formula support for categorical endog variable in logistic regression
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Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
Research direction
Start by reproducing the two smf.logit formula examples in the issue, comparing the manual dummy-variable form with C(sex) ~ C(smoker) + C(time). Investigate the reported patsy limitation and determine what behavior categorical endogenous variables should have; the work is done when the categorical formula is supported with appropriate regression results and regression coverage.
Written by the indexing model from the issue text.
Description
patsy: '0.5.1'
https://github.com/statsmodels/statsmodels/issues/5552
SM: 0.9.0
For categorical endog variable in logistic regression, I still have to gerneate a dummay variable for it like the following.import pandas as pd import seaborn as sns import numpy as np import statsmodels.formula.api as smf # generate dummy df['male'] = df.sex.map({'Male': 1, 'Female': 0}) # regression formula = 'male ~ C(smoker) + C(time)' model = smf.logit(formula, data=df).fit() model.summary()If I just do
formula = 'C(sex) ~ C(smoker) + C(time)' model = smf.logit(formula, data=df).fit() model.summary()I will get
ValueError: operands could not be broadcast together with shapes (244,2) (244,)This is a little bit weird, since the formula support all categorical variables but the endog. I wonder if this could be a poential feature to imporve.
Btw, is there any current workaround for this issue if I wanna use formula?
@bashtage:
This is a patsy limit. You could just define a function C1
def C1(cat): return pd.get_dummies(cat, drop_first=True)and then use
formula = 'C1(sex) ~ C(smoker) + C(time)'
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