A simple wrapper class to estimate a Rare Event Logit model of King and Zeng (2001) in Python.
- This module is provided 'as is' and is prone to errors.
– In a terminal window install the requirements as:
pip install -r requirements.txt
– In Python environment import the relogit module as:
from relogit import relogit
– Specify the function using the following variables:
Y : array_like
A 1-d endogenous response variable. See statsmodels guidance.
X : array_like
A nobs x k array where nobs is the number of observations and k is
the number of regressors. An intercept is added by setting add_const
to True.
add_const : Boolean, optional
Whether to add a constant into X. The default is False.
disp : Boolean, optional
Whether to display details for fitting. The default is False.
See statsmodels guidance
– Train a RE-Logit model by
relogit_model=relogit(Y, X, *optional keywords*)
– Get estimations of unbiased probability predicted_relogit and unbiased coefficients coeffs_unbiased by RE-Logit for a new set of exogenous set X_test as:
predicted_relogit,coeffs_unbiased = relogit_model.predict(X_test)
– Get additional estimations of probability predicted_logit and coefficients coeff_biased by the Logit for the same input X_test as:
predicted_relogit,coeffs_unbiased,predicted_logit,coeff_biased = relogit_model.predict(X_test)
– For more see the accompanying example script vignette.py
The following packages are required to use this module:
- Numpy
- statsmodels