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+ <h1>Calibration data from Massart et al. (1997), example 3</h1>
+ </div>
+
+
+ <p>Sample dataset from p. 188 to test the package.</p>
+
+
+ <pre><span class='fu'>data</span>(<span class='no'>massart97ex3</span>)</pre>
+
+ <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>
+
+ <p>A dataframe containing 6 levels of x values with 5
+ observations of y for each level.</p>
+
+ <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>
+
+ <p>Massart, L.M, Vandenginste, B.G.M., Buydens, L.M.C., De Jong, S., Lewi, P.J.,
+ Smeyers-Verbeke, J. (1997) Handbook of Chemometrics and Qualimetrics: Part A,
+ Chapter 8.</p>
+
+
+ <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+ <pre class="examples"><div class='input'><span class='fu'>data</span>(<span class='no'>massart97ex3</span>)
+<span class='fu'>attach</span>(<span class='no'>massart97ex3</span>)</div><div class='output co'>#&gt; <span class='message'>The following objects are masked from massart97ex3 (pos = 3):</span>
+#&gt; <span class='message'></span>
+#&gt; <span class='message'> x, y</span></div><div class='input'><span class='no'>yx</span> <span class='kw'>&lt;-</span> <span class='fu'>split</span>(<span class='no'>y</span>, <span class='no'>x</span>)
+<span class='no'>ybar</span> <span class='kw'>&lt;-</span> <span class='fu'>sapply</span>(<span class='no'>yx</span>, <span class='no'>mean</span>)
+<span class='no'>s</span> <span class='kw'>&lt;-</span> <span class='fu'>round</span>(<span class='fu'>sapply</span>(<span class='no'>yx</span>, <span class='no'>sd</span>), <span class='kw'>digits</span> <span class='kw'>=</span> <span class='fl'>2</span>)
+<span class='no'>w</span> <span class='kw'>&lt;-</span> <span class='fu'>round</span>(<span class='fl'>1</span> / (<span class='no'>s</span>^<span class='fl'>2</span>), <span class='kw'>digits</span> <span class='kw'>=</span> <span class='fl'>3</span>)
+<span class='no'>weights</span> <span class='kw'>&lt;-</span> <span class='no'>w</span>[<span class='fu'>factor</span>(<span class='no'>x</span>)]
+<span class='no'>m</span> <span class='kw'>&lt;-</span> <span class='fu'>lm</span>(<span class='no'>y</span> ~ <span class='no'>x</span>, <span class='kw'>w</span> <span class='kw'>=</span> <span class='no'>weights</span>)
+<span class='fu'><a href='calplot.html'>calplot</a></span>(<span class='no'>m</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Assuming constant prediction variance even though model fit is weighted</span></div><img src='massart97ex3-5.png' alt='' width='540' height='400' /><div class='input'>
+<span class='co'># The following concords with the book p. 200</span>
+<span class='fu'><a href='inverse.predict.html'>inverse.predict</a></span>(<span class='no'>m</span>, <span class='fl'>15</span>, <span class='kw'>ws</span> <span class='kw'>=</span> <span class='fl'>1.67</span>) <span class='co'># 5.9 +- 2.5</span></div><div class='output co'>#&gt; $Prediction
+#&gt; [1] 5.865367
+#&gt;
+#&gt; $`Standard Error`
+#&gt; [1] 0.8926109
+#&gt;
+#&gt; $Confidence
+#&gt; [1] 2.478285
+#&gt;
+#&gt; $`Confidence Limits`
+#&gt; [1] 3.387082 8.343652
+#&gt; </div><div class='input'><span class='fu'><a href='inverse.predict.html'>inverse.predict</a></span>(<span class='no'>m</span>, <span class='fl'>90</span>, <span class='kw'>ws</span> <span class='kw'>=</span> <span class='fl'>0.145</span>) <span class='co'># 44.1 +- 7.9</span></div><div class='output co'>#&gt; $Prediction
+#&gt; [1] 44.06025
+#&gt;
+#&gt; $`Standard Error`
+#&gt; [1] 2.829162
+#&gt;
+#&gt; $Confidence
+#&gt; [1] 7.855012
+#&gt;
+#&gt; $`Confidence Limits`
+#&gt; [1] 36.20523 51.91526
+#&gt; </div><div class='input'>
+<span class='co'># The LOD is only calculated for models from unweighted regression</span>
+<span class='co'># with this version of chemCal</span>
+<span class='no'>m0</span> <span class='kw'>&lt;-</span> <span class='fu'>lm</span>(<span class='no'>y</span> ~ <span class='no'>x</span>)
+<span class='fu'><a href='lod.html'>lod</a></span>(<span class='no'>m0</span>)</div><div class='output co'>#&gt; $x
+#&gt; [1] 5.407085
+#&gt;
+#&gt; $y
+#&gt; 1
+#&gt; 13.63911
+#&gt; </div><div class='input'>
+<span class='co'># Limit of quantification from unweighted regression</span>
+<span class='fu'><a href='loq.html'>loq</a></span>(<span class='no'>m0</span>)</div><div class='output co'>#&gt; $x
+#&gt; [1] 13.97764
+#&gt;
+#&gt; $y
+#&gt; 1
+#&gt; 30.6235
+#&gt; </div><div class='input'>
+<span class='co'># For calculating the limit of quantification from a model from weighted</span>
+<span class='co'># regression, we need to supply weights, internally used for inverse.predict</span>
+<span class='co'># If we are not using a variance function, we can use the weight from</span>
+<span class='co'># the above example as a first approximation (x = 15 is close to our</span>
+<span class='co'># loq approx 14 from above).</span>
+<span class='fu'><a href='loq.html'>loq</a></span>(<span class='no'>m</span>, <span class='kw'>w.loq</span> <span class='kw'>=</span> <span class='fl'>1.67</span>)</div><div class='output co'>#&gt; $x
+#&gt; [1] 7.346195
+#&gt;
+#&gt; $y
+#&gt; 1
+#&gt; 17.90777
+#&gt; </div><div class='input'><span class='co'># The weight for the loq should therefore be derived at x = 7.3 instead</span>
+<span class='co'># of 15, but the graphical procedure of Massart (p. 201) to derive the </span>
+<span class='co'># variances on which the weights are based is quite inaccurate anyway. </span></div></pre>
+ </div>
+ <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
+ <h2>Contents</h2>
+ <ul class="nav nav-pills nav-stacked">
+
+ <li><a href="#format">Format</a></li>
+
+ <li><a href="#source">Source</a></li>
+
+ <li><a href="#examples">Examples</a></li>
+ </ul>
+
+ </div>
+</div>
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