R Dataset / Package MASS / motors

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How To Compute the Mean

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How To Create a Plot

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How to Compute the Median

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Embed
<iframe src="https://embed.picostat.com/r-dataset-package-mass-motors.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-69620.csv 449 bytes
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
Dataset Help

On this Picostat.com statistics page, you will find information about the motors data set which pertains to Accelerated Life Testing of Motorettes. The motors data set is found in the MASS R package. You can load the motors data set in R by issuing the following command at the console data("motors"). This will load the data into a variable called motors. If R says the motors data set is not found, you can try installing the package by issuing this command install.packages("MASS") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the motors R data set. The size of this file is about 449 bytes.

Documentation

Accelerated Life Testing of Motorettes

Description

The 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.

Usage

motors

Format

This data frame contains the following columns:

temp

the temperature (degrees C) of the test.

time

the time in hours to failure or censoring at 8064 hours (= 336 days).

cens

an indicator variable for death.

Source

Kalbfleisch, J. D. and Prentice, R. L. (1980) 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. Technometrics, 14, 247–276.

References

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

Examples

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)) )
--

Dataset imported from https://www.r-project.org.

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