genmod.families.family.Gamma()

statsmodels.genmod.families.family.Gamma

class statsmodels.genmod.families.family.Gamma(link=<class 'statsmodels.genmod.families.links.inverse_power'>) [source]

Gamma exponential family distribution.

Parameters:	link : a link instance, optional The default link for the Gamma family is the inverse link. Available links are log, identity, and inverse. See statsmodels.family.links for more information.

See also

statsmodels.genmod.families.family.Family, Link Functions

Attributes

Gamma.link	(a link instance) The link function of the Gamma instance
Gamma.variance	(varfunc instance) variance is an instance of statsmodels.family.varfuncs.mu_squared

Methods

deviance(endog, mu[, freq_weights, scale])	Gamma 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 Gamma exponential family distribution
resid_dev(endog, mu[, scale])	Gamma deviance residuals
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])	Gamma 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 Gamma exponential family distribution
resid_dev(endog, mu[, scale])	Gamma deviance residuals
starting_mu(y)	Starting value for mu in the IRLS algorithm.
weights(mu)	Weights for IRLS steps