Logo

statsmodels.discrete.discrete_model.Poisson

class statsmodels.discrete.discrete_model.Poisson(endog, exog, offset=None, exposure=None)[source]

Poisson model for count data

Parameters:

endog : array-like

1-d array of the response variable.

exog : array-like

exog is an n x p array where n is the number of observations and p is the number of regressors including the intercept if one is included in the data.

Attributes

endog array A reference to the endogenous response variable
exog array A reference to the exogenous design.

Methods

cdf(X) Poisson model cumulative distribution function
fit([start_params, method, maxiter, ...]) Fit the model using maximum likelihood.
hessian(params) 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.
jac(params) Poisson model Jacobian of the log-likelihood for each observation
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

Attributes

endog_names
exog_names

Previous topic

statsmodels.discrete.discrete_model.MNLogit.score

Next topic

statsmodels.discrete.discrete_model.Poisson.cdf

This Page