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pairid lbwt age lastwt race smoke ptd ht ui race1 race2 race3 | |
1 0 14 135 1 0 0 0 0 1 0 0 | |
1 1 14 101 3 1 1 0 0 0 0 1 | |
2 0 15 98 2 0 0 0 0 0 1 0 | |
2 1 15 115 3 0 0 0 1 0 0 1 | |
3 0 16 95 3 0 0 0 0 0 0 1 | |
3 1 16 130 3 0 0 0 0 0 0 1 | |
4 0 17 103 3 0 0 0 0 0 0 1 | |
4 1 17 130 3 1 1 0 1 0 0 1 | |
5 0 17 122 1 1 0 0 0 1 0 0 | |
5 1 17 110 1 1 0 0 0 1 0 0 | |
6 0 17 113 2 0 0 0 0 0 1 0 | |
6 1 17 120 1 1 0 0 0 1 0 0 | |
7 0 17 113 2 0 0 0 0 0 1 0 | |
7 1 17 120 2 0 0 0 0 0 1 0 | |
8 0 17 119 3 0 0 0 0 0 0 1 | |
8 1 17 142 2 0 0 1 0 0 1 0 | |
9 0 18 100 1 1 0 0 0 1 0 0 | |
9 1 18 148 3 0 0 0 0 0 0 1 | |
10 0 18 90 1 1 0 0 1 1 0 0 | |
10 1 18 110 2 1 1 0 0 0 1 0 |
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from lifelines.estimation import CLogit | |
cl = CLogit() | |
df = d.read_csv('https://gist.githubusercontent.com/CamDavidsonPilon/9fe76773a1895a766ccc/raw/779b7ebb4ff61d871d924e588b017b75fbbd8d25/data.tsv', sep="\s+") | |
cl.fit(df[df.columns-['lbwt', 'age', 'race1', 'race']], df['lbwt'], strata=['pairid']) | |
print cl.summary | |
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