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
 modelLikelihoodModel instance or subclass instance
LikelihoodModelResults holds a reference to the model that is fit.
 params1d array_like
parameter estimates from estimated model
 normalized_cov_params2d array
Normalized (before scaling) covariance of params. (dot(X.T,X))**1
 scalefloat
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’
 foptfloat
The value of the (negative) loglikelihood at its minimum.
 iterationsint
Number of iterations performed.
 scorendarray
The score vector at the optimum.
 Hessianndarray
The Hessian at the optimum.
 warnflagint
1 if maxiter is exceeded. 0 if successful convergence.
 convergedbool
True: converged. False: did not converge.
 allvecslist
List of solutions at each iteration.
 ‘nm’
 foptfloat
The value of the (negative) loglikelihood at its minimum.
 iterationsint
Number of iterations performed.
 warnflagint
1: Maximum number of function evaluations made. 2: Maximum number of iterations reached.
 convergedbool
True: converged. False: did not converge.
 allvecslist
List of solutions at each iteration.
 ‘bfgs’
 foptfloat
Value of the (negative) loglikelihood at its minimum.
 goptfloat
Value of gradient at minimum, which should be near 0.
 Hinvndarray
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.
 fcallsint
Number of calls to loglike.
 gcallsint
Number of calls to gradient/score.
 warnflagint
1: Maximum number of iterations exceeded. 2: Gradient and/or function calls are not changing.
 convergedbool
True: converged. False: did not converge.
 allvecslist
Results at each iteration.
 ‘lbfgs’
 foptfloat
Value of the (negative) loglikelihood at its minimum.
 goptfloat
Value of gradient at minimum, which should be near 0.
 fcallsint
Number of calls to loglike.
 warnflagint
Warning flag:
0 if converged
1 if too many function evaluations or too many iterations
2 if stopped for another reason
 convergedbool
True: converged. False: did not converge.
 ‘powell’
 foptfloat
Value of the (negative) loglikelihood at its minimum.
 direcndarray
Current direction set.
 iterationsint
Number of iterations performed.
 fcallsint
Number of calls to loglike.
 warnflagint
1: Maximum number of function evaluations. 2: Maximum number of iterations.
 convergedbool
True : converged. False: did not converge.
 allvecslist
Results at each iteration.
 ‘cg’
 foptfloat
Value of the (negative) loglikelihood at its minimum.
 fcallsint
Number of calls to loglike.
 gcallsint
Number of calls to gradient/score.
 warnflagint
1: Maximum number of iterations exceeded. 2: Gradient and/ or function calls not changing.
 convergedbool
True: converged. False: did not converge.
 allvecslist
Results at each iteration.
 ‘ncg’
 foptfloat
Value of the (negative) loglikelihood at its minimum.
 fcallsint
Number of calls to loglike.
 gcallsint
Number of calls to gradient/score.
 hcallsint
Number of calls to hessian.
 warnflagint
1: Maximum number of iterations exceeded.
 convergedbool
True: converged. False: did not converge.
 allvecslist
Results at each iteration.
 Attributes
 mle_retvalsdict
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_settingsdict
Contains the arguments passed to the chosen optimization method. Available if the model is fit by maximum likelihood. See LikelihoodModel.fit for more information.
 modelmodel instance
LikelihoodResults contains a reference to the model that is fit.
 paramsndarray
The parameters estimated for the model.
 scalefloat
The scaling factor of the model given during instantiation.
tvalues
arrayReturn the tstatistic for a given parameter estimate.
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 Ftest for a joint linear hypothesis.
initialize
(model, params, **kwd)Initialize (possibly reinitialize) a Results instance.
llf
()Loglikelihood of model
load
(fname)load a pickle, (class method)
See specific model class docstring
predict
([exog, transform])Call self.model.predict with self.params as the first argument.
pvalues
()The twotailed p values for the tstats of the params.
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 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