dose.p.html

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<html><head><title>R: Predict Doses for Binomial Assay model</title>
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<table width="100%" summary="page for dose.p {MASS}"><tr><td>dose.p {MASS}</td><td align="right">R Documentation</td></tr></table>
<h2>Predict Doses for Binomial Assay model</h2>


<h3>Description</h3>

<p>
Calibrate binomial assays, generalizing the calculation of LD50.
</p>


<h3>Usage</h3>

<pre>
dose.p(obj, cf = 1:2, p = 0.5)
</pre>


<h3>Arguments</h3>

<table summary="R argblock">
<tr valign="top"><td><code>obj</code></td>
<td>
A fitted model object of class inheriting from <code>"glm"</code>.
</td></tr>
<tr valign="top"><td><code>cf</code></td>
<td>
The terms in the coefficient vector giving the intercept and
coefficient of (log-)dose
</td></tr>
<tr valign="top"><td><code>p</code></td>
<td>
Probabilities at which to predict the dose needed.
</td></tr>
</table>

<h3>Value</h3>

<p>
An object of class <code>"glm.dose"</code> giving the prediction (attribute
<code>"p"</code> and standard error (attribute <code>"SE"</code>) at each response
probability.</p>

<h3>References</h3>

<p>
Venables, W. N. and Ripley, B. D. (2002)
<EM>Modern Applied Statistics with S.</EM>
Springer.
</p>


<h3>Examples</h3>

<pre>
ldose &lt;- rep(0:5, 2)
numdead &lt;- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16)
sex &lt;- factor(rep(c("M", "F"), c(6, 6)))
SF &lt;- cbind(numdead, numalive = 20 - numdead)
budworm.lg0 &lt;- glm(SF ~ sex + ldose - 1, family = binomial)

dose.p(budworm.lg0, cf = c(1,3), p = 1:3/4)
dose.p(update(budworm.lg0, family = binomial(link=probit)),
       cf = c(1,3), p = 1:3/4)
</pre>



<hr><div align="center">[Package <em>MASS</em> version 7.2-44 <a href="00Index.html">Index]</a></div>

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