Python implementation of Logistic Regression with Firth's bias reduction
Project description
firthlogist
A Python implementation of Logistic Regression with Firth's bias reduction.
WIP!
Installation
pip install firthlogist
Usage
firthlogist follows the sklearn API.
from firthlogist import FirthLogisticRegression
firth = FirthLogisticRegression()
firth.fit(X, y)
coefs = firth.coef_
pvals = firth.pvals_
Parameters
max_iter
: int, default=25
The maximum number of Newton-Raphson iterations.
max_halfstep
: int, default=1000
The maximum number of step-halvings in one Newton-Raphson iteration.
max_stepsize
: int, default=5
The maximum step size - for each coefficient, the step size is forced to be less than max_stepsize.
tol
: float, default=0.0001
Convergence tolerance for stopping.
fit_intercept
: bool, default=True
Specifies if intercept should be added.
skip_lrt
: bool, default=False
If True, p-values will not be calculated. Calculating the p-values can be expensive since the fitting procedure is repeated for each coefficient.
Attributes
bse_
Standard errors of the coefficients.
classes_
A list of the class labels.
coef_
The coefficients of the features.
intercept_
Fitted intercept. If fit_intercept = False
, the intercept is set to zero.
loglik_
Fitted penalized log-likelihood.
n_iter_
Number of Newton-Raphson iterations performed.
pvals_
p-values calculated by penalized likelihood ratio tests.
References
Firth, D (1993). Bias reduction of maximum likelihood estimates. Biometrika 80, 27–38.
Heinze G, Schemper M (2002). A solution to the problem of separation in logistic regression. Statistics in Medicine 21: 2409-2419.
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