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.
Returns:

  • *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
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