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+ <h1>Estimate a limit of quantification (LOQ)</h1>
+ </div>
+
+
+ <p>The limit of quantification is the x value, where the relative error
+ of the quantification given the calibration model reaches a prespecified
+ value 1/k. Thus, it is the solution of the equation
+ $$L = k c(L)$$
+ where c(L) is half of the length of the confidence interval at the limit L
+ (DIN 32645, equivalent to ISO 11843). c(L) is internally estimated by
+ <code><a href='inverse.predict.html'>inverse.predict</a></code>, and L is obtained by iteration.</p>
+
+
+ <pre>loq(object, &#8230;, alpha = 0.05, k = 3, n = 1, w.loq = "auto",
+ var.loq = "auto", tol = "default")</pre>
+
+ <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a> Arguments</h2>
+ <dl class="dl-horizontal">
+ <dt>object</dt>
+ <dd>
+ A univariate model object of class <code>lm</code> or
+ <code><a href='http://www.rdocumentation.org/packages/MASS/topics/rlm'>rlm</a></code>
+ with model formula <code>y ~ x</code> or <code>y ~ x - 1</code>,
+ optionally from a weighted regression. If weights are specified
+ in the model, either <code>w.loq</code> or <code>var.loq</code> have to
+ be specified.
+ </dd>
+ <dt>alpha</dt>
+ <dd>
+ The error tolerance for the prediction of x values in the calculation.
+ </dd>
+ <dt>&#8230;</dt>
+ <dd>
+ Placeholder for further arguments that might be needed by
+ future implementations.
+ </dd>
+ <dt>k</dt>
+ <dd>
+ The inverse of the maximum relative error tolerated at the
+ desired LOQ.
+ </dd>
+ <dt>n</dt>
+ <dd>
+ The number of replicate measurements for which the LOQ should be
+ specified.
+ </dd>
+ <dt>w.loq</dt>
+ <dd>
+ The weight that should be attributed to the LOQ. Defaults
+ to one for unweighted regression, and to the mean of the weights
+ for weighted regression. See <code><a href='massart97ex3.html'>massart97ex3</a></code> for
+ an example how to take advantage of knowledge about the
+ variance function.
+ </dd>
+ <dt>var.loq</dt>
+ <dd>
+ The approximate variance at the LOQ. The default value is
+ calculated from the model.
+ </dd>
+ <dt>tol</dt>
+ <dd>
+ The default tolerance for the LOQ on the x scale is the value of the
+ smallest non-zero standard divided by 1000. Can be set to a
+ numeric value to override this.
+ </dd>
+ </dl>
+
+ <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+ <p>The estimated limit of quantification for a model used for calibration.</p>
+
+ <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+ <p>- IUPAC recommends to base the LOQ on the standard deviation of the signal
+ where x = 0.
+ - The calculation of a LOQ based on weighted regression is non-standard
+ and therefore not tested. Feedback is welcome.</p>
+
+ <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
+
+ <p>Examples for <code><a href='din32645.html'>din32645</a></code></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>)
+<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='fu'>loq</span>(<span class='no'>m</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'># We can get better by using replicate measurements</span>
+<span class='fu'>loq</span>(<span class='no'>m</span>, <span class='kw'>n</span> <span class='kw'>=</span> <span class='fl'>3</span>)</div><div class='output co'>#&gt; $x
+#&gt; [1] 9.971963
+#&gt;
+#&gt; $y
+#&gt; 1
+#&gt; 22.68539
+#&gt; </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="#arguments">Arguments</a></li>
+
+ <li><a href="#value">Value</a></li>
+
+ <li><a href="#note">Note</a></li>
+
+ <li><a href="#see-also">See also</a></li>
+
+ <li><a href="#examples">Examples</a></li>
+ </ul>
+
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