statsmodels.base.optimizer._fit_cg¶

statsmodels.base.optimizer._fit_cg(f, score, start_params, fargs, kwargs, disp=True, maxiter=100, callback=None, retall=False, full_output=True, hess=None)[source]¶

Fit using Conjugate Gradient algorithm.

Parameters:¶

ffunction

Returns negative log likelihood given parameters.

scorefunction

Returns gradient of negative log likelihood with respect to params.

start_paramsarray_like, optional

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

fargstuple

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

kwargsdict[str, Any]

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

dispbool

Set to True to print convergence messages.

maxiterint

The maximum number of iterations to perform.

callbackcallable callback(xk)

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

retallbool

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

full_outputbool

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.

hessstr, optional

Method for computing the Hessian matrix, if applicable.

Returns:¶

xoptndarray

The solution to the objective function

retvalsdict, 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.