# statsmodels.genmod.families.family.InverseGaussian¶

class `statsmodels.genmod.families.family.``InverseGaussian`(link=<class 'statsmodels.genmod.families.links.inverse_squared'>)[source]

InverseGaussian exponential family.

Notes

The inverse Guassian distribution is sometimes referred to in the literature as the Wald distribution.

Attributes

 InverseGaussian.link (a link instance) The link function of the inverse Gaussian instance InverseGaussian.variance (varfunc instance) variance is an instance of statsmodels.family.varfuncs.mu_cubed

Methods

 `deviance`(endog, mu[, freq_weights, scale]) Inverse Gaussian 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 inverse Gaussian distribution `resid_dev`(endog, mu[, scale]) Returns the deviance residuals for the inverse Gaussian family. `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]) Inverse Gaussian 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 inverse Gaussian distribution `resid_dev`(endog, mu[, scale]) Returns the deviance residuals for the inverse Gaussian family. `starting_mu`(y) Starting value for mu in the IRLS algorithm. `weights`(mu) Weights for IRLS steps