statsmodels.discrete.discrete_model.GeneralizedPoissonResults

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

A results class for Generalized 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.

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()

lnalpha()

lnalpha_std_err()

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

resid_response()

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