statsmodels.discrete.discrete_model.NegativeBinomialP

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

Generalized Negative Binomial (NB-P) 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.

  • p (scalar) – P denotes parameterizations for NB regression. p=1 for NB-1 and p=2 for NB-2. Default is p=2.

  • 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.

  • 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

p

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

Type

scalar

Methods

cdf(X)

The cumulative distribution function of the model.

convert_params(params, mu)

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, …])

param use_transparams

This parameter enable internal transformation to impose non-negativity.

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.

Attributes

endog_names

Names of endogenous variables

exog_names

Names of exogenous variables