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<html><head><title>R: Accelerated Life Testing of Motorettes</title>
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<table width="100%" summary="page for motors {MASS}"><tr><td>motors {MASS}</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:
<dl>
<dt><code>temp</code></dt><dd>the temperature (degrees C) of the test
</dd>
<dt><code>time</code></dt><dd>the time in hours to failure or censoring at 8064 hours (= 336 days).
</dd>
<dt><code>cens</code></dt><dd>an indicator variable for death
</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 – simple methods and their application.
<EM>Technometrics</EM>, <B>14</B>, 247–276.
</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 <- 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 <- 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>
<hr><div align="center">[Package <em>MASS</em> version 7.2-44 <a href="00Index.html">Index]</a></div>
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