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statsmodels.genmod.families.family.Tweedie

class statsmodels.genmod.families.family.Tweedie(link=None, var_power=1.0, link_power=0)[source]

Tweedie family.

Parameters:

link : a link instance, optional

The default link for the Tweedie family is the log link when the link_power is 0. Otherwise, the power link is default. Available links are log and Power.

var_power : float, optional

The variance power.

link_power : float, optional

The link power.

Notes

Logliklihood 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 GLM class.

Attributes

Tweedie.link a link instance The link function of the Tweedie instance
Tweedie.variance varfunc instance variance is an instance of statsmodels.family.varfuncs.Power
Tweedie.link_power float The power of the link function, or 0 if its a log link.
Tweedie.var_power float The power of the variance function.

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 Tweedie family
resid_dev(endog, mu[, scale]) Tweedie Deviance Residual
starting_mu(y) Starting value for mu in the IRLS algorithm.
variance alias of Power
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 Tweedie family
resid_dev(endog, mu[, scale]) Tweedie Deviance Residual
starting_mu(y) Starting value for mu in the IRLS algorithm.
weights(mu) Weights for IRLS steps

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