statsmodels.base.model.LikelihoodModelResults¶

class
statsmodels.base.model.
LikelihoodModelResults
(model, params, normalized_cov_params=None, scale=1.0, **kwargs)[source]¶ Class to contain results from likelihood models
Parameters:  model (LikelihoodModel instance or subclass instance) – LikelihoodModelResults holds a reference to the model that is fit.
 params (1d array_like) – parameter estimates from estimated model
 normalized_cov_params (2d array) – Normalized (before scaling) covariance of params. (dot(X.T,X))**1
 scale (float) – For (some subset of models) scale will typically be the mean square error from the estimated model (sigma^2)
Returns:  **Attributes**
 mle_retvals (dict) – Contains the values returned from the chosen optimization method if full_output is True during the fit. Available only if the model is fit by maximum likelihood. See notes below for the output from the different methods.
 mle_settings (dict) – Contains the arguments passed to the chosen optimization method. Available if the model is fit by maximum likelihood. See LikelihoodModel.fit for more information.
 model (model instance) – LikelihoodResults contains a reference to the model that is fit.
 params (ndarray) – The parameters estimated for the model.
 scale (float) – The scaling factor of the model given during instantiation.
 tvalues (array) – The tvalues of the standard errors.
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 summary
()t_test
(r_matrix[, cov_p, scale, use_t])Compute a ttest for a each linear hypothesis of the form Rb = q t_test_pairwise
(term_name[, method, alpha, …])perform pairwise t_test with multiple testing corrected pvalues 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. wald_test_terms
([skip_single, …])Compute a sequence of Wald tests for terms over multiple columns Attributes
use_t