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April 3, 2017 07:50
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Reproduce Table 2.2 of Applied Logistic Regression (3rd Edition) with Statsmodels
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import pandas as pd | |
import statsmodels.api as sm | |
# glow500.xls at https://www.umass.edu/statdata/statdata/data/glow/index.html | |
xls_file = pd.ExcelFile('glow500.xls') | |
df = xls_file.parse(header=0) | |
rate_dummies = pd.get_dummies(df['RATERISK']) | |
rate_dummies.columns = ['RATERISK1', 'RATERISK2', 'RATERISK3'] | |
y = df['FRACTURE'] | |
X = df.drop(['SUB_ID', 'SITE_ID', 'PHY_ID', 'RATERISK', 'FRACSCORE', 'FRACTURE'], axis=1) | |
X = pd.concat([X, rate_dummies], axis=1) | |
X = X.drop('RATERISK1', axis=1) | |
X = X.drop(['HEIGHT', 'BMI', 'MOMFRAC', 'ARMASSIST', 'SMOKE'], axis=1) | |
X = sm.add_constant(X) | |
result = sm.Logit(y, X).fit(disp=0) | |
print(result.summary()) |
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