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<h1 class="title toc-ignore">Example evaluation of FOCUS Example Dataset D</h1>
<h4 class="author"><em>Johannes Ranke</em></h4>
<h4 class="date"><em>2018-07-17</em></h4>
<p>This is just a very simple vignette showing how to fit a degradation model for a parent compound with one transformation product using <code>mkin</code>. After loading the library we look a the data. We have observed concentrations in the column named <code>value</code> at the times specified in column <code>time</code> for the two observed variables named <code>parent</code> and <code>m1</code>.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"mkin"</span>, <span class="dt">quietly =</span> <span class="ot">TRUE</span>)
<span class="kw">print</span>(FOCUS_<span class="dv">2006</span>_D)</code></pre></div>
<pre><code>## name time value
## 1 parent 0 99.46
## 2 parent 0 102.04
## 3 parent 1 93.50
## 4 parent 1 92.50
## 5 parent 3 63.23
## 6 parent 3 68.99
## 7 parent 7 52.32
## 8 parent 7 55.13
## 9 parent 14 27.27
## 10 parent 14 26.64
## 11 parent 21 11.50
## 12 parent 21 11.64
## 13 parent 35 2.85
## 14 parent 35 2.91
## 15 parent 50 0.69
## 16 parent 50 0.63
## 17 parent 75 0.05
## 18 parent 75 0.06
## 19 parent 100 NA
## 20 parent 100 NA
## 21 parent 120 NA
## 22 parent 120 NA
## 23 m1 0 0.00
## 24 m1 0 0.00
## 25 m1 1 4.84
## 26 m1 1 5.64
## 27 m1 3 12.91
## 28 m1 3 12.96
## 29 m1 7 22.97
## 30 m1 7 24.47
## 31 m1 14 41.69
## 32 m1 14 33.21
## 33 m1 21 44.37
## 34 m1 21 46.44
## 35 m1 35 41.22
## 36 m1 35 37.95
## 37 m1 50 41.19
## 38 m1 50 40.01
## 39 m1 75 40.09
## 40 m1 75 33.85
## 41 m1 100 31.04
## 42 m1 100 33.13
## 43 m1 120 25.15
## 44 m1 120 33.31</code></pre>
<p>Next we specify the degradation model: The parent compound degrades with simple first-order kinetics (SFO) to one metabolite named m1, which also degrades with SFO kinetics.</p>
<p>The call to mkinmod returns a degradation model. The differential equations represented in R code can be found in the character vector <code>$diffs</code> of the <code>mkinmod</code> object. If a C compiler (gcc) is installed and functional, the differential equation model will be compiled from auto-generated C code.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">SFO_SFO <-<span class="st"> </span><span class="kw">mkinmod</span>(<span class="dt">parent =</span> <span class="kw">mkinsub</span>(<span class="st">"SFO"</span>, <span class="st">"m1"</span>), <span class="dt">m1 =</span> <span class="kw">mkinsub</span>(<span class="st">"SFO"</span>))</code></pre></div>
<pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">print</span>(SFO_SFO<span class="op">$</span>diffs)</code></pre></div>
<pre><code>## parent
## "d_parent = - k_parent_sink * parent - k_parent_m1 * parent"
## m1
## "d_m1 = + k_parent_m1 * parent - k_m1_sink * m1"</code></pre>
<p>We do the fitting without progress report (<code>quiet = TRUE</code>).</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">fit <-<span class="st"> </span><span class="kw">mkinfit</span>(SFO_SFO, FOCUS_<span class="dv">2006</span>_D, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)</code></pre></div>
<p>A plot of the fit including a residual plot for both observed variables is obtained using the <code>plot_sep</code> method for <code>mkinfit</code> objects, which shows separate graphs for all compounds and their residuals.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">plot_sep</span>(fit, <span class="dt">lpos =</span> <span class="kw">c</span>(<span class="st">"topright"</span>, <span class="st">"bottomright"</span>))</code></pre></div>
<p><img 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" /><!-- --></p>
<p>Confidence intervals for the parameter estimates are obtained using the <code>mkinparplot</code> function.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">mkinparplot</span>(fit)</code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(fit)</code></pre></div>
<pre><code>## mkin version used for fitting: 0.9.47.1
## R version used for fitting: 3.5.1
## Date of fit: Tue Jul 17 15:54:19 2018
## Date of summary: Tue Jul 17 15:54:19 2018
##
## Equations:
## d_parent/dt = - k_parent_sink * parent - k_parent_m1 * parent
## d_m1/dt = + k_parent_m1 * parent - k_m1_sink * m1
##
## Model predictions using solution type deSolve
##
## Fitted with method Port using 153 model solutions performed in 0.658 s
##
## Weighting: none
##
## Starting values for parameters to be optimised:
## value type
## parent_0 100.7500 state
## k_parent_sink 0.1000 deparm
## k_parent_m1 0.1001 deparm
## k_m1_sink 0.1002 deparm
##
## Starting values for the transformed parameters actually optimised:
## value lower upper
## parent_0 100.750000 -Inf Inf
## log_k_parent_sink -2.302585 -Inf Inf
## log_k_parent_m1 -2.301586 -Inf Inf
## log_k_m1_sink -2.300587 -Inf Inf
##
## Fixed parameter values:
## value type
## m1_0 0 state
##
## Optimised, transformed parameters with symmetric confidence intervals:
## Estimate Std. Error Lower Upper
## parent_0 99.600 1.61400 96.330 102.900
## log_k_parent_sink -3.038 0.07826 -3.197 -2.879
## log_k_parent_m1 -2.980 0.04124 -3.064 -2.897
## log_k_m1_sink -5.248 0.13610 -5.523 -4.972
##
## Parameter correlation:
## parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink
## parent_0 1.00000 0.6075 -0.06625 -0.1701
## log_k_parent_sink 0.60752 1.0000 -0.08740 -0.6253
## log_k_parent_m1 -0.06625 -0.0874 1.00000 0.4716
## log_k_m1_sink -0.17006 -0.6253 0.47164 1.0000
##
## Residual standard error: 3.211 on 36 degrees of freedom
##
## Backtransformed parameters:
## Confidence intervals for internally transformed parameters are asymmetric.
## t-test (unrealistically) based on the assumption of normal distribution
## for estimators of untransformed parameters.
## Estimate t value Pr(>t) Lower Upper
## parent_0 99.600000 61.720 2.024e-38 96.330000 1.029e+02
## k_parent_sink 0.047920 12.780 3.050e-15 0.040890 5.616e-02
## k_parent_m1 0.050780 24.250 3.407e-24 0.046700 5.521e-02
## k_m1_sink 0.005261 7.349 5.758e-09 0.003992 6.933e-03
##
## Chi2 error levels in percent:
## err.min n.optim df
## All data 6.398 4 15
## parent 6.827 3 6
## m1 4.490 1 9
##
## Resulting formation fractions:
## ff
## parent_sink 0.4855
## parent_m1 0.5145
## m1_sink 1.0000
##
## Estimated disappearance times:
## DT50 DT90
## parent 7.023 23.33
## m1 131.761 437.70
##
## Data:
## time variable observed predicted residual
## 0 parent 99.46 99.59848 -1.385e-01
## 0 parent 102.04 99.59848 2.442e+00
## 1 parent 93.50 90.23787 3.262e+00
## 1 parent 92.50 90.23787 2.262e+00
## 3 parent 63.23 74.07320 -1.084e+01
## 3 parent 68.99 74.07320 -5.083e+00
## 7 parent 52.32 49.91207 2.408e+00
## 7 parent 55.13 49.91207 5.218e+00
## 14 parent 27.27 25.01257 2.257e+00
## 14 parent 26.64 25.01257 1.627e+00
## 21 parent 11.50 12.53462 -1.035e+00
## 21 parent 11.64 12.53462 -8.946e-01
## 35 parent 2.85 3.14787 -2.979e-01
## 35 parent 2.91 3.14787 -2.379e-01
## 50 parent 0.69 0.71624 -2.624e-02
## 50 parent 0.63 0.71624 -8.624e-02
## 75 parent 0.05 0.06074 -1.074e-02
## 75 parent 0.06 0.06074 -7.382e-04
## 0 m1 0.00 0.00000 0.000e+00
## 0 m1 0.00 0.00000 0.000e+00
## 1 m1 4.84 4.80296 3.704e-02
## 1 m1 5.64 4.80296 8.370e-01
## 3 m1 12.91 13.02400 -1.140e-01
## 3 m1 12.96 13.02400 -6.400e-02
## 7 m1 22.97 25.04476 -2.075e+00
## 7 m1 24.47 25.04476 -5.748e-01
## 14 m1 41.69 36.69002 5.000e+00
## 14 m1 33.21 36.69002 -3.480e+00
## 21 m1 44.37 41.65310 2.717e+00
## 21 m1 46.44 41.65310 4.787e+00
## 35 m1 41.22 43.31312 -2.093e+00
## 35 m1 37.95 43.31312 -5.363e+00
## 50 m1 41.19 41.21831 -2.831e-02
## 50 m1 40.01 41.21831 -1.208e+00
## 75 m1 40.09 36.44704 3.643e+00
## 75 m1 33.85 36.44704 -2.597e+00
## 100 m1 31.04 31.98163 -9.416e-01
## 100 m1 33.13 31.98163 1.148e+00
## 120 m1 25.15 28.78984 -3.640e+00
## 120 m1 33.31 28.78984 4.520e+00</code></pre>
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