statsmodels.base.optimizer._fit_newton¶

statsmodels.base.optimizer._fit_newton(f, score, start_params, fargs, kwargs, disp=True, maxiter=100, callback=None, retall=False, full_output=True, hess=None, ridge_factor=1e-10)[source]¶

Fit using Newton-Raphson algorithm.

Parameters:¶

f : function¶

Returns negative log likelihood given parameters.

score : function¶

Returns gradient of negative log likelihood with respect to params.

start_params : array_like, optional¶

Initial guess of the solution for the loglikelihood maximization. The default is an array of zeros.

fargs : tuple¶

Extra arguments passed to the objective function, i.e. objective(x,*args)

kwargs : dict[str, Any]¶

Extra keyword arguments passed to the objective function, i.e. objective(x,**kwargs)

disp : bool¶

Set to True to print convergence messages.

maxiter : int¶

The maximum number of iterations to perform.

callback : callable callback(xk)¶

Called after each iteration, as callback(xk), where xk is the current parameter vector.

retall : bool¶

Set to True to return list of solutions at each iteration. Available in Results object’s mle_retvals attribute.

full_output : bool¶

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.

hess : str, optional¶

Method for computing the Hessian matrix, if applicable.

ridge_factor : float¶

Regularization factor for Hessian matrix.

Returns:¶

• xopt (ndarray) – The solution to the objective function

• retvals (dict, None) – If full_output is True then this is a dictionary which holds information returned from the solver used. If it is False, this is None.