statsmodels.discrete.count_model.ZeroInflatedNegativeBinomialResults

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