, 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.


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, 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


atol : float, optional

The absolute tolerance criterion that must be satisfied. Defaults to tol. Convergence is attained when: rtol * prior + atol > abs(current - prior)

rtol : float, optional

The relative tolerance criterion that must be satisfied. Defaults to 0 which means rtol is not used. Convergence is attained when: rtol * prior + atol > abs(current - prior)

tol_criterion : str, optional

Defaults to 'deviance'. Can optionally be 'params'.