Fit method for likelihood based models
Parameters : | start_params : array-like, optional
method : str {‘newton’,’nm’,’bfgs’,’powell’,’cg’, or ‘ncg’}
maxiter : int
full_output : bool
disp : bool
fargs : tuple
callback : callable callback(xk)
retall : bool
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Notes
Optional arguments for the solvers (available in Results.mle_settings):
- ‘newton’
- tol : float
- Relative error in params acceptable for convergence.
- ‘nm’ – Nelder Mead
- xtol : float
- Relative error in params acceptable for convergence
- ftol : float
- Relative error in loglike(params) acceptable for convergence
- maxfun : int
- Maximum number of function evaluations to make.
- ‘bfgs’
- gtol : float
- Stop when norm of gradient is less than gtol.
- norm : float
- Order of norm (np.Inf is max, -np.Inf is min)
- epsilon
- If fprime is approximated, use this value for the step size. Only relevant if LikelihoodModel.score is None.
- ‘cg’
- gtol : float
- Stop when norm of gradient is less than gtol.
- norm : float
- Order of norm (np.Inf is max, -np.Inf is min)
- epsilon : float
- If fprime is approximated, use this value for the step size. Can be scalar or vector. Only relevant if Likelihoodmodel.score is None.
- ‘ncg’
- fhess_p : callable f’(x,*args)
- Function which computes the Hessian of f times an arbitrary vector, p. Should only be supplied if LikelihoodModel.hessian is None.
- avextol : float
- Stop when the average relative error in the minimizer falls below this amount.
- epsilon : float or ndarray
- If fhess is approximated, use this value for the step size. Only relevant if Likelihoodmodel.hessian is None.
- ‘powell’
- xtol : float
- Line-search error tolerance
- ftol : float
- Relative error in loglike(params) for acceptable for convergence.
- maxfun : int
- Maximum number of function evaluations to make.
- start_direc : ndarray
- Initial direction set.