statsmodels.discrete.count_model.ZeroInflatedGeneralizedPoissonResults¶

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
modelA DiscreteModel instance
paramsarray-like

The parameters of a fitted model.

hessianarray-like

The hessian of the fitted model.

scalefloat

A scale parameter for the covariance matrix.

Attributes
df_residfloat

See model definition.

df_modelfloat

See model definition.

llffloat

Log-likelihood of model

Methods

 Akaike information criterion. Bayesian information criterion. 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. Linear predictor XB. get_margeff([at, method, atexog, dummy, count]) Get marginal effects of the fitted model. initialize(model, params, **kwd) Initialize (possibly re-initialize) a Results instance. Log-likelihood of model Value of the constant-only loglikelihood Likelihood ratio chi-squared statistic; -2*(llnull - llf) The chi-squared probability of getting a log-likelihood ratio statistic greater than llr. 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. McFadden’s pseudo-R-squared. The two-tailed p values for the t-stats of the params. remove data arrays, all nobs arrays from result and model Residuals Respnose residuals. save(fname[, remove_data]) save a pickle of this instance set_null_options([llnull, attach_results]) set fit options for 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 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