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-<title>massart97ex3. chemCal 0.1-37</title>
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- <ul class="nav">
- <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li>
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- <header>
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-
- <h1>Calibration data from Massart et al. (1997), example 3</h1>
-
-<div class="row">
- <div class="span8">
- <h2>Usage</h2>
- <pre><div>data(massart97ex3)</div></pre>
-
- <div class="Description">
- <h2>Description</h2>
-
- <p>Sample dataset from p. 188 to test the package.</p>
-
- </div>
-
- <div class="Format">
- <h2>Format</h2>
-
- <p>A dataframe containing 6 levels of x values with 5
- observations of y for each level.</p>
-
- </div>
-
- <div class="Source">
- <h2>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>
-
- </div>
-
- <h2 id="examples">Examples</h2>
- <pre class="examples"><div class='input'>data(massart97ex3)
-attach(massart97ex3)
-</div>
-<strong class='message'>Die folgenden Objekte sind maskiert von massart97ex3 (pos = 3):
-
- x, y
-</strong>
-<div class='input'>yx &lt;- split(y, x)
-ybar &lt;- sapply(yx, mean)
-s &lt;- round(sapply(yx, sd), digits = 2)
-w &lt;- round(1 / (s^2), digits = 3)
-weights &lt;- w[factor(x)]
-m &lt;- lm(y ~ x, w = weights)
-calplot(m)
-</div>
-<strong class='warning'>Warning message:
-Assuming constant prediction variance even though model fit is weighted
-</strong>
-<p><img src='massart97ex3-5.png' alt='' width='540' height='400' /></p>
-<div class='input'>
-# The following concords with the book p. 200
-inverse.predict(m, 15, ws = 1.67) # 5.9 +- 2.5
-</div>
-<div class='output'>$Prediction
-[1] 5.865367
-
-$`Standard Error`
-[1] 0.8926109
-
-$Confidence
-[1] 2.478285
-
-$`Confidence Limits`
-[1] 3.387082 8.343652
-
-</div>
-<div class='input'>inverse.predict(m, 90, ws = 0.145) # 44.1 +- 7.9
-</div>
-<div class='output'>$Prediction
-[1] 44.06025
-
-$`Standard Error`
-[1] 2.829162
-
-$Confidence
-[1] 7.855012
-
-$`Confidence Limits`
-[1] 36.20523 51.91526
-
-</div>
-<div class='input'>
-# The LOD is only calculated for models from unweighted regression
-# with this version of chemCal
-m0 &lt;- lm(y ~ x)
-lod(m0)
-</div>
-<div class='output'>$x
-[1] 5.407085
-
-$y
- 1
-13.63911
-
-</div>
-<div class='input'>
-# Limit of quantification from unweighted regression
-loq(m0)
-</div>
-<div class='output'>$x
-[1] 13.97764
-
-$y
- 1
-30.6235
-
-</div>
-<div class='input'>
-# For calculating the limit of quantification from a model from weighted
-# regression, we need to supply weights, internally used for inverse.predict
-# If we are not using a variance function, we can use the weight from
-# the above example as a first approximation (x = 15 is close to our
-# loq approx 14 from above).
-loq(m, w.loq = 1.67)
-</div>
-<div class='output'>$x
-[1] 7.346195
-
-$y
- 1
-17.90777
-
-</div>
-<div class='input'># The weight for the loq should therefore be derived at x = 7.3 instead
-# of 15, but the graphical procedure of Massart (p. 201) to derive the
-# variances on which the weights are based is quite inaccurate anyway.
-</div></pre>
- </div>
- <div class="span4">
- <!-- <ul>
- <li>massart97ex3</li>
- </ul>
- <ul>
- <li>datasets</li>
- </ul> -->
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