R Dataset / Package MASS / leuk
Attachment | Size |
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dataset-59866.csv | 612 bytes |
Documentation |
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On this Picostat.com statistics page, you will find information about the leuk data set which pertains to Survival Times and White Blood Counts for Leukaemia Patients. The leuk data set is found in the MASS R package. Try to load the leuk data set in R by issuing the following command at the console data("leuk"). This may load the data into a variable called leuk. If R says the leuk 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 with library("MASS") followed by data("leuk"). Perhaps strangley, if R gives you no output after entering a command, it means the command succeeded. If it succeeded you can see the data by typing leuk at the command-line which should display the entire dataset. 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 leuk R data set. The size of this file is about 612 bytes. Survival Times and White Blood Counts for Leukaemia PatientsDescriptionA data frame of data from 33 leukaemia patients. Usageleuk FormatA data frame with columns:
DetailsSurvival times are given for 33 patients who died from acute myelogenous leukaemia. Also measured was the patient's white blood cell count at the time of diagnosis. The patients were also factored into 2 groups according to the presence or absence of a morphologic characteristic of white blood cells. Patients termed AG positive were identified by the presence of Auer rods and/or significant granulation of the leukaemic cells in the bone marrow at the time of diagnosis. SourceCox, D. R. and Oakes, D. (1984) Analysis of Survival Data. Chapman & Hall, p. 9. Taken from Feigl, P. & Zelen, M. (1965) Estimation of exponential survival probabilities with concomitant information. Biometrics 21, 826–838. ReferencesVenables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer. Exampleslibrary(survival) plot(survfit(Surv(time) ~ ag, data = leuk), lty = 2:3, col = 2:3)# now Cox models leuk.cox <- coxph(Surv(time) ~ ag + log(wbc), leuk) summary(leuk.cox) -- Dataset imported from https://www.r-project.org. |
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