statsmodels.genmod.families.family.InverseGaussian¶
-
class
statsmodels.genmod.families.family.
InverseGaussian
(link=None)[source]¶ InverseGaussian exponential family.
Parameters: link : a 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.link (a link instance) The link function of the inverse Gaussian instance InverseGaussian.variance (varfunc instance) variance
is an instance of statsmodels.genmod.families.varfuncs.mu_cubedMethods
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