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)
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.
Attributes
tvalues
()Return the t-statistic for a given parameter estimate. 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. Methods
bse
()The standard errors of the parameter estimates. 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 F-test for a joint linear hypothesis. initialize
(model, params, **kwd)Initialize (possibly re-initialize) a Results instance. llf
()Log-likelihood of model load
(fname)load a pickle, (class method); use only on trusted files, as unpickling can run arbitrary code. normalized_cov_params
()See specific model class docstring predict
([exog, transform])Call self.model.predict with self.params as the first argument. pvalues
()The two-tailed p values for the t-stats of the params. remove_data
()remove data arrays, all nobs arrays from result and model save
(fname[, remove_data])save a pickle of this instance summary
()Summary t_test
(r_matrix[, cov_p, scale, use_t])Compute a t-test 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 p-values tvalues
()Return the t-statistic for a given parameter estimate. wald_test
(r_matrix[, cov_p, scale, invcov, …])Compute a Wald-test for a joint linear hypothesis. wald_test_terms
([skip_single, …])Compute a sequence of Wald tests for terms over multiple columns