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)[source]¶
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_paramsarray_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)
- dispbool
Set to True to print convergence messages.
- maxiter
int The maximum number of iterations to perform.
- callback
callablecallback(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.
- hess
str,optional Method for computing the Hessian matrix, if applicable.
- f
- Returns: