Class to contain results from likelihood models
Parameters:  model : LikelihoodModel instance or subclass instance
params : 1d array_like
normalized_cov_params : 2d array
scale : float


Returns:  **Attributes** : mle_retvals : dict
mle_settings : dict
model : model instance
params : ndarray
scale : float
tvalues : array

Notes
The covariance of params is given by scale times normalized_cov_params.
Return values by solver if full_output is True during fit:
 ‘newton’
 fopt : float
 The value of the (negative) loglikelihood at its minimum.
 iterations : int
 Number of iterations performed.
 score : ndarray
 The score vector at the optimum.
 Hessian : ndarray
 The Hessian at the optimum.
 warnflag : int
 1 if maxiter is exceeded. 0 if successful convergence.
 converged : bool
 True: converged. False: did not converge.
 allvecs : list
 List of solutions at each iteration.
 ‘nm’
 fopt : float
 The value of the (negative) loglikelihood at its minimum.
 iterations : int
 Number of iterations performed.
 warnflag : int
 1: Maximum number of function evaluations made. 2: Maximum number of iterations reached.
 converged : bool
 True: converged. False: did not converge.
 allvecs : list
 List of solutions at each iteration.
 ‘bfgs’
 fopt : float
 Value of the (negative) loglikelihood at its minimum.
 gopt : float
 Value of gradient at minimum, which should be near 0.
 Hinv : ndarray
 value of the inverse Hessian matrix at minimum. Note that this is just an approximation and will often be different from the value of the analytic Hessian.
 fcalls : int
 Number of calls to loglike.
 gcalls : int
 Number of calls to gradient/score.
 warnflag : int
 1: Maximum number of iterations exceeded. 2: Gradient and/or function calls are not changing.
 converged : bool
 True: converged. False: did not converge.
 allvecs : list
 Results at each iteration.
 ‘lbfgs’
 fopt : float
 Value of the (negative) loglikelihood at its minimum.
 gopt : float
 Value of gradient at minimum, which should be near 0.
 fcalls : int
 Number of calls to loglike.
 warnflag : int
Warning flag:
 0 if converged
 1 if too many function evaluations or too many iterations
 2 if stopped for another reason
 converged : bool
 True: converged. False: did not converge.
 ‘powell’
 fopt : float
 Value of the (negative) loglikelihood at its minimum.
 direc : ndarray
 Current direction set.
 iterations : int
 Number of iterations performed.
 fcalls : int
 Number of calls to loglike.
 warnflag : int
 1: Maximum number of function evaluations. 2: Maximum number of iterations.
 converged : bool
 True : converged. False: did not converge.
 allvecs : list
 Results at each iteration.
 ‘cg’
 fopt : float
 Value of the (negative) loglikelihood at its minimum.
 fcalls : int
 Number of calls to loglike.
 gcalls : int
 Number of calls to gradient/score.
 warnflag : int
 1: Maximum number of iterations exceeded. 2: Gradient and/ or function calls not changing.
 converged : bool
 True: converged. False: did not converge.
 allvecs : list
 Results at each iteration.
 ‘ncg’
 fopt : float
 Value of the (negative) loglikelihood at its minimum.
 fcalls : int
 Number of calls to loglike.
 gcalls : int
 Number of calls to gradient/score.
 hcalls : int
 Number of calls to hessian.
 warnflag : int
 1: Maximum number of iterations exceeded.
 converged : bool
 True: converged. False: did not converge.
 allvecs : list
 Results at each iteration.
Methods
bse()  
conf_int([alpha, cols, method])  Returns the confidence interval of the fitted parameters. 
cov_params([r_matrix, column, scale, cov_p, ...])  Returns the variance/covariance matrix. 
f_test(r_matrix[, cov_p, scale, invcov])  Compute the Ftest for a joint linear hypothesis. 
initialize(model, params, **kwd)  
llf()  
load(fname)  load a pickle, (class method) 
normalized_cov_params()  
predict([exog, transform])  Call self.model.predict with self.params as the first argument. 
pvalues()  
remove_data()  remove data arrays, all nobs arrays from result and model 
save(fname[, remove_data])  save a pickle of this instance 
t_test(r_matrix[, cov_p, scale, use_t])  Compute a ttest for a each linear hypothesis of the form Rb = q 
tvalues()  Return the tstatistic for a given parameter estimate. 
wald_test(r_matrix[, cov_p, scale, invcov, ...])  Compute a Waldtest for a joint linear hypothesis. 
Attributes
use_t  bool(x) > bool 