statsmodels.discrete.count_model.ZeroInflatedPoissonResults

class statsmodels.discrete.count_model.ZeroInflatedPoissonResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None)[source]

A results class for Zero Inflated Poisson

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

df_resid (float) See model definition.
df_model (float) See model definition.
llf (float) 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, scale, 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, 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.
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.

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, scale, 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, 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.
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.

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.