statsmodels.genmod.families.family.InverseGaussian

class statsmodels.genmod.families.family.InverseGaussian(link=None)

InverseGaussian exponential family.

Parameters

linka link instance, optional

The default link for the inverse Gaussian family is the inverse squared link. Available links are inverse_squared, inverse, log, and identity. See statsmodels.genmod.families.links for more information.

See also

statsmodels.genmod.families.family.Family

Parent class for all links.

Link Functions

Further details on links.

Notes

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

Attributes

InverseGaussian.linka link instance

The link function of the inverse Gaussian instance

InverseGaussian.variancevarfunc instance

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

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 Inverse Gaussian 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 Inverse Gaussian 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