GLM.fit()

statsmodels.genmod.generalized_linear_model.GLM.fit

GLM.fit(start_params=None, maxiter=100, method='IRLS', tol=1e-08, scale=None, cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs) [source]

Fits a generalized linear model for a given family.

Parameters:

maxiter : int, optional Default is 100. method : string Default is ?IRLS? for iteratively reweighted least squares. This is currently the only method available for GLM fit. scale : string or float, optional scale can be ?X2?, ?dev?, or a float The default value is None, which uses X2 for Gamma, Gaussian, and Inverse Gaussian. X2 is Pearson?s chi-square divided by df_resid. The default is 1 for the Binomial and Poisson families. dev is the deviance divided by df_resid tol : float Convergence tolerance. Default is 1e-8. start_params : array-like, optional Initial guess of the solution for the loglikelihood maximization. The default is family-specific and is given by the family.starting_mu(endog). If start_params is given then the initial mean will be calculated as np.dot(exog, start_params).

Notes

This method does not take any extra undocumented kwargs.