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<!-- README.md is generated from README.rmd. Please edit that file -->
<h1 id="chemcal---calibration-functions-for-analytical-chemistry">chemCal - Calibration functions for analytical chemistry</h1>
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<p><a href="https://cran.r-project.org/package=chemCal"><img src="data:image/svg+xml; 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/></a> <a href="https://app.travis-ci.com/github/jranke/chemCal"><img src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSI5MCIgaGVpZ2h0PSIyMCI+PGxpbmVhckdyYWRpZW50IGlkPSJhIiB4Mj0iMCIgeTI9IjEwMCUiPjxzdG9wIG9mZnNldD0iMCIgc3RvcC1jb2xvcj0iI2JiYiIgc3RvcC1vcGFjaXR5PSIuMSIvPjxzdG9wIG9mZnNldD0iMSIgc3RvcC1vcGFjaXR5PSIuMSIvPjwvbGluZWFyR3JhZGllbnQ+PHJlY3Qgcng9IjMiIHdpZHRoPSI5MCIgaGVpZ2h0PSIyMCIgZmlsbD0iIzU1NSIvPjxyZWN0IHJ4PSIzIiB4PSIzNyIgd2lkdGg9IjUzIiBoZWlnaHQ9IjIwIiBmaWxsPSIjNGMxIi8+PHBhdGggZmlsbD0iIzRjMSIgZD0iTTM3IDBoNHYyMGgtNHoiLz48cmVjdCByeD0iMyIgd2lkdGg9IjkwIiBoZWlnaHQ9IjIwIiBmaWxsPSJ1cmwoI2EpIi8+PGcgZmlsbD0iI2ZmZiIgdGV4dC1hbmNob3I9Im1pZGRsZSIgZm9udC1mYW1pbHk9IkRlamFWdSBTYW5zLFZlcmRhbmEsR2VuZXZhLHNhbnMtc2VyaWYiIGZvbnQtc2l6ZT0iMTEiPjx0ZXh0IHg9IjE5LjUiIHk9IjE1IiBmaWxsPSIjMDEwMTAxIiBmaWxsLW9wYWNpdHk9Ii4zIj5idWlsZDwvdGV4dD48dGV4dCB4PSIxOS41IiB5PSIxNCI+YnVpbGQ8L3RleHQ+PHRleHQgeD0iNjIuNSIgeT0iMTUiIGZpbGw9IiMwMTAxMDEiIGZpbGwtb3BhY2l0eT0iLjMiPnBhc3Npbmc8L3RleHQ+PHRleHQgeD0iNjIuNSIgeT0iMTQiPnBhc3Npbmc8L3RleHQ+PC9nPjwvc3ZnPg==" alt="Build Status" /></a> <a href="https://codecov.io/github/jranke/chemCal"><img 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alt="codecov" /></a></p>
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<h2 id="overview">Overview</h2>
<p>chemCal is an R package providing some basic functions for conveniently working with linear calibration curves with one explanatory variable.</p>
<h2 id="installation">Installation</h2>
<p>From within <a href="https://www.r-project.org/">R</a>, get the official chemCal release using</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true"></a><span class="kw">install.packages</span>(<span class="st">"chemCal"</span>)</span></code></pre></div>
<h2 id="usage">Usage</h2>
<p>chemCal works with univariate linear models of class <code>lm</code>. Working with one of the datasets coming with chemCal, we can produce a calibration plot using the <code>calplot</code> function:</p>
<h3 id="plotting-a-calibration">Plotting a calibration</h3>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true"></a><span class="kw">library</span>(chemCal)</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true"></a>m0 <-<span class="st"> </span><span class="kw">lm</span>(y <span class="op">~</span><span class="st"> </span>x, <span class="dt">data =</span> massart97ex3)</span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true"></a><span class="kw">calplot</span>(m0)</span></code></pre></div>
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" /><!-- --></p>
<h3 id="lod-and-loq">LOD and LOQ</h3>
<p>If you use unweighted regression, as in the above example, we can calculate a Limit Of Detection (LOD) from the calibration data.</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true"></a><span class="kw">lod</span>(m0)</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true"></a><span class="co">#> $x</span></span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true"></a><span class="co">#> [1] 5.407085</span></span>
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true"></a><span class="co">#> </span></span>
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true"></a><span class="co">#> $y</span></span>
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true"></a><span class="co">#> [1] 13.63911</span></span></code></pre></div>
<p>This is the minimum detectable value (German: Erfassungsgrenze), i.e. the value where the probability that the signal is not detected although the analyte is present is below a specified error tolerance beta (default is 0.05 following the IUPAC recommendation).</p>
<p>You can also calculate the decision limit (German: Nachweisgrenze), i.e. the value that is significantly different from the blank signal with an error tolerance alpha (default is 0.05, again following IUPAC recommendations) by setting beta to 0.5.</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true"></a><span class="kw">lod</span>(m0, <span class="dt">beta =</span> <span class="fl">0.5</span>)</span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true"></a><span class="co">#> $x</span></span>
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true"></a><span class="co">#> [1] 2.720388</span></span>
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true"></a><span class="co">#> </span></span>
<span id="cb4-5"><a href="#cb4-5" aria-hidden="true"></a><span class="co">#> $y</span></span>
<span id="cb4-6"><a href="#cb4-6" aria-hidden="true"></a><span class="co">#> [1] 8.314841</span></span></code></pre></div>
<p>Furthermore, you can calculate the Limit Of Quantification (LOQ), being defined as the value where the relative error of the quantification given the calibration model reaches a prespecified value (default is 1/3).</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true"></a><span class="kw">loq</span>(m0)</span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true"></a><span class="co">#> $x</span></span>
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true"></a><span class="co">#> [1] 9.627349</span></span>
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true"></a><span class="co">#> </span></span>
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true"></a><span class="co">#> $y</span></span>
<span id="cb5-6"><a href="#cb5-6" aria-hidden="true"></a><span class="co">#> [1] 22.00246</span></span></code></pre></div>
<h3 id="confidence-intervals-for-measured-values">Confidence intervals for measured values</h3>
<p>Finally, you can get a confidence interval for the values measured using the calibration curve, i.e. for the inverse predictions using the function <code>inverse.predict</code>.</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true"></a><span class="kw">inverse.predict</span>(m0, <span class="dv">90</span>)</span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true"></a><span class="co">#> $Prediction</span></span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true"></a><span class="co">#> [1] 43.93983</span></span>
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true"></a><span class="co">#> </span></span>
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true"></a><span class="co">#> $`Standard Error`</span></span>
<span id="cb6-6"><a href="#cb6-6" aria-hidden="true"></a><span class="co">#> [1] 1.576985</span></span>
<span id="cb6-7"><a href="#cb6-7" aria-hidden="true"></a><span class="co">#> </span></span>
<span id="cb6-8"><a href="#cb6-8" aria-hidden="true"></a><span class="co">#> $Confidence</span></span>
<span id="cb6-9"><a href="#cb6-9" aria-hidden="true"></a><span class="co">#> [1] 3.230307</span></span>
<span id="cb6-10"><a href="#cb6-10" aria-hidden="true"></a><span class="co">#> </span></span>
<span id="cb6-11"><a href="#cb6-11" aria-hidden="true"></a><span class="co">#> $`Confidence Limits`</span></span>
<span id="cb6-12"><a href="#cb6-12" aria-hidden="true"></a><span class="co">#> [1] 40.70952 47.17014</span></span></code></pre></div>
<p>If you have replicate measurements of the same sample, you can also give a vector of numbers.</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true"></a><span class="kw">inverse.predict</span>(m0, <span class="kw">c</span>(<span class="dv">91</span>, <span class="dv">89</span>, <span class="dv">87</span>, <span class="dv">93</span>, <span class="dv">90</span>))</span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true"></a><span class="co">#> $Prediction</span></span>
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true"></a><span class="co">#> [1] 43.93983</span></span>
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true"></a><span class="co">#> </span></span>
<span id="cb7-5"><a href="#cb7-5" aria-hidden="true"></a><span class="co">#> $`Standard Error`</span></span>
<span id="cb7-6"><a href="#cb7-6" aria-hidden="true"></a><span class="co">#> [1] 0.796884</span></span>
<span id="cb7-7"><a href="#cb7-7" aria-hidden="true"></a><span class="co">#> </span></span>
<span id="cb7-8"><a href="#cb7-8" aria-hidden="true"></a><span class="co">#> $Confidence</span></span>
<span id="cb7-9"><a href="#cb7-9" aria-hidden="true"></a><span class="co">#> [1] 1.632343</span></span>
<span id="cb7-10"><a href="#cb7-10" aria-hidden="true"></a><span class="co">#> </span></span>
<span id="cb7-11"><a href="#cb7-11" aria-hidden="true"></a><span class="co">#> $`Confidence Limits`</span></span>
<span id="cb7-12"><a href="#cb7-12" aria-hidden="true"></a><span class="co">#> [1] 42.30749 45.57217</span></span></code></pre></div>
<h2 id="reference">Reference</h2>
<p>You can use the R help system to view documentation, or you can have a look at the <a href="https://pkgdown.jrwb.de/chemCal/">online documentation</a>.</p>
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