statsmodels.genmod.families.family.Tweedie

class statsmodels.genmod.families.family.Tweedie(link=None, var_power=1.0, eql=False)

Tweedie family.

Parameters

linka link instance, optional

The default link for the Tweedie family is the log link. Available links are log and Power. See statsmodels.genmod.families.links for more information.

var_powerfloat, optional

The variance power. The default is 1.

eqlbool

If True, the Extended Quasi-Likelihood is used, else the likelihood is used (however the latter is not implemented). If eql is True, var_power must be between 1 and 2.

See also

statsmodels.genmod.families.family.Family

Parent class for all links.

Link Functions

Further details on links.

Notes

Loglikelihood function not implemented because of the complexity of calculating an infinite series of summations. The variance power can be estimated using the estimate_tweedie_power function that is part of the statsmodels.genmod.generalized_linear_model.GLM class.

Attributes

Tweedie.linka link instance

The link function of the Tweedie instance

Tweedie.variancevarfunc instance

variance is an instance of statsmodels.genmod.families.varfuncs.Power

Tweedie.var_powerfloat

The power of the variance function.

Methods

deviance(endog, mu[, var_weights, …])	The deviance function evaluated at (endog, mu, var_weights, freq_weights, scale) for the distribution.
fitted(lin_pred)	Fitted values based on linear predictors lin_pred.
loglike(endog, mu[, var_weights, …])	The log-likelihood function in terms of the fitted mean response.
loglike_obs(endog, mu[, var_weights, scale])	The log-likelihood function for each observation in terms of the fitted mean response for the Tweedie distribution.
predict(mu)	Linear predictors based on given mu values.
resid_anscombe(endog, mu[, var_weights, scale])	The Anscombe residuals
resid_dev(endog, mu[, var_weights, scale])	The deviance residuals
starting_mu(y)	Starting value for mu in the IRLS algorithm.
variance
weights(mu)	Weights for IRLS steps

Methods

deviance(endog, mu[, var_weights, …])	The deviance function evaluated at (endog, mu, var_weights, freq_weights, scale) for the distribution.
fitted(lin_pred)	Fitted values based on linear predictors lin_pred.
loglike(endog, mu[, var_weights, …])	The log-likelihood function in terms of the fitted mean response.
loglike_obs(endog, mu[, var_weights, scale])	The log-likelihood function for each observation in terms of the fitted mean response for the Tweedie distribution.
predict(mu)	Linear predictors based on given mu values.
resid_anscombe(endog, mu[, var_weights, scale])	The Anscombe residuals
resid_dev(endog, mu[, var_weights, scale])	The deviance residuals
starting_mu(y)	Starting value for mu in the IRLS algorithm.
weights(mu)	Weights for IRLS steps

Properties

link	Link function for family
links
safe_links
valid
variance