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<table width="100%" summary="page for chlorsulfuron"><tr><td>chlorsulfuron</td><td align="right">R Documentation</td></tr></table>

<h2>Chlorsulfuron Data</h2>

<h3>Description</h3>


<p>The <code>chlorsulfuron</code> data frame has 51 rows and 3 columns.
</p>
<p>Bioassay on the action of the herbicide chlorsulfuron on the callus 
area of colonies of <EM>Brassica napus L</EM>.  The experiment 
consists of 51 measurements for 10 different dose levels.  The 
design is unbalanced: the number of replicates per dose varies from
a minimum of 5 to a maximum of 8.
</p>


<h3>Usage</h3>

<pre>data(chlorsulfuron)</pre>


<h3>Format</h3>


<p>This data frame contains the following columns:
</p>

<dl>
<dt><code>group</code></dt><dd>
<p>indicator variable for each tested dose;
</p>
</dd>    
<dt><code>dose</code></dt><dd>
<p>the tested dose (nmol/l);
</p>
</dd>
<dt><code>area </code></dt><dd>
<p>the callus area (<i>mm^2</i>).
</p>
</dd>
</dl>



<h3>Source</h3>


<p>The data were obtained from
</p>
<p>Seiden, P., Kappel, D. and Streibig, J. C. (1998) Response of
<EM>Brassica napus L.</EM> tissue culture to metsulfuron methyl and
chlorsulfuron.  <EM>Weed Research</EM>, <B>38</B>, 221&ndash;228.  Dataset
C3. 
</p>


<h3>References</h3>


<p>Bellio, R., Jensen, J.E. and Seiden, P. (2000).  Applications of 
likelihood asymptotics for nonlinear regression in herbicide bioassays.
<EM>Biometrics</EM>, <B>56</B>, 1204&ndash;1212.
</p>
<p>Brazzale, A. R. (2000) <EM>Practical Small-Sample Parametric 
Inference</EM>.  Ph.D. Thesis N. 2230, Department of Mathematics, Swiss 
Federal Institute of Technology Lausanne.  Section 5.3, Example 8.
</p>


<h3>Examples</h3>

<pre>
data(chlorsulfuron)
attach(chlorsulfuron)
plot(dose, area, xlab = "tested dose (nmol/l)", 
     ylab = "log callus area (mm^2)", log = "y")
detach()
</pre>


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