statsmodels.discrete.count_model.ZeroInflatedPoissonResults¶

class statsmodels.discrete.count_model.ZeroInflatedPoissonResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None)[source]

A results class for Zero Inflated 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* aic (float) – Akaike information criterion. -2*(llf - p) where p is the number of regressors including the intercept. bic (float) – Bayesian information criterion. -2*llf + ln(nobs)*p where p is the number of regressors including the intercept. bse (array) – The standard errors of the coefficients. df_resid (float) – See model definition. df_model (float) – See model definition. fitted_values (array) – Linear predictor XB. llf (float) – Value of the loglikelihood llnull (float) – Value of the constant-only loglikelihood llr (float) – Likelihood ratio chi-squared statistic; -2*(llnull - llf) llr_pvalue (float) – The chi-squared probability of getting a log-likelihood ratio statistic greater than llr. llr has a chi-squared distribution with degrees of freedom df_model. prsquared (float) – McFadden’s pseudo-R-squared. 1 - (llf / llnull)

Methods

 aic() bic() 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. fittedvalues() get_margeff([at, method, atexog, dummy, count]) Get marginal effects of the fitted model. initialize(model, params, **kwd) llf() llnull() llr() llr_pvalue() load(fname) load a pickle, (class method) normalized_cov_params() predict([exog, transform]) Call self.model.predict with self.params as the first argument. prsquared() pvalues() remove_data() remove data arrays, all nobs arrays from result and model resid() Residuals resid_response() 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 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

Attributes

 use_t