statsmodels.discrete.discrete_model.NegativeBinomialP

class statsmodels.discrete.discrete_model.NegativeBinomialP(endog, exog, p=2, offset=None, exposure=None, missing='none', **kwargs)

Generalized Negative Binomial (NB-P) model for count data

Parameters

endogarray-like

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.

p: scalar

P denotes parameterizations for NB regression. p=1 for NB-1 and p=2 for NB-2. Default is p=2.

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.

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

———-

endogarray

A reference to the endogenous response variable

exogarray

A reference to the exogenous design.

pscalar

P denotes parameterizations for NB-P regression. p=1 for NB-1 and p=2 for NB-2. Default is p=1.

Attributes

endog_names

Names of endogenous variables

exog_names

Names of exogenous variables

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, …])	use_transparams : bool
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)	Generalized Negative Binomial (NB-P) model hessian maxtrix 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)	Loglikelihood of Generalized Negative Binomial (NB-P) model
loglikeobs(params)	Loglikelihood for observations of Generalized Negative Binomial (NB-P) model
pdf(X)	The probability density (mass) function of the model.
predict(params[, exog, exposure, offset, which])	Predict response variable of a model given exogenous variables.
score(params)	Generalized Negative Binomial (NB-P) model score (gradient) vector of the log-likelihood
score_obs(params)	Generalized Negative Binomial (NB-P) model score (gradient) vector of the log-likelihood for each observations.

convert_params