statsmodels.discrete.count_model.ZeroInflatedGeneralizedPoisson

class statsmodels.discrete.count_model.ZeroInflatedGeneralizedPoisson(endog, exog, exog_infl=None, offset=None, exposure=None, inflation='logit', p=2, missing='none', **kwargs)

Zero-Inflated Generalized 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.

exog_inflarray_like or None

Explanatory variables for the binary inflation model, i.e. for mixing probability model. If None, then a constant is used.

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.

inflation{‘logit’, ‘probit’}

The model for zero inflation, either Logit (default) or Probit.

pfloat

dispersion power parameter for the GeneralizedPoisson model. p=1 for ZIGP-1 and p=2 for ZIGP-2. Default is p=2

missingstr

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’.

Attributes:

endogndarray

A reference to the endogenous response variable

exogndarray

A reference to the exogenous design.

exog_inflndarray

A reference to the zero-inflated exogenous design.

pscalar

P denotes parameterizations for ZIGP regression.

Methods

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
get_distribution(params[, exog, exog_infl, ...])	Get a frozen instance of distribution based on predicted parameters
hessian(params)	Generic Zero-Inflated model Hessian matrix of the log-likelihood.
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 Generic Zero-Inflated model.
loglikeobs(params)	Log-likelihood for observations of Generic Zero-Inflated model.
pdf(X)	The probability density (mass) function of the model.
predict(params[, exog, exog_infl, exposure, ...])	Predict expected response or other statistic given exogenous variables.
score(params)	Score vector of model
score_obs(params)	Generic Zero-Inflated model score (gradient) vector of the log-likelihood.

Methods

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
get_distribution(params[, exog, exog_infl, ...])	Get a frozen instance of distribution based on predicted parameters
hessian(params)	Generic Zero-Inflated model Hessian matrix of the log-likelihood.
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 Generic Zero-Inflated model.
loglikeobs(params)	Log-likelihood for observations of Generic Zero-Inflated model.
pdf(X)	The probability density (mass) function of the model.
predict(params[, exog, exog_infl, exposure, ...])	Predict expected response or other statistic given exogenous variables.
score(params)	Score vector of model
score_obs(params)	Generic Zero-Inflated model score (gradient) vector of the log-likelihood.

Properties

endog_names	Names of endogenous variables
exog_names	Names of exogenous variables