discrete.discrete_model.NegativeBinomial()

statsmodels.discrete.discrete_model.NegativeBinomial

class statsmodels.discrete.discrete_model.NegativeBinomial(endog, exog, loglike_method='nb2', offset=None, exposure=None, missing='none', **kwargs) [source]

Negative Binomial Model for count data

Parameters:

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`. loglike_method : string Log-likelihood type. ?nb2?,?nb1?, or ?geometric?. Fitted value $\mu$ Heterogeneity parameter $\alpha$ nb2: Variance equal to $\mu + \alpha\mu^2$ (most common) nb1: Variance equal to $\mu + \alpha\mu$ geometric: Variance equal to $\mu + \mu^2$ 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 : 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.

loglike_method : string

Log-likelihood type. ?nb2?,?nb1?, or ?geometric?. Fitted value $\mu$ Heterogeneity parameter $\alpha$

nb2: Variance equal to $\mu + \alpha\mu^2$ (most common)
nb1: Variance equal to $\mu + \alpha\mu$
geometric: Variance equal to $\mu + \mu^2$

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.?

References

References:

Greene, W. 2008. ?Functional forms for the negtive binomial model: for count data?. Economics Letters. Volume 99, Number 3, pp.585-590.
Hilbe, J.M. 2011. ?Negative binomial regression?. Cambridge University: Press.

Attributes

endog	array	A reference to the endogenous response variable
exog	array	A reference to the exogenous design.

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_regularized`([start_params, method, ...])
`from_formula`(formula, data[, subset])	Create a Model from a formula and dataframe.
`hessian`(params)	The Hessian matrix of the model
`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.
`jac`(args, *kwds)	`jac` is deprecated, use `score_obs` instead!
`loglike`(params)	Loglikelihood for negative binomial model
`pdf`(X)	The probability density (mass) function of the model.
`predict`(params[, exog, exposure, offset, linear])	Predict response variable of a count model given exogenous variables.
`score`(params)	Score vector of model.
`score_obs`(params)

Attributes

`endog_names`
`exog_names`

Links:

http://statsmodels.sourceforge.net/stable/generated/statsmodels.discrete.discrete_model.NegativeBinomial.html

doc_statsmodels

2017-01-18 16:08:14

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