statsmodels.discrete.count_model.ZeroInflatedNegativeBinomialP¶

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

Zero Inflated Generalized Negative Binomial 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 the zero inflation, either Logit (default) or Probit

pfloat

dispersion power parameter for the NegativeBinomialP model. p=1 for ZINB-1 and p=2 for ZINM-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 parametrizations for ZINB regression. p=1 for ZINB-1 and

p=2 for ZINB-2. Default is p=2

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 frozen instance of distribution based on predicted parameters.
hessian(params)	Generic Zero Inflated 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.
loglike(params)	Loglikelihood of Generic Zero Inflated model.
loglikeobs(params)	Loglikelihood 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.