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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(**kwargs[, start_params, method, ...]) 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
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

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