# statsmodels.discrete.conditional_models.ConditionalLogit.fit¶

ConditionalLogit.fit(start_params=None, method='BFGS', maxiter=100, full_output=True, disp=False, fargs=(), callback=None, retall=False, skip_hessian=False, **kwargs)

Fit method for likelihood based models

Parameters
start_paramsarray_like, optional

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

methodstr, optional

The method determines which solver from scipy.optimize is used, and it can be chosen from among the following strings:

• ‘newton’ for Newton-Raphson, ‘nm’ for Nelder-Mead

• ‘bfgs’ for Broyden-Fletcher-Goldfarb-Shanno (BFGS)

• ‘lbfgs’ for limited-memory BFGS with optional box constraints

• ‘powell’ for modified Powell’s method

• ‘basinhopping’ for global basin-hopping solver

• ‘minimize’ for generic wrapper of scipy minimize (BFGS by default)

The explicit arguments in fit are passed to the solver, with the exception of the basin-hopping solver. Each solver has several optional arguments that are not the same across solvers. See the notes section below (or scipy.optimize) for the available arguments and for the list of explicit arguments that the basin-hopping solver supports.

maxiterint, optional

The maximum number of iterations to perform.

full_outputbool, optional

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.

dispbool, optional

Set to True to print convergence messages.

fargstuple, optional

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

callbackcallable callback(xk), optional

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

retallbool, optional

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

skip_hessianbool, optional

If False (default), then the negative inverse hessian is calculated after the optimization. If True, then the hessian will not be calculated. However, it will be available in methods that use the hessian in the optimization (currently only with “newton”).

kwargskeywords

All kwargs are passed to the chosen solver with one exception. The following keyword controls what happens after the fit:

warn_convergence : bool, optional
If True, checks the model for the converged flag. If the
converged flag is False, a ConvergenceWarning is issued.


Notes

The ‘basinhopping’ solver ignores maxiter, retall, full_output explicit arguments.

Optional arguments for solvers (see returned Results.mle_settings):

'newton'
tol : float
Relative error in params acceptable for convergence.
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.
'lbfgs'
m : int
This many terms are used for the Hessian approximation.
factr : float
A stop condition that is a variant of relative error.
pgtol : float
A stop condition that uses the projected gradient.
epsilon
If fprime is approximated, use this value for the step
size. Only relevant if LikelihoodModel.score is None.
maxfun : int
Maximum number of function evaluations to make.
bounds : sequence
(min, max) pairs for each element in x,
defining the bounds on that parameter.
Use None for one of min or max when there is no bound
in that direction.
'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.
'basinhopping'
niter : int
The number of basin hopping iterations.
niter_success : int
Stop the run if the global minimum candidate remains the
same for this number of iterations.
T : float
The "temperature" parameter for the accept or reject
criterion. Higher "temperatures" mean that larger jumps
in function value will be accepted. For best results
T should be comparable to the separation (in function
value) between local minima.
stepsize : float
Initial step size for use in the random displacement.
interval : int
The interval for how often to update the stepsize.
minimizer : dict
Extra keyword arguments to be passed to the minimizer
scipy.optimize.minimize(), for example 'method' - the
minimization method (e.g. 'L-BFGS-B'), or 'tol' - the
tolerance for termination. Other arguments are mapped from
explicit argument of fit:
- args <- fargs
- jac <- score
- hess <- hess
'minimize'
min_method : str, optional
Name of minimization method to use.
Any method specific arguments can be passed directly.
For a list of methods and their arguments, see
documentation of scipy.optimize.minimize.
If no method is specified, then BFGS is used.