statsmodels.base.optimizer._fit_powell

statsmodels.base.optimizer._fit_powell(f, score, start_params, fargs, kwargs, disp=True, maxiter=100, callback=None, retall=False, full_output=True, hess=None)

Fit using Powell’s conjugate direction 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.

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.