EVOLUTION-MANAGER
Edit File: motors.html
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Accelerated Life Testing of Motorettes</title> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <link rel="stylesheet" type="text/css" href="R.css" /> </head><body> <table width="100%" summary="page for motors {MASS}"><tr><td>motors {MASS}</td><td style="text-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 – 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 style="text-align: center;">[Package <em>MASS</em> version 7.3-51.4 <a href="00Index.html">Index</a>]</div> </body></html>