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 for count data

Parameters:
  • endog (array-like) – 1-d endogenous response variable. The dependent variable.
  • exog (array-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_infl (array_like or None) – Explanatory variables for the binary inflation model, i.e. for mixing probability model. If None, then a constant is used.
  • offset (array_like) – Offset is added to the linear prediction with coefficient equal to 1.
  • exposure (array_like) – Log(exposure) is added to the linear prediction with coefficient equal to 1.
  • inflation (string, 'logit' or 'probit') – The model for the zero inflation, either Logit (default) or Probit
  • p (float) – dispersion power parameter for the NegativeBinomialP model. p=1 for ZINB-1 and p=2 for ZINM-2. Default is p=2
  • 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.’
endog

A reference to the endogenous response variable

Type:array
exog

A reference to the exogenous design.

Type:array
exog_infl

A reference to the zero-inflated exogenous design.

Type:array
p

P denotes parametrizations for ZINB regression. p=1 for ZINB-1 and

Type:scalar
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.
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 response variable of a count model given exogenous variables.
score(params) Score vector of model.
score_obs(params) Generic Zero Inflated model score (gradient) vector of the log-likelihood

Attributes

endog_names Names of endogenous variables
exog_names Names of exogenous variables