statsmodels.discrete.discrete_model.NegativeBinomialResults

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

A results class for NegativeBinomial 1 and 2

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
bic
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.
lnalpha Natural log of alpha
lnalpha_std_err Natural log of standardized error
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.