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, full_output=True, disp=False, max_start_irls=3, **kwargs) [source]

Fits a generalized linear model for a given family.

Parameters:

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). maxiter : int, optional Default is 100. method : string Default is ‘IRLS’ for iteratively reweighted least squares. Otherwise gradient optimization is used. tol : float Convergence tolerance. Default is 1e-8. 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 cov_type : string The type of parameter estimate covariance matrix to compute. cov_kwds : dict-like Extra arguments for calculating the covariance of the parameter estimates. use_t : bool If True, the Student t-distribution is used for inference. full_output : bool, optional Set to True to have all available output in the Results object’s mle_retvals attribute. The output is dependent on the solver. See LikelihoodModelResults notes section for more information. Not used if methhod is IRLS. disp : bool, optional Set to True to print convergence messages. Not used if method is IRLS. max_start_irls : int The number of IRLS iterations used to obtain starting values for gradient optimization. Only relevant if method is set to something other than ‘IRLS’. If IRLS fitting used, the following additional parameters are available: atol : float, optional The absolute tolerance criterion that must be satisfied. Defaults to tol. Convergence is attained when: rtol : float, optional The relative tolerance criterion that must be satisfied. Defaults to 0 which means rtol is not used. Convergence is attained when: tol_criterion : str, optional Defaults to 'deviance'. Can optionally be 'params'.