statsmodels.discrete.count_model.ZeroInflatedGeneralizedPoissonResults¶
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class
statsmodels.discrete.count_model.
ZeroInflatedGeneralizedPoissonResults
(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None)[source]¶ A results class for Zero Inflated Generalized Poisson
Parameters: model : A DiscreteModel instance
params : array_like
The parameters of a fitted model.
hessian : array_like
The hessian of the fitted model.
scale : float
A scale parameter for the covariance matrix.
Attributes
df_resid (float) See model definition. df_model (float) See model definition. llf (float) Value of the loglikelihood Methods
conf_int
([alpha, cols])Construct confidence interval for the fitted parameters. cov_params
([r_matrix, column, scale, cov_p, …])Compute the variance/covariance matrix. f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis. get_margeff
([at, method, atexog, dummy, count])Get marginal effects of the fitted model. initialize
(model, params, **kwargs)Initialize (possibly re-initialize) a Results instance. load
(fname)Load a pickled results instance normalized_cov_params
()See specific model class docstring predict
([exog, transform])Call self.model.predict with self.params as the first argument. remove_data
()Remove data arrays, all nobs arrays from result and model. save
(fname[, remove_data])Save a pickle of this instance. set_null_options
([llnull, attach_results])Set the fit options for the Null (constant-only) model. summary
([yname, xname, title, alpha, yname_list])Summarize the Regression Results. summary2
([yname, xname, title, alpha, …])Experimental function to summarize regression results. 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. 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. Methods
conf_int
([alpha, cols])Construct confidence interval for the fitted parameters. cov_params
([r_matrix, column, scale, cov_p, …])Compute the variance/covariance matrix. f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis. get_margeff
([at, method, atexog, dummy, count])Get marginal effects of the fitted model. initialize
(model, params, **kwargs)Initialize (possibly re-initialize) a Results instance. load
(fname)Load a pickled results instance normalized_cov_params
()See specific model class docstring predict
([exog, transform])Call self.model.predict with self.params as the first argument. remove_data
()Remove data arrays, all nobs arrays from result and model. save
(fname[, remove_data])Save a pickle of this instance. set_null_options
([llnull, attach_results])Set the fit options for the Null (constant-only) model. summary
([yname, xname, title, alpha, yname_list])Summarize the Regression Results. summary2
([yname, xname, title, alpha, …])Experimental function to summarize regression results. 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. 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. Properties
aic
Akaike information criterion. bic
Bayesian information criterion. bse
The standard errors of the parameter estimates. fittedvalues
Linear predictor XB. llf
Log-likelihood of model llnull
Value of the constant-only loglikelihood llr
Likelihood ratio chi-squared statistic; -2*(llnull - llf) llr_pvalue
The chi-squared probability of getting a log-likelihood ratio statistic greater than llr. prsquared
McFadden’s pseudo-R-squared. pvalues
The two-tailed p values for the t-stats of the params. resid
Residuals resid_response
Respnose residuals. tvalues
Return the t-statistic for a given parameter estimate. use_t
Flag indicating to use the Student’s distribution in inference.