View statlib-20050214 lupus (public)

2010-11-06 09:59 by mldata | Version 1 | Rating Empty StarEmpty StarEmpty StarEmpty StarEmpty StarEmpty Star
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Summary

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License
unknown (from Weka repository)
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Tags
arff slurped Weka
Attribute Types
Integer,Floating Point,String
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Original Data Format
arff
Name
lupus
Version mldata
0
Comment

87 persons with lupus nephritis. Followed up 15+ years. 35 deaths. Var = duration of disease. Over 40 baseline variables avaiable from authors. Description : For description of this data set arising from 87 persons with lupus nephritis followed for 15+ years after an initial renal biopsy (the starting point of follow-up) see the introduction to Abrahamowicz, MacKenzie and Esdaile (December 1996 issue). This data set only contains time to death/censoring, indicator, duration and log(1+duration), where duration is the duration of untreated disease prior to biopsy. This variable was the focus in the aforementioned JASA article because it clearly violates the proportionality of hazards assumption. More than 40 other variables acquired at baseline are available from authors. Permission : This data can be freely used for non-commercial purposes and distributed freely. Michal Abrahamowicz, Todd MacKenzie and John Esdaile

Information about the dataset CLASSTYPE: nominal CLASSINDEX: 2

Names
TIME,STATUS,DURATION,LOG(1+DURATION),
Types
  1. numeric
  2. nominal:alive,dead
  3. numeric
  4. numeric
Data (first 10 data points)
    TIME STATUS DURA... LOG(...
    157 alive 1.0 0.69
    268 alive 10.0 2.4
    209 dead 2.0 1.1
    134 alive 0.1 0.1
    21 dead 0.1 0.1
    28 dead 7.0 2.08
    150 alive 25.0 3.26
    221 alive 12.0 2.56
    225 alive 18.0 2.94
    264 alive 2.0 1.1
    ... ... ... ...
Description

A gzip'ed tar containing StatLib datasets (statlib-20050214.tar.gz, 12,785,582 Bytes)

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    Data Source
    http://lib.stat.cmu.edu/datasets/
    Measurement Details
    Usage Scenario
    revision 1
    by mldata on 2010-11-06 09:59

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    This project is supported by PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning)
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