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
- Parameters
endog : array_like
A 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 : str
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
- Greene, W. 2008. “Functional forms for the negative 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
(ndarray) A reference to the endogenous response variable
exog
(ndarray) 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 the model using maximum likelihood.
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)The Hessian matrix of the model.
information
(params)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.
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)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 model using maximum likelihood.
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)The Hessian matrix of the model.
information
(params)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.
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)Properties
Names of endogenous variables.
Names of exogenous variables.