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

<h2>
Accelerated Life Testing of Motorettes
</h2>

<h3>Description</h3>


<p>The <code>motors</code> data frame has 40 rows and 3 columns.  It describes an
accelerated life test at each of four temperatures of 10 motorettes,
and has rather discrete times.
</p>


<h3>Usage</h3>

<pre>
motors
</pre>


<h3>Format</h3>


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

<dl>
<dt><code>temp</code></dt><dd>
<p>the temperature (degrees C) of the test.
</p>
</dd>
<dt><code>time</code></dt><dd>
<p>the time in hours to failure or censoring at 8064 hours (= 336 days).
</p>
</dd>
<dt><code>cens</code></dt><dd>
<p>an indicator variable for death.
</p>
</dd>
</dl>



<h3>Source</h3>


<p>Kalbfleisch, J. D. and Prentice, R. L. (1980)
<EM>The Statistical Analysis of Failure Time Data.</EM>
New York: Wiley.
</p>
<p>taken from
</p>
<p>Nelson, W. D. and Hahn, G. J. (1972)
Linear regression of a regression relationship from censored data.
Part 1 &ndash; simple methods and their application.
<EM>Technometrics</EM>, <B>14</B>, 247&ndash;276.
</p>


<h3>References</h3>


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


<h3>Examples</h3>

<pre>
library(survival)
plot(survfit(Surv(time, cens) ~ factor(temp), motors), conf.int = FALSE)
# fit Weibull model
motor.wei &lt;- survreg(Surv(time, cens) ~ temp, motors)
summary(motor.wei)
# and predict at 130C
unlist(predict(motor.wei, data.frame(temp=130), se.fit = TRUE))

motor.cox &lt;- coxph(Surv(time, cens) ~ temp, motors)
summary(motor.cox)
# predict at temperature 200
plot(survfit(motor.cox, newdata = data.frame(temp=200),
     conf.type = "log-log"))
summary( survfit(motor.cox, newdata = data.frame(temp=130)) )
</pre>


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