.88944 7.1879    -0.401987
#&gt;  ds 1 parent   14     84.1    90.989  -6.88944 7.1879    -0.958480
#&gt;  ds 1 parent   28     80.2    81.493  -1.29305 6.4377    -0.200857
#&gt;  ds 1 parent   28     91.3    81.493   9.80695 6.4377     1.523364
#&gt;  ds 1 parent   60     65.1    63.344   1.75642 5.0039     0.351008
#&gt;  ds 1 parent   60     65.8    63.344   2.45642 5.0039     0.490898
#&gt;  ds 1 parent   90     47.8    50.018  -2.21764 3.9512    -0.561252
#&gt;  ds 1 parent   90     53.5    50.018   3.48236 3.9512     0.881335
#&gt;  ds 1 parent  120     37.6    39.495  -1.89515 3.1200    -0.607423
#&gt;  ds 1 parent  120     39.3    39.495  -0.19515 3.1200    -0.062549
#&gt;  ds 2 parent    0    107.9   101.592   6.30757 8.0255     0.785943
#&gt;  ds 2 parent    0    102.1   101.592   0.50757 8.0255     0.063245
#&gt;  ds 2 parent    1    103.8   100.058   3.74159 7.9043     0.473361
#&gt;  ds 2 parent    1    108.6   100.058   8.54159 7.9043     1.080626
#&gt;  ds 2 parent    3     91.0    97.060  -6.05952 7.6674    -0.790297
#&gt;  ds 2 parent    3     84.9    97.060 -12.15952 7.6674    -1.585874
#&gt;  ds 2 parent    7     79.3    91.329 -12.02867 7.2147    -1.667251
#&gt;  ds 2 parent    7    100.9    91.329   9.57133 7.2147     1.326647
#&gt;  ds 2 parent   14     77.3    82.102  -4.80185 6.4858    -0.740366
#&gt;  ds 2 parent   14     83.5    82.102   1.39815 6.4858     0.215571
#&gt;  ds 2 parent   28     66.8    66.351   0.44945 5.2415     0.085748
#&gt;  ds 2 parent   28     63.3    66.351  -3.05055 5.2415    -0.582002
#&gt;  ds 2 parent   60     40.8    40.775   0.02474 3.2211     0.007679
#&gt;  ds 2 parent   60     44.8    40.775   4.02474 3.2211     1.249485
#&gt;  ds 2 parent   90     27.8    25.832   1.96762 2.0407     0.964198
#&gt;  ds 2 parent   90     27.0    25.832   1.16762 2.0407     0.572171
#&gt;  ds 2 parent  120     15.2    16.366  -1.16561 1.2928    -0.901595
#&gt;  ds 2 parent  120     15.5    16.366  -0.86561 1.2928    -0.669547
#&gt;  ds 3 parent    0     97.7   101.592  -3.89243 8.0255    -0.485009
#&gt;  ds 3 parent    0     88.2   101.592 -13.39243 8.0255    -1.668739
#&gt;  ds 3 parent    1    109.9    99.218  10.68196 7.8379     1.362858
#&gt;  ds 3 parent    1     97.8    99.218  -1.41804 7.8379    -0.180921
#&gt;  ds 3 parent    3    100.5    94.634   5.86555 7.4758     0.784603
#&gt;  ds 3 parent    3     77.4    94.634 -17.23445 7.4758    -2.305360
#&gt;  ds 3 parent    7     78.3    86.093  -7.79273 6.8011    -1.145813
#&gt;  ds 3 parent    7     90.3    86.093   4.20727 6.8011     0.618620
#&gt;  ds 3 parent   14     76.0    72.958   3.04222 5.7634     0.527848
#&gt;  ds 3 parent   14     79.1    72.958   6.14222 5.7634     1.065722
#&gt;  ds 3 parent   28     46.0    52.394  -6.39404 4.1390    -1.544842
#&gt;  ds 3 parent   28     53.4    52.394   1.00596 4.1390     0.243046
#&gt;  ds 3 parent   60     25.1    24.582   0.51786 1.9419     0.266676
#&gt;  ds 3 parent   60     21.4    24.582  -3.18214 1.9419    -1.638664
#&gt;  ds 3 parent   90     11.0    12.092  -1.09202 0.9552    -1.143199
#&gt;  ds 3 parent   90     14.2    12.092   2.10798 0.9552     2.206776
#&gt;  ds 3 parent  120      5.8     5.948  -0.14810 0.4699    -0.315178
#&gt;  ds 3 parent  120      6.1     5.948   0.15190 0.4699     0.323282
#&gt;  ds 4 parent    0     95.3   101.592  -6.29243 8.0255    -0.784057
#&gt;  ds 4 parent    0    102.0   101.592   0.40757 8.0255     0.050784
#&gt;  ds 4 parent    1    104.4   101.125   3.27549 7.9885     0.410025
#&gt;  ds 4 parent    1    105.4   101.125   4.27549 7.9885     0.535205
#&gt;  ds 4 parent    3    113.7   100.195  13.50487 7.9151     1.706218
#&gt;  ds 4 parent    3     82.3   100.195 -17.89513 7.9151    -2.260886
#&gt;  ds 4 parent    7     98.1    98.362  -0.26190 7.7703    -0.033706
#&gt;  ds 4 parent    7     87.8    98.362 -10.56190 7.7703    -1.359270
#&gt;  ds 4 parent   14     97.9    95.234   2.66590 7.5232     0.354357
#&gt;  ds 4 parent   14    104.8    95.234   9.56590 7.5232     1.271521
#&gt;  ds 4 parent   28     85.0    89.274  -4.27372 7.0523    -0.606001
#&gt;  ds 4 parent   28     77.2    89.274 -12.07372 7.0523    -1.712017
#&gt;  ds 4 parent   60     82.2    77.013   5.18661 6.0838     0.852526
#&gt;  ds 4 parent   60     86.1    77.013   9.08661 6.0838     1.493571
#&gt;  ds 4 parent   90     70.5    67.053   3.44692 5.2970     0.650733
#&gt;  ds 4 parent   90     61.7    67.053  -5.35308 5.2970    -1.010591
#&gt;  ds 4 parent  120     60.0    58.381   1.61905 4.6119     0.351058
#&gt;  ds 4 parent  120     56.4    58.381  -1.98095 4.6119    -0.429530
#&gt;  ds 5 parent    0     92.6   101.592  -8.99243 8.0255    -1.120485
#&gt;  ds 5 parent    0    116.5   101.592  14.90757 8.0255     1.857531
#&gt;  ds 5 parent    1    108.0    99.914   8.08560 7.8929     1.024413
#&gt;  ds 5 parent    1    104.9    99.914   4.98560 7.8929     0.631655
#&gt;  ds 5 parent    3    100.5    96.641   3.85898 7.6343     0.505477
#&gt;  ds 5 parent    3     89.5    96.641  -7.14102 7.6343    -0.935382
#&gt;  ds 5 parent    7     91.7    90.412   1.28752 7.1423     0.180267
#&gt;  ds 5 parent    7     95.1    90.412   4.68752 7.1423     0.656304
#&gt;  ds 5 parent   14     82.2    80.463   1.73715 6.3563     0.273295
#&gt;  ds 5 parent   14     84.5    80.463   4.03715 6.3563     0.635141
#&gt;  ds 5 parent   28     60.5    63.728  -3.22788 5.0343    -0.641178
#&gt;  ds 5 parent   28     72.8    63.728   9.07212 5.0343     1.802062
#&gt;  ds 5 parent   60     38.3    37.399   0.90061 2.9544     0.304835
#&gt;  ds 5 parent   60     40.7    37.399   3.30061 2.9544     1.117174
#&gt;  ds 5 parent   90     22.5    22.692  -0.19165 1.7926    -0.106913
#&gt;  ds 5 parent   90     20.8    22.692  -1.89165 1.7926    -1.055273
#&gt;  ds 5 parent  120     13.4    13.768  -0.36790 1.0876    -0.338259
#&gt;  ds 5 parent  120     13.8    13.768   0.03210 1.0876     0.029517</div><div class='input'>
</div></pre>
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