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degradation model using mkinpredict. The variance of the error
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<h1>Add normally distributed errors to simulated kinetic degradation data</h1>
<small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/add_err.R" class="external-link"><code>R/add_err.R</code></a></small>
<div class="hidden name"><code>add_err.Rd</code></div>
</div>
<div class="ref-description">
<p>Normally distributed errors are added to data predicted for a specific
degradation model using <code><a href="mkinpredict.html">mkinpredict</a></code>. The variance of the error
may depend on the predicted value and is specified as a standard deviation.</p>
</div>
<div id="ref-usage">
<div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">add_err</span><span class="op">(</span></span>
<span> <span class="va">prediction</span>,</span>
<span> <span class="va">sdfunc</span>,</span>
<span> secondary <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span>
<span> n <span class="op">=</span> <span class="fl">10</span>,</span>
<span> LOD <span class="op">=</span> <span class="fl">0.1</span>,</span>
<span> reps <span class="op">=</span> <span class="fl">2</span>,</span>
<span> digits <span class="op">=</span> <span class="fl">1</span>,</span>
<span> seed <span class="op">=</span> <span class="cn">NA</span></span>
<span><span class="op">)</span></span></code></pre></div>
</div>
<div id="arguments">
<h2>Arguments</h2>
<dl><dt>prediction</dt>
<dd><p>A prediction from a kinetic model as produced by
<code><a href="mkinpredict.html">mkinpredict</a></code>.</p></dd>
<dt>sdfunc</dt>
<dd><p>A function taking the predicted value as its only argument and
returning a standard deviation that should be used for generating the
random error terms for this value.</p></dd>
<dt>secondary</dt>
<dd><p>The names of state variables that should have an initial
value of zero</p></dd>
<dt>n</dt>
<dd><p>The number of datasets to be generated.</p></dd>
<dt>LOD</dt>
<dd><p>The limit of detection (LOD). Values that are below the LOD after
adding the random error will be set to NA.</p></dd>
<dt>reps</dt>
<dd><p>The number of replicates to be generated within the datasets.</p></dd>
<dt>digits</dt>
<dd><p>The number of digits to which the values will be rounded.</p></dd>
<dt>seed</dt>
<dd><p>The seed used for the generation of random numbers. If NA, the
seed is not set.</p></dd>
</dl></div>
<div id="value">
<h2>Value</h2>
<p>A list of datasets compatible with <code><a href="mmkin.html">mmkin</a></code>, i.e. the
components of the list are datasets compatible with <code><a href="mkinfit.html">mkinfit</a></code>.</p>
</div>
<div id="references">
<h2>References</h2>
<p>Ranke J and Lehmann R (2015) To t-test or not to t-test, that is
the question. XV Symposium on Pesticide Chemistry 2-4 September 2015,
Piacenza, Italy
https://jrwb.de/posters/piacenza_2015.pdf</p>
</div>
<div id="author">
<h2>Author</h2>
<p>Johannes Ranke</p>
</div>
<div id="ref-examples">
<h2>Examples</h2>
<div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># The kinetic model</span></span></span>
<span class="r-in"><span><span class="va">m_SFO_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="st">"M1"</span><span class="op">)</span>,</span></span>
<span class="r-in"><span> M1 <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>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span>
<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Generate a prediction for a specific set of parameters</span></span></span>
<span class="r-in"><span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">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></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># This is the prediction used for the "Type 2 datasets" on the Piacenza poster</span></span></span>
<span class="r-in"><span><span class="co"># from 2015</span></span></span>
<span class="r-in"><span><span class="va">d_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO_SFO</span>,</span></span>
<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.1</span>, f_parent_to_M1 <span class="op">=</span> <span class="fl">0.5</span>,</span></span>
<span class="r-in"><span> k_M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">/</span><span class="fl">1000</span><span class="op">)</span>,</span></span>
<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span></span>
<span class="r-in"><span> <span class="va">sampling_times</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Add an error term with a constant (independent of the value) standard deviation</span></span></span>
<span class="r-in"><span><span class="co"># of 10, and generate three datasets</span></span></span>
<span class="r-in"><span><span class="va">d_SFO_SFO_err</span> <span class="op"><-</span> <span class="fu">add_err</span><span class="op">(</span><span class="va">d_SFO_SFO</span>, <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fl">10</span>, n <span class="op">=</span> <span class="fl">3</span>, seed <span class="op">=</span> <span class="fl">123456789</span> <span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Name the datasets for nicer plotting</span></span></span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">d_SFO_SFO_err</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Name the model in the list of models (with only one member in this case) for</span></span></span>
<span class="r-in"><span><span class="co"># nicer plotting later on. Be quiet and use only one core not to offend CRAN</span></span></span>
<span class="r-in"><span><span class="co"># checks</span></span></span>
<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
<span class="r-in"><span><span class="va">f_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"SFO-SFO"</span> <span class="op">=</span> <span class="va">m_SFO_SFO</span><span class="op">)</span>,</span></span>
<span class="r-in"><span> <span class="va">d_SFO_SFO_err</span>, cores <span class="op">=</span> <span class="fl">1</span>,</span></span>
<span class="r-in"><span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_SFO_SFO</span><span class="op">)</span></span></span>
<span class="r-plt img"><img src="add_err-1.png" alt="" width="700" height="433"></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># We would like to inspect the fit for dataset 3 more closely</span></span></span>
<span class="r-in"><span><span class="co"># Using double brackets makes the returned object an mkinfit object</span></span></span>
<span class="r-in"><span><span class="co"># instead of a list of mkinfit objects, so plot.mkinfit is used</span></span></span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_SFO_SFO</span><span class="op">[[</span><span class="fl">3</span><span class="op">]</span><span class="op">]</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
<span class="r-plt img"><img src="add_err-2.png" alt="" width="700" height="433"></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># If we use single brackets, we should give two indices (model and dataset),</span></span></span>
<span class="r-in"><span><span class="co"># and plot.mmkin is used</span></span></span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_SFO_SFO</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">]</span><span class="op">)</span></span></span>
<span class="r-plt img"><img src="add_err-3.png" alt="" width="700" height="433"></span>
<span class="r-in"><span><span class="co"># }</span></span></span>
<span class="r-in"><span></span></span>
</code></pre></div>
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