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<h1>Plot calibration graphs from univariate linear models</h1>
@@ -125,102 +57,88 @@
as confidence, and, for unweighted regression, prediction bands.</p>
</div>
- <pre class="usage"><span class='fu'>calplot</span><span class='op'>(</span><span class='va'>object</span>, xlim <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"auto"</span>, <span class='st'>"auto"</span><span class='op'>)</span>, ylim <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"auto"</span>, <span class='st'>"auto"</span><span class='op'>)</span>,
- xlab <span class='op'>=</span> <span class='st'>"Concentration"</span>, ylab <span class='op'>=</span> <span class='st'>"Response"</span>, legend_x <span class='op'>=</span> <span class='st'>"auto"</span>,
- alpha<span class='op'>=</span><span class='fl'>0.05</span>, varfunc <span class='op'>=</span> <span class='cn'>NULL</span><span class='op'>)</span></pre>
-
- <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
- <table class="ref-arguments">
- <colgroup><col class="name" /><col class="desc" /></colgroup>
- <tr>
- <th>object</th>
- <td><p>A univariate model object of class <code><a href='https://rdrr.io/r/stats/lm.html'>lm</a></code> or
- <code><a href='https://rdrr.io/pkg/MASS/man/rlm.html'>rlm</a></code>
- with model formula <code>y ~ x</code> or <code>y ~ x - 1</code>.</p></td>
- </tr>
- <tr>
- <th>xlim</th>
- <td><p>The limits of the plot on the x axis.</p></td>
- </tr>
- <tr>
- <th>ylim</th>
- <td><p>The limits of the plot on the y axis.</p></td>
- </tr>
- <tr>
- <th>xlab</th>
- <td><p>The label of the x axis.</p></td>
- </tr>
- <tr>
- <th>ylab</th>
- <td><p>The label of the y axis.</p></td>
- </tr>
- <tr>
- <th>legend_x</th>
- <td><p>An optional numeric value for adjusting the x coordinate of the legend.</p></td>
- </tr>
- <tr>
- <th>alpha</th>
- <td><p>The error tolerance level for the confidence and prediction bands. Note that this
- includes both tails of the Gaussian distribution, unlike the alpha and beta parameters
- used in <code><a href='lod.html'>lod</a></code> (see note below).</p></td>
- </tr>
- <tr>
- <th>varfunc</th>
- <td><p>The variance function for generating the weights in the model.
- Currently, this argument is ignored (see note below).</p></td>
- </tr>
- </table>
-
- <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+ <div id="ref-usage">
+ <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">calplot</span><span class="op">(</span><span class="va">object</span>, xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"auto"</span>, <span class="st">"auto"</span><span class="op">)</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"auto"</span>, <span class="st">"auto"</span><span class="op">)</span>,
+ xlab <span class="op">=</span> <span class="st">"Concentration"</span>, ylab <span class="op">=</span> <span class="st">"Response"</span>, legend_x <span class="op">=</span> <span class="st">"auto"</span>,
+ alpha<span class="op">=</span><span class="fl">0.05</span>, varfunc <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></code></pre></div>
+ </div>
+ <div id="arguments">
+ <h2>Arguments</h2>
+ <dl><dt>object</dt>
+<dd><p>A univariate model object of class <code><a href="https://rdrr.io/r/stats/lm.html" class="external-link">lm</a></code> or
+ <code><a href="https://rdrr.io/pkg/MASS/man/rlm.html" class="external-link">rlm</a></code>
+ with model formula <code>y ~ x</code> or <code>y ~ x - 1</code>.</p></dd>
+<dt>xlim</dt>
+<dd><p>The limits of the plot on the x axis.</p></dd>
+<dt>ylim</dt>
+<dd><p>The limits of the plot on the y axis.</p></dd>
+<dt>xlab</dt>
+<dd><p>The label of the x axis.</p></dd>
+<dt>ylab</dt>
+<dd><p>The label of the y axis.</p></dd>
+<dt>legend_x</dt>
+<dd><p>An optional numeric value for adjusting the x coordinate of the legend.</p></dd>
+<dt>alpha</dt>
+<dd><p>The error tolerance level for the confidence and prediction bands. Note that this
+ includes both tails of the Gaussian distribution, unlike the alpha and beta parameters
+ used in <code><a href="lod.html">lod</a></code> (see note below).</p></dd>
+<dt>varfunc</dt>
+<dd><p>The variance function for generating the weights in the model.
+ Currently, this argument is ignored (see note below).</p></dd>
+</dl></div>
+ <div id="value">
+ <h2>Value</h2>
<p>A plot of the calibration data, of your fitted model as well as lines showing
the confidence limits. Prediction limits are only shown for models from
unweighted regression.</p>
- <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
-
+ </div>
+ <div id="note">
+ <h2>Note</h2>
<p>Prediction bands for models from weighted linear regression require weights
for the data, for which responses should be predicted. Prediction intervals
using weights e.g. from a variance function are currently not supported by
- the internally used function <code><a href='https://rdrr.io/r/stats/predict.lm.html'>predict.lm</a></code>, therefore,
+ the internally used function <code><a href="https://rdrr.io/r/stats/predict.lm.html" class="external-link">predict.lm</a></code>, therefore,
<code>calplot</code> does not draw prediction bands for such models.</p>
<p>It is possible to compare the <code>calplot</code> prediction bands with the
- <code><a href='lod.html'>lod</a></code> values if the <code><a href='lod.html'>lod()</a></code> alpha and beta parameters are
+ <code><a href="lod.html">lod</a></code> values if the <code><a href="lod.html">lod()</a></code> alpha and beta parameters are
half the value of the <code>calplot()</code> alpha parameter.</p>
- <h2 class="hasAnchor" id="author"><a class="anchor" href="#author"></a>Author</h2>
-
+ </div>
+ <div id="author">
+ <h2>Author</h2>
<p>Johannes Ranke
- <a href='mailto:jranke@uni-bremen.de'>jranke@uni-bremen.de</a></p>
+ <a href="mailto:jranke@uni-bremen.de">jranke@uni-bremen.de</a></p>
+ </div>
- <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
- <pre class="examples"><div class='input'><span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>massart97ex3</span><span class='op'>)</span>
-<span class='va'>m</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/stats/lm.html'>lm</a></span><span class='op'>(</span><span class='va'>y</span> <span class='op'>~</span> <span class='va'>x</span>, data <span class='op'>=</span> <span class='va'>massart97ex3</span><span class='op'>)</span>
-<span class='fu'>calplot</span><span class='op'>(</span><span class='va'>m</span><span class='op'>)</span>
-</div><div class='img'><img src='calplot.lm-1.png' alt='' width='700' height='433' /></div></pre>
+ <div id="ref-examples">
+ <h2>Examples</h2>
+ <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">massart97ex3</span><span class="op">)</span></span>
+<span class="r-in"><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/lm.html" class="external-link">lm</a></span><span class="op">(</span><span class="va">y</span> <span class="op">~</span> <span class="va">x</span>, data <span class="op">=</span> <span class="va">massart97ex3</span><span class="op">)</span></span>
+<span class="r-in"><span class="fu">calplot</span><span class="op">(</span><span class="va">m</span><span class="op">)</span></span>
+<span class="r-plt img"><img src="calplot.lm-1.png" alt="" width="700" height="433"></span>
+</code></pre></div>
+ </div>
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