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

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, 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, 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, invcov, use_f, ...])	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.