Zero Inflated Generalized Negative Binomial Model A 1-d endogenous response variable. The dependent variable. 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 Explanatory variables for the binary inflation model, i.e. for mixing probability model. If None, then a constant is used. Offset is added to the linear prediction
with coefficient equal to 1. Log[exposure] is added to the linear prediction with coefficient equal to 1. The model for the zero inflation, either Logit [default] or Probit dispersion power parameter for the NegativeBinomialP model. p=1 for ZINB-1 and p=2 for ZINM-2. Default is p=2 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’. A reference to the endogenous response variable A reference to the exogenous design.statsmodels.tools.add_constant
.None
float
str
ndarray
ndarray
ndarray
A reference to the zero-inflated exogenous design.
pscalarP denotes parametrizations for ZINB regression. p=1 for ZINB-1 and
p=2 for ZINB-2. Default is p=2Methods
| The cumulative distribution function of the model. |
| Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. |
| Fit the model using maximum likelihood. |
| Fit the model using a regularized maximum likelihood. |
| Create a Model from a formula and dataframe. |
| Get frozen instance of distribution based on predicted parameters. |
| Generic Zero Inflated model Hessian matrix of the loglikelihood |
| Fisher information matrix of model. |
| Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model. |
| Loglikelihood of Generic Zero Inflated model. |
| Loglikelihood for observations of Generic Zero Inflated model. |
| The probability density [mass] function of the model. |
| Predict response variable or other statistic given exogenous variables. |
| Score vector of model. |
| Generic Zero Inflated model score [gradient] vector of the log-likelihood |
Properties