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.
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 function for family