2.7.1.1.5. statsmodels.base.model.LikelihoodModelResults¶
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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 t-values 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
__init__(model, params[, ...])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 F-test 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 t-test for a each linear hypothesis of the form Rb = q 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. Attributes
use_t