statsmodels.discrete.count_model.ZeroInflatedGeneralizedPoissonResults

class statsmodels.discrete.count_model.ZeroInflatedGeneralizedPoissonResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None)

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

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)
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