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<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/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'><-</span> <span class='fl'>50</span>
<span class='va'>k_in_pop</span> <span class='op'><-</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'><-</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'><-</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'><-</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'><-</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'><-</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'><-</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'><-</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'>#> <span class='warning'>Warning: Optimisation did not converge:</span>
#> <span class='warning'>iteration limit reached without convergence (10)</span></div><div class='input'><span class='va'>f_nlme</span> <span class='op'><-</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'>#> <span class='warning'>Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</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'>#> nlme version used for fitting: 3.1.150.1
#> mkin version used for pre-fitting: 0.9.50.4
#> R version used for fitting: 4.0.3
#> Date of fit: Mon Nov 30 16:01:23 2020
#> Date of summary: Mon Nov 30 16:01:23 2020
#>
#> Equations:
#> d_parent/dt = - k_parent * parent
#>
#> Data:
#> 90 observations of 1 variable(s) grouped in 5 datasets
#>
#> Model predictions using solution type analytical
#>
#> Fitted in 0.996 s using 6 iterations
#>
#> Variance model: Two-component variance function
#>
#> Mean of starting values for individual parameters:
#> parent_0 log_k_parent
#> 101.568773 -4.454103
#>
#> Fixed degradation parameter values:
#> None
#>
#> Results:
#>
#> AIC BIC logLik
#> 586.4159 603.9145 -286.2079
#>
#> Optimised, transformed parameters with symmetric confidence intervals:
#> lower est. upper
#> parent_0 99.360 101.593 103.825
#> log_k_parent -4.979 -4.451 -3.924
#>
#> Correlation:
#> prnt_0
#> log_k_parent 0.155
#>
#> Random effects:
#> Formula: list(parent_0 ~ 1, log_k_parent ~ 1)
#> Level: ds
#> Structure: General positive-definite, Log-Cholesky parametrization
#> StdDev Corr
#> parent_0 0.2624248 prnt_0
#> log_k_parent 0.5907146 0.999
#> Residual 1.0000000
#>
#> Variance function:
#> Structure: Constant plus proportion of variance covariate
#> Formula: ~fitted(.)
#> Parameter estimates:
#> const prop
#> 6.56706e-24 7.89583e-02
#>
#> Backtransformed parameters with asymmetric confidence intervals:
#> lower est. upper
#> parent_0 99.360213 101.59280 103.82539
#> k_parent 0.006883 0.01166 0.01976
#>
#> Estimated disappearance times:
#> DT50 DT90
#> parent 59.44 197.4
#>
#> Data:
#> ds name time observed predicted residual std standardized
#> ds 1 parent 0 104.1 101.417 2.68283 8.0077 0.335030
#> ds 1 parent 0 105.0 101.417 3.58283 8.0077 0.447422
#> ds 1 parent 1 98.5 100.624 -2.12400 7.9451 -0.267334
#> ds 1 parent 1 96.1 100.624 -4.52400 7.9451 -0.569407
#> ds 1 parent 3 101.9 99.056 2.84379 7.8213 0.363595
#> ds 1 parent 3 85.2 99.056 -13.85621 7.8213 -1.771597
#> ds 1 parent 7 99.1 95.994 3.10646 7.5795 0.409850
#> ds 1 parent 7 93.0 95.994 -2.99354 7.5795 -0.394953
#> ds 1 parent 14 88.1 90.860 -2.75997 7.1741 -0.384710
#> ds 1 parent 14 84.1 90.860 -6.75997 7.1741 -0.942268
#> ds 1 parent 28 80.2 81.402 -1.20174 6.4273 -0.186973
#> ds 1 parent 28 91.3 81.402 9.89826 6.4273 1.540024
#> ds 1 parent 60 65.1 63.316 1.78361 4.9994 0.356767
#> ds 1 parent 60 65.8 63.316 2.48361 4.9994 0.496785
#> ds 1 parent 90 47.8 50.029 -2.22862 3.9502 -0.564182
#> ds 1 parent 90 53.5 50.029 3.47138 3.9502 0.878792
#> ds 1 parent 120 37.6 39.529 -1.92946 3.1212 -0.618182
#> ds 1 parent 120 39.3 39.529 -0.22946 3.1212 -0.073516
#> ds 2 parent 0 107.9 101.711 6.18875 8.0309 0.770613
#> ds 2 parent 0 102.1 101.711 0.38875 8.0309 0.048406
#> ds 2 parent 1 103.8 100.174 3.62597 7.9096 0.458428
#> ds 2 parent 1 108.6 100.174 8.42597 7.9096 1.065287
#> ds 2 parent 3 91.0 97.169 -6.16895 7.6723 -0.804055
#> ds 2 parent 3 84.9 97.169 -12.26895 7.6723 -1.599124
#> ds 2 parent 7 79.3 91.427 -12.12652 7.2189 -1.679833
#> ds 2 parent 7 100.9 91.427 9.47348 7.2189 1.312320
#> ds 2 parent 14 77.3 82.182 -4.88174 6.4889 -0.752318
#> ds 2 parent 14 83.5 82.182 1.31826 6.4889 0.203155
#> ds 2 parent 28 66.8 66.402 0.39792 5.2430 0.075895
#> ds 2 parent 28 63.3 66.402 -3.10208 5.2430 -0.591662
#> ds 2 parent 60 40.8 40.789 0.01138 3.2206 0.003532
#> ds 2 parent 60 44.8 40.789 4.01138 3.2206 1.245537
#> ds 2 parent 90 27.8 25.830 1.97002 2.0395 0.965937
#> ds 2 parent 90 27.0 25.830 1.17002 2.0395 0.573682
#> ds 2 parent 120 15.2 16.357 -1.15721 1.2915 -0.895991
#> ds 2 parent 120 15.5 16.357 -0.85721 1.2915 -0.663710
#> ds 3 parent 0 97.7 101.907 -4.20726 8.0464 -0.522873
#> ds 3 parent 0 88.2 101.907 -13.70726 8.0464 -1.703521
#> ds 3 parent 1 109.9 99.522 10.37805 7.8581 1.320685
#> ds 3 parent 1 97.8 99.522 -1.72195 7.8581 -0.219130
#> ds 3 parent 3 100.5 94.918 5.58248 7.4945 0.744875
#> ds 3 parent 3 77.4 94.918 -17.51752 7.4945 -2.337375
#> ds 3 parent 7 78.3 86.338 -8.03788 6.8171 -1.179077
#> ds 3 parent 7 90.3 86.338 3.96212 6.8171 0.581204
#> ds 3 parent 14 76.0 73.147 2.85281 5.7756 0.493944
#> ds 3 parent 14 79.1 73.147 5.95281 5.7756 1.030687
#> ds 3 parent 28 46.0 52.504 -6.50373 4.1456 -1.568826
#> ds 3 parent 28 53.4 52.504 0.89627 4.1456 0.216197
#> ds 3 parent 60 25.1 24.605 0.49461 1.9428 0.254586
#> ds 3 parent 60 21.4 24.605 -3.20539 1.9428 -1.649882
#> ds 3 parent 90 11.0 12.090 -1.09046 0.9546 -1.142271
#> ds 3 parent 90 14.2 12.090 2.10954 0.9546 2.209770
#> ds 3 parent 120 5.8 5.941 -0.14094 0.4691 -0.300464
#> ds 3 parent 120 6.1 5.941 0.15906 0.4691 0.339077
#> ds 4 parent 0 95.3 101.177 -5.87672 7.9887 -0.735625
#> ds 4 parent 0 102.0 101.177 0.82328 7.9887 0.103056
#> ds 4 parent 1 104.4 100.716 3.68438 7.9523 0.463307
#> ds 4 parent 1 105.4 100.716 4.68438 7.9523 0.589057
#> ds 4 parent 3 113.7 99.800 13.90026 7.8800 1.763989
#> ds 4 parent 3 82.3 99.800 -17.49974 7.8800 -2.220774
#> ds 4 parent 7 98.1 97.993 0.10713 7.7374 0.013846
#> ds 4 parent 7 87.8 97.993 -10.19287 7.7374 -1.317359
#> ds 4 parent 14 97.9 94.909 2.99079 7.4939 0.399098
#> ds 4 parent 14 104.8 94.909 9.89079 7.4939 1.319851
#> ds 4 parent 28 85.0 89.030 -4.02995 7.0297 -0.573279
#> ds 4 parent 28 77.2 89.030 -11.82995 7.0297 -1.682864
#> ds 4 parent 60 82.2 76.923 5.27690 6.0737 0.868808
#> ds 4 parent 60 86.1 76.923 9.17690 6.0737 1.510919
#> ds 4 parent 90 70.5 67.073 3.42743 5.2959 0.647182
#> ds 4 parent 90 61.7 67.073 -5.37257 5.2959 -1.014470
#> ds 4 parent 120 60.0 58.483 1.51654 4.6178 0.328416
#> ds 4 parent 120 56.4 58.483 -2.08346 4.6178 -0.451184
#> ds 5 parent 0 92.6 101.752 -9.15161 8.0341 -1.139091
#> ds 5 parent 0 116.5 101.752 14.74839 8.0341 1.835716
#> ds 5 parent 1 108.0 100.069 7.93112 7.9013 1.003778
#> ds 5 parent 1 104.9 100.069 4.83112 7.9013 0.611436
#> ds 5 parent 3 100.5 96.786 3.71355 7.6421 0.485934
#> ds 5 parent 3 89.5 96.786 -7.28645 7.6421 -0.953462
#> ds 5 parent 7 91.7 90.541 1.15895 7.1490 0.162114
#> ds 5 parent 7 95.1 90.541 4.55895 7.1490 0.637707
#> ds 5 parent 14 82.2 80.566 1.63437 6.3613 0.256923
#> ds 5 parent 14 84.5 80.566 3.93437 6.3613 0.618483
#> ds 5 parent 28 60.5 63.791 -3.29084 5.0368 -0.653357
#> ds 5 parent 28 72.8 63.791 9.00916 5.0368 1.788662
#> ds 5 parent 60 38.3 37.412 0.88840 2.9540 0.300748
#> ds 5 parent 60 40.7 37.412 3.28840 2.9540 1.113217
#> ds 5 parent 90 22.5 22.685 -0.18500 1.7912 -0.103287
#> ds 5 parent 90 20.8 22.685 -1.88500 1.7912 -1.052387
#> ds 5 parent 120 13.4 13.755 -0.35534 1.0861 -0.327173
#> ds 5 parent 120 13.8 13.755 0.04466 1.0861 0.041118</div><div class='input'>
</div></pre>
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