statsmodels.discrete.discrete_model.NegativeBinomialP

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

Generalized Negative Binomial (NB-P) 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.

pscalar

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. 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’.

check_rankbool

Check exog rank to determine model degrees of freedom. Default is True. Setting to False reduces model initialization time when exog.shape[1] is large.

Attributes:

endogndarray

A reference to the endogenous response variable

exogndarray

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.

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 NegativeBinomialP model.
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, exposure, ...])	Get a frozen instance of distribution Get frozen instance of distribution based on predicted parameters
hessian(params)	Generalized Negative Binomial (NB-P) model Hessian matrix of the log-likelihood.
hessian_factor(params)	Generalized Negative Binomial (NB-P) model Hessian matrix 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)	Log-likelihood of Generalized Negative Binomial (NB-P) model.
loglikeobs(params)	Log-likelihood for observations of Generalized Negative Binomial (NB-P) model.
pdf(X)	The probability density (mass) function of the model.
predict(params[, exog, exposure, offset, ...])	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_factor(params[, endog])	Generalized Negative Binomial (NB-P) model score (gradient) vector of the log-likelihood for each observations.
score_obs(params)	Generalized Negative Binomial (NB-P) model score (gradient) vector of the log-likelihood for each observations.

convert_params

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, ...])	Fit the NegativeBinomialP model.
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, exposure, ...])	Get a frozen instance of distribution Get frozen instance of distribution based on predicted parameters
hessian(params)	Generalized Negative Binomial (NB-P) model Hessian matrix of the log-likelihood.
hessian_factor(params)	Generalized Negative Binomial (NB-P) model Hessian matrix 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)	Log-likelihood of Generalized Negative Binomial (NB-P) model.
loglikeobs(params)	Log-likelihood for observations of Generalized Negative Binomial (NB-P) model.
pdf(X)	The probability density (mass) function of the model.
predict(params[, exog, exposure, offset, ...])	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_factor(params[, endog])	Generalized Negative Binomial (NB-P) model score (gradient) vector of the log-likelihood for each observations.
score_obs(params)	Generalized Negative Binomial (NB-P) model score (gradient) vector of the log-likelihood for each observations.

Properties

endog_names	Names of endogenous variables
exog_names	Names of exogenous variables