statsmodels.discrete.count_model.ZeroInflatedNegativeBinomialResults¶
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class
statsmodels.discrete.count_model.
ZeroInflatedNegativeBinomialResults
(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None)[source]¶ A results class for Zero Inflated Genaralized Negative Binomial
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
llf
()Log-likelihood of model df_resid (float) See model definition. df_model (float) See model definition. Methods
aic
()Akaike information criterion. bic
()Bayesian information criterion. 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 F-test for a joint linear hypothesis. fittedvalues
()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. 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. load
(fname)load a pickle, (class method); use only on trusted files, as unpickling can run arbitrary code. normalized_cov_params
()See specific model class docstring predict
([exog, transform])Call self.model.predict with self.params as the first argument. prsquared
()McFadden’s pseudo-R-squared. pvalues
()The two-tailed p values for the t-stats of the params. remove_data
()remove data arrays, all nobs arrays from result and model resid
()Residuals resid_response
()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 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