statsmodels.discrete.discrete_model.Poisson

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

Poisson Model

Parameters:

endogarray_like

A 1-d endogenous response variable. The dependent variable.

exogarray_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.

offsetarray_like

Offset is added to the linear prediction with coefficient equal to 1.

exposurearray_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’.

check_rankbool

Check exog rank to determine model degrees of freedom. Default is True. Setting to False reduces model initialization time when exog.shape[1] is large.

Attributes:

endogndarray

A reference to the endogenous response variable

exogndarray

A reference to the exogenous design.

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
get_distribution(params[, exog, exposure, ...])	Get frozen instance of distribution based on predicted parameters
hessian(params)	Poisson model Hessian matrix of the log-likelihood.
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)	Log-likelihood of Poisson model.
loglikeobs(params)	Log-likelihood for observations of Poisson model.
pdf(X)	Poisson model probability mass function
predict(params[, exog, exposure, offset, ...])	Predict response variable of a 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

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
get_distribution(params[, exog, exposure, ...])	Get frozen instance of distribution based on predicted parameters
hessian(params)	Poisson model Hessian matrix of the log-likelihood.
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)	Log-likelihood of Poisson model.
loglikeobs(params)	Log-likelihood for observations of Poisson model.
pdf(X)	Poisson model probability mass function
predict(params[, exog, exposure, offset, ...])	Predict response variable of a 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

Properties

endog_names	Names of endogenous variables
exog_names	Names of exogenous variables
family