\HeaderA{motors}{Accelerated Life Testing of Motorettes}{motors}
\keyword{datasets}{motors}
\begin{Description}\relax
The \code{motors} 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.
\end{Description}
\begin{Usage}
\begin{verbatim}
motors
\end{verbatim}
\end{Usage}
\begin{Format}\relax
This data frame contains the following columns:
\describe{
\item[\code{temp}] the temperature (degrees C) of the test.

\item[\code{time}] the time in hours to failure or censoring at 8064 hours (= 336 days).

\item[\code{cens}] an indicator variable for death.

}
\end{Format}
\begin{Source}\relax
Kalbfleisch, J. D. and Prentice, R. L. (1980)
\emph{The Statistical Analysis of Failure Time Data.}
New York: Wiley.

taken from

Nelson, W. D. and Hahn, G. J. (1972)
Linear regression of a regression relationship from censored data.
Part 1 -- simple methods and their application.
\emph{Technometrics}, \bold{14}, 247--276.
\end{Source}
\begin{References}\relax
Venables, W. N. and Ripley, B. D. (2002)
\emph{Modern Applied Statistics with S.} Fourth edition.  Springer.
\end{References}
\begin{Examples}
\begin{ExampleCode}
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)) )
\end{ExampleCode}
\end{Examples}

