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<h1>Calibration data from Massart et al. (1997), example 3</h1>
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<p>Sample dataset from p. 188 to test the package.</p>
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<pre class="usage"><span class='fu'>data</span>(<span class='no'>massart97ex3</span>)</pre>
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<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'>calplot</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><div class='img'><img src='massart97ex3-1.png' alt='' width='700' height='432.632880098887' /></div><div class='input'>
+<span class='fu'>calplot</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><div class='img'><img src='massart97ex3-1.png' alt='' width='700' height='433' /></div><div class='input'>
<span class='co'># The following concords with the book p. 200</span>
<span class='fu'>inverse.predict</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
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