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<h1>Calibration data from DIN 32645</h1>
<div class="row">
<div class="span8">
<h2>Usage</h2>
<pre><div>data(din32645)</div></pre>
<div class="Description">
<h2>Description</h2>
<p>Sample dataset to test the package.</p>
</div>
<div class="Format">
<h2>Format</h2>
<p>A dataframe containing 10 rows of x and y values.</p>
</div>
<div class="References">
<h2>References</h2>
<p>DIN 32645 (equivalent to ISO 11843), Beuth Verlag, Berlin, 1994</p>
<p>Dintest. Plugin for MS Excel for evaluations of calibration data. Written
by Georg Schmitt, University of Heidelberg. Formerly available from
the Website of the University of Heidelberg.</p>
<p>Currie, L. A. (1997) Nomenclature in evaluation of analytical methods including
detection and quantification capabilities (IUPAC Recommendations 1995).
Analytica Chimica Acta 391, 105 - 126.</p>
</div>
<h2 id="examples">Examples</h2>
<pre class="examples"><div class='input'>data(din32645)
m <- lm(y ~ x, data = din32645)
calplot(m)
</div>
<p><img src='din32645-2.png' alt='' width='540' height='400' /></p>
<div class='input'>
## Prediction of x with confidence interval
(prediction <- inverse.predict(m, 3500, alpha = 0.01))
</div>
<div class='output'>$Prediction
[1] 0.1054792
$`Standard Error`
[1] 0.02215619
$Confidence
[1] 0.07434261
$`Confidence Limits`
[1] 0.03113656 0.17982178
</div>
<div class='input'>
# This should give 0.07434 according to test data from Dintest, which
# was collected from Procontrol 3.1 (isomehr GmbH) in this case
round(prediction$Confidence,5)
</div>
<div class='output'>[1] 0.07434
</div>
<div class='input'>
## Critical value:
(crit <- lod(m, alpha = 0.01, beta = 0.5))
</div>
<div class='output'>$x
[1] 0.0698127
$y
1
3155.393
</div>
<div class='input'>
# According to DIN 32645, we should get 0.07 for the critical value
# (decision limit, "Nachweisgrenze")
round(crit$x, 2)
</div>
<div class='output'>[1] 0.07
</div>
<div class='input'># and according to Dintest test data, we should get 0.0698 from
round(crit$x, 4)
</div>
<div class='output'>[1] 0.0698
</div>
<div class='input'>
## Limit of detection (smallest detectable value given alpha and beta)
# In German, the smallest detectable value is the "Erfassungsgrenze", and we
# should get 0.14 according to DIN, which we achieve by using the method
# described in it:
lod.din <- lod(m, alpha = 0.01, beta = 0.01, method = "din")
round(lod.din$x, 2)
</div>
<div class='output'>[1] 0.14
</div>
<div class='input'>
## Limit of quantification
# This accords to the test data coming with the test data from Dintest again,
# except for the last digits of the value cited for Procontrol 3.1 (0.2121)
(loq <- loq(m, alpha = 0.01))
</div>
<div class='output'>$x
[1] 0.2119575
$y
1
4528.787
</div>
<div class='input'>round(loq$x,4)
</div>
<div class='output'>[1] 0.212
</div>
<div class='input'>
# A similar value is obtained using the approximation
# LQ = 3.04 * LC (Currie 1999, p. 120)
3.04 * lod(m,alpha = 0.01, beta = 0.5)$x
</div>
<div class='output'>[1] 0.2122306
</div></pre>
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