R Dataset / Package boot / nodal
Attachment | Size |
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dataset-47067.csv | 787 bytes |
Documentation |
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On this Picostat.com statistics page, you will find information about the nodal data set which pertains to Nodal Involvement in Prostate Cancer. The nodal data set is found in the boot R package. Try to load the nodal data set in R by issuing the following command at the console data("nodal"). This may load the data into a variable called nodal. If R says the nodal data set is not found, you can try installing the package by issuing this command install.packages("boot") and then attempt to reload the data with library("boot") followed by data("nodal"). 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 nodal 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 nodal R data set. The size of this file is about 787 bytes. Nodal Involvement in Prostate CancerDescriptionThe The treatment strategy for a patient diagnosed with cancer of the prostate depend highly on whether the cancer has spread to the surrounding lymph nodes. It is common to operate on the patient to get samples from the nodes which can then be analysed under a microscope but clearly it would be preferable if an accurate assessment of nodal involvement could be made without surgery. For a sample of 53 prostate cancer patients, a number of possible predictor variables were measured before surgery. The patients then had surgery to determine nodal involvement. It was required to see if nodal involvement could be accurately predicted from the predictor variables and which ones were most important. Usagenodal FormatThis data frame contains the following columns:
SourceThe data were obtained from Brown, B.W. (1980) Prediction analysis for binary data. In Biostatistics Casebook. R.G. Miller, B. Efron, B.W. Brown and L.E. Moses (editors), 3–18. John Wiley. ReferencesDavison, A.C. and Hinkley, D.V. (1997) Bootstrap Methods and Their Application. Cambridge University Press. -- Dataset imported from https://www.r-project.org. |
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