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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
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." />


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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/pkg/saemix/man/summary-methods.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='mkinsub.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='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>
<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 3, 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/pkg/saemix/man/summary-methods.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:      3.1.150.1 
#&gt; mkin version used for pre-fitting:  0.9.50.4 
#&gt; R version used for fitting:         4.0.3 
#&gt; Date of fit:     Thu Nov 19 14:59:43 2020 
#&gt; Date of summary: Thu Nov 19 14:59:43 2020 
#&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 0.681 s using 5 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;    97.849556    -4.455036 
#&gt; 
#&gt; Fixed degradation parameter values:
#&gt; None
#&gt; 
#&gt; Results:
#&gt; 
#&gt;       AIC      BIC   logLik
#&gt;   555.792 570.7908 -271.896
#&gt; 
#&gt; Optimised, transformed parameters with symmetric confidence intervals:
#&gt;               lower   est.   upper
#&gt; parent_0     94.701 97.763 100.826
#&gt; log_k_parent -5.008 -4.462  -3.916
#&gt; 
#&gt; Correlation: 
#&gt;              prnt_0
#&gt; log_k_parent 0.024 
#&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:    2.898       0.6119        1
#&gt; 
#&gt; Variance function:
#&gt;  Structure: Constant plus proportion of variance covariate
#&gt;  Formula: ~fitted(.) 
#&gt;  Parameter estimates:
#&gt;      const       prop 
#&gt; 1.55075176 0.05680853 
#&gt; 
#&gt; Backtransformed parameters with asymmetric confidence intervals:
#&gt;              lower     est.     upper
#&gt; parent_0 94.701336 97.76345 100.82556
#&gt; k_parent  0.006687  0.01154   0.01992
#&gt; 
#&gt; Estimated disappearance times:
#&gt;         DT50  DT90
#&gt; parent 60.05 199.5
#&gt; 
#&gt; Data:
#&gt;    ds   name time observed predicted residual   std standardized
#&gt;  ds 1 parent    0    103.6     97.42  6.17540 5.748     1.074413
#&gt;  ds 1 parent    0     95.9     97.42 -1.52460 5.748    -0.265253
#&gt;  ds 1 parent    1     95.4     96.72 -1.31719 5.709    -0.230721
#&gt;  ds 1 parent    1     95.3     96.72 -1.41719 5.709    -0.248237
#&gt;  ds 1 parent    3     91.6     95.32 -3.71774 5.633    -0.660046
#&gt;  ds 1 parent    3     94.5     95.32 -0.81774 5.633    -0.145181
#&gt;  ds 1 parent    7     88.1     92.58 -4.47930 5.483    -0.816920
#&gt;  ds 1 parent    7     89.6     92.58 -2.97930 5.483    -0.543355
#&gt;  ds 1 parent   14     90.3     87.97  2.32502 5.233     0.444318
#&gt;  ds 1 parent   14     96.0     87.97  8.02502 5.233     1.533602
#&gt;  ds 1 parent   28     80.2     79.44  0.75809 4.772     0.158862
#&gt;  ds 1 parent   28     77.9     79.44 -1.54191 4.772    -0.323118
#&gt;  ds 1 parent   60     59.3     62.92 -3.61742 3.896    -0.928458
#&gt;  ds 1 parent   60     59.6     62.92 -3.31742 3.896    -0.851459
#&gt;  ds 1 parent   90     59.4     50.56  8.83825 3.264     2.707613
#&gt;  ds 1 parent   90     51.0     50.56  0.43825 3.264     0.134260
#&gt;  ds 1 parent  120     38.8     40.63 -1.83247 2.781    -0.658968
#&gt;  ds 1 parent  120     38.9     40.63 -1.73247 2.781    -0.623007
#&gt;  ds 2 parent    0    103.2     97.17  6.02995 5.734     1.051655
#&gt;  ds 2 parent    0     95.1     97.17 -2.07005 5.734    -0.361027
#&gt;  ds 2 parent    1     88.3     95.59 -7.28901 5.647    -1.290694
#&gt;  ds 2 parent    1    102.4     95.59  6.81099 5.647     1.206048
#&gt;  ds 2 parent    3     88.4     92.50 -4.10371 5.479    -0.748984
#&gt;  ds 2 parent    3     95.2     92.50  2.69629 5.479     0.492110
#&gt;  ds 2 parent    7     83.5     86.63 -3.12863 5.160    -0.606349
#&gt;  ds 2 parent    7     96.4     86.63  9.77137 5.160     1.893751
#&gt;  ds 2 parent   14     77.3     77.23  0.06920 4.653     0.014871
#&gt;  ds 2 parent   14     76.0     77.23 -1.23080 4.653    -0.264497
#&gt;  ds 2 parent   28     61.7     61.38  0.31692 3.816     0.083043
#&gt;  ds 2 parent   28     56.5     61.38 -4.88308 3.816    -1.279513
#&gt;  ds 2 parent   60     35.1     36.31 -1.21343 2.581    -0.470178
#&gt;  ds 2 parent   60     32.2     36.31 -4.11343 2.581    -1.593868
#&gt;  ds 2 parent   90     21.2     22.20 -0.99906 1.999    -0.499832
#&gt;  ds 2 parent   90     23.3     22.20  1.10094 1.999     0.550800
#&gt;  ds 2 parent  120     14.1     13.57  0.52931 1.732     0.305638
#&gt;  ds 2 parent  120     16.9     13.57  3.32931 1.732     1.922443
#&gt;  ds 3 parent    0     92.4     94.12 -1.71979 5.567    -0.308917
#&gt;  ds 3 parent    0     94.0     94.12 -0.11979 5.567    -0.021517
#&gt;  ds 3 parent    1     95.7     91.97  3.72634 5.450     0.683712
#&gt;  ds 3 parent    1     90.8     91.97 -1.17366 5.450    -0.215343
#&gt;  ds 3 parent    3     86.7     87.83 -1.12709 5.225    -0.215720
#&gt;  ds 3 parent    3     85.8     87.83 -2.02709 5.225    -0.387976
#&gt;  ds 3 parent    7     77.1     80.09 -2.98635 4.807    -0.621300
#&gt;  ds 3 parent    7     81.5     80.09  1.41365 4.807     0.294104
#&gt;  ds 3 parent   14     69.1     68.15  0.95467 4.170     0.228922
#&gt;  ds 3 parent   14     62.4     68.15 -5.74533 4.170    -1.377682
#&gt;  ds 3 parent   28     49.1     49.34 -0.23911 3.203    -0.074644
#&gt;  ds 3 parent   28     47.2     49.34 -2.13911 3.203    -0.667787
#&gt;  ds 3 parent   60     21.9     23.58 -1.68477 2.049    -0.822090
#&gt;  ds 3 parent   60     23.6     23.58  0.01523 2.049     0.007431
#&gt;  ds 3 parent   90     12.4     11.81  0.59388 1.690     0.351500
#&gt;  ds 3 parent   90     13.8     11.81  1.99388 1.690     1.180112
#&gt;  ds 3 parent  120      4.9      5.91 -1.00993 1.587    -0.636506
#&gt;  ds 3 parent  120      7.5      5.91  1.59007 1.587     1.002137
#&gt;  ds 4 parent    0     91.8    101.72 -9.92097 5.983    -1.658171
#&gt;  ds 4 parent    0    104.6    101.72  2.87903 5.983     0.481194
#&gt;  ds 4 parent    1    117.5    101.27 16.23017 5.958     2.723944
#&gt;  ds 4 parent    1     99.3    101.27 -1.96983 5.958    -0.330602
#&gt;  ds 4 parent    3     94.0    100.37 -6.37355 5.909    -1.078583
#&gt;  ds 4 parent    3     98.7    100.37 -1.67355 5.909    -0.283212
#&gt;  ds 4 parent    7    109.2     98.60 10.59529 5.812     1.822915
#&gt;  ds 4 parent    7     89.2     98.60 -9.40471 5.812    -1.618075
#&gt;  ds 4 parent   14    103.3     95.58  7.71609 5.647     1.366386
#&gt;  ds 4 parent   14    103.0     95.58  7.41609 5.647     1.313261
#&gt;  ds 4 parent   28     90.8     89.82  0.98290 5.333     0.184310
#&gt;  ds 4 parent   28     88.7     89.82 -1.11710 5.333    -0.209477
#&gt;  ds 4 parent   60     74.8     77.91 -3.10870 4.690    -0.662879
#&gt;  ds 4 parent   60     75.3     77.91 -2.60870 4.690    -0.556262
#&gt;  ds 4 parent   90     71.1     68.18  2.91738 4.172     0.699234
#&gt;  ds 4 parent   90     78.0     68.18  9.81738 4.172     2.353017
#&gt;  ds 4 parent  120     59.1     59.67 -0.57073 3.728    -0.153107
#&gt;  ds 4 parent  120     53.8     59.67 -5.87073 3.728    -1.574902
#&gt;  ds 5 parent    0     94.9     98.38 -3.48183 5.800    -0.600307
#&gt;  ds 5 parent    0    101.8     98.38  3.41817 5.800     0.589332
#&gt;  ds 5 parent    1     96.1     96.75 -0.65141 5.711    -0.114065
#&gt;  ds 5 parent    1     97.1     96.75  0.34859 5.711     0.061040
#&gt;  ds 5 parent    3     93.8     93.57  0.22881 5.537     0.041323
#&gt;  ds 5 parent    3     85.8     93.57 -7.77119 5.537    -1.403444
#&gt;  ds 5 parent    7     87.6     87.52  0.07909 5.208     0.015186
#&gt;  ds 5 parent    7     94.0     87.52  6.47909 5.208     1.244026
#&gt;  ds 5 parent   14     82.5     77.86  4.64101 4.687     0.990182
#&gt;  ds 5 parent   14     81.7     77.86  3.84101 4.687     0.819498
#&gt;  ds 5 parent   28     60.0     61.62 -1.61729 3.829    -0.422433
#&gt;  ds 5 parent   28     61.0     61.62 -0.61729 3.829    -0.161236
#&gt;  ds 5 parent   60     32.5     36.10 -3.59608 2.571    -1.398750
#&gt;  ds 5 parent   60     35.5     36.10 -0.59608 2.571    -0.231854
#&gt;  ds 5 parent   90     21.8     21.86 -0.06415 1.987    -0.032287
#&gt;  ds 5 parent   90     24.4     21.86  2.53585 1.987     1.276317
#&gt;  ds 5 parent  120     14.1     13.24  0.85643 1.724     0.496877
#&gt;  ds 5 parent  120     12.1     13.24 -1.14357 1.724    -0.663473</div><div class='input'>
</div></pre>
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