View statlib-20050214 stanford (public)
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- unknown (from Weka repository)
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- Tags
- arff slurped Weka
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- Original Data Format
- arff
- Name
- stanford
- Version mldata
- 0
- Comment
Here are two versions of the Stanford Heart Transplant data. The first is from Kalbfleisch and Prentice (first edition) while the second is from from Crowley and Hu. The two versions are slightly different, and the X matrix is sufficiently ill-conditioned that the variations make a big difference. Thanks to Terry Therneau for noticing the problems and providing both data sets.
Note: Here the Kalbfleisch and Prentice data is used.
Information about the dataset CLASSTYPE: numeric CLASSINDEX: none specific
- Names
- Patient_id,Year_of_acceptance,Age,Survival_Status,Survival_Time,Prior_Surgery,Transplant_Status,Waiting_Time_for_Transplant,Mismatch_on_alleles,Mismatch_on_antigen,
- Types
- numeric
- numeric
- numeric
- nominal:alive,dead
- numeric
- nominal:no,yes
- nominal:not_transplanted,transplanted
- numeric
- numeric
- numeric
- Data (first 10 data points)
Pati... Year... Age Surv... Surv... Prio... Tran... Wait... Mism... Mism... ... 15 68 53 dead 1 no not_... -2147... -2147... -2147... ... 43 70 43 dead 2 no not_... -2147... -2147... -2147... ... 61 71 52 dead 2 no not_... -2147... -2147... -2147... ... 75 72 52 dead 2 no not_... -2147... -2147... -2147... ... 6 68 54 dead 3 no not_... -2147... -2147... -2147... ... 42 70 36 dead 3 no not_... -2147... -2147... -2147... ... 54 71 47 dead 3 no not_... -2147... -2147... -2147... ... 38 70 41 dead 5 no tran... 5 3 0 ... 85 73 47 dead 5 no not_... -2147... -2147... -2147... ... 2 68 51 dead 6 no not_... -2147... -2147... -2147... ... ... ... ... ... ... ... ... ... ... ... ...
- Description
A gzip'ed tar containing StatLib datasets (statlib-20050214.tar.gz, 12,785,582 Bytes)
- URLs
- (No information yet)
- Publications
- Data Source
- http://lib.stat.cmu.edu/datasets/
- Measurement Details
- Usage Scenario
- revision 1
- by mldata on 2010-11-06 10:00
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Acknowledgements
This project is supported by PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning)
http://www.pascal-network.org/.