firthlogist 0.1.2

Python implementation of Logistic Regression with Firth's bias reduction

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.