class statsmodels.discrete.discrete_model.GeneralizedPoisson(endog, exog, p=1, offset=None, exposure=None, missing='none', **kwargs)[source]

Generalized Poisson model for count data

  • endog (array-like) – 1-d endogenous response variable. The dependent variable.
  • exog (array-like) – A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by the user. See
  • p (scalar) – P denotes parameterizations for GP regression. p=1 for GP-1 and p=2 for GP-2. Default is p=1.
  • offset (array_like) – Offset is added to the linear prediction with coefficient equal to 1.
  • exposure (array_like) – Log(exposure) is added to the linear prediction with coefficient equal to 1.
  • missing (str) – Available options are ‘none’, ‘drop’, and ‘raise’. If ‘none’, no nan checking is done. If ‘drop’, any observations with nans are dropped. If ‘raise’, an error is raised. Default is ‘none.’

A reference to the endogenous response variable


A reference to the exogenous design.



cdf(X) The cumulative distribution function of the model.
cov_params_func_l1(likelihood_model, xopt, …) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit.
fit([start_params, method, maxiter, …]) Fit the model using maximum likelihood.
fit_regularized([start_params, method, …]) Fit the model using a regularized maximum likelihood.
from_formula(formula, data[, subset, drop_cols]) Create a Model from a formula and dataframe.
hessian(params) Generalized Poisson model Hessian matrix of the loglikelihood
information(params) Fisher information matrix of model
initialize() Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model.
loglike(params) Loglikelihood of Generalized Poisson model
loglikeobs(params) Loglikelihood for observations of Generalized Poisson model
pdf(X) The probability density (mass) function of the model.
predict(params[, exog, exposure, offset, which]) Predict response variable of a count model given exogenous variables.
score(params) Score vector of model.


endog_names Names of endogenous variables
exog_names Names of exogenous variables