genmod.families.family.NegativeBinomial()
statsmodels.genmod.families.family.NegativeBinomial
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class statsmodels.genmod.families.family.NegativeBinomial(link=<class 'statsmodels.genmod.families.links.log'>, alpha=1.0)
[source] -
Negative Binomial exponential family.
Parameters: link : a link instance, optional
The default link for the negative binomial family is the log link. Available links are log, cloglog, identity, nbinom and power. See statsmodels.family.links for more information.
alpha : float, optional
The ancillary parameter for the negative binomial distribution. For now
alpha
is assumed to be nonstochastic. The default value is 1. Permissible values are usually assumed to be between .01 and 2.Notes
Power link functions are not yet supported.
Parameterization for is
with and .
Attributes
NegativeBinomial.link (a link instance) The link function of the negative binomial instance NegativeBinomial.variance (varfunc instance) variance
is an instance of statsmodels.family.varfuncs.nbinomMethods
deviance
(endog, mu[, freq_weights, scale])Returns the value of the deviance function. fitted
(lin_pred)Fitted values based on linear predictors lin_pred. loglike
(endog, mu[, freq_weights, scale])The log-likelihood function in terms of the fitted mean response. predict
(mu)Linear predictors based on given mu values. resid_anscombe
(endog, mu)The Anscombe residuals for the negative binomial family resid_dev
(endog, mu[, scale])Negative Binomial Deviance Residual starting_mu
(y)Starting value for mu in the IRLS algorithm. variance
weights
(mu)Weights for IRLS steps Methods
deviance
(endog, mu[, freq_weights, scale])Returns the value of the deviance function. fitted
(lin_pred)Fitted values based on linear predictors lin_pred. loglike
(endog, mu[, freq_weights, scale])The log-likelihood function in terms of the fitted mean response. predict
(mu)Linear predictors based on given mu values. resid_anscombe
(endog, mu)The Anscombe residuals for the negative binomial family resid_dev
(endog, mu[, scale])Negative Binomial Deviance Residual starting_mu
(y)Starting value for mu in the IRLS algorithm. weights
(mu)Weights for IRLS steps
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.genmod.families.family.NegativeBinomial.html