# statsmodels.discrete.discrete_model.Poisson¶

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

Poisson model for count data

Parameters: 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 statsmodels.tools.add_constant. 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.’
endog

A reference to the endogenous response variable

Type: array
exog

A reference to the exogenous design.

Type: array

Methods

 cdf(X) Poisson model cumulative distribution function 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_constrained(constraints[, start_params]) fit the model subject to linear equality constraints 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) Poisson model Hessian matrix of the loglikelihood hessian_factor(params) Poisson model Hessian factor 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 Poisson model loglikeobs(params) Loglikelihood for observations of Poisson model pdf(X) Poisson model probability mass function predict(params[, exog, exposure, offset, linear]) Predict response variable of a count model given exogenous variables. score(params) Poisson model score (gradient) vector of the log-likelihood score_factor(params) Poisson model score_factor for each observation score_obs(params) Poisson model Jacobian of the log-likelihood for each observation

Attributes

 endog_names Names of endogenous variables exog_names Names of exogenous variables family