statsmodels.genmod.families.family.Gaussian

class statsmodels.genmod.families.family.Gaussian(link=<class 'statsmodels.genmod.families.links.identity'>)[source]

Gaussian exponential family distribution.

Parameters:

link : a link instance, optional

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

See also

statsmodels.genmod.families.family.Family, Link Functions

Attributes

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

Methods

deviance(endog, mu[, freq_weights, scale]) Gaussian deviance function
fitted(lin_pred) Fitted values based on linear predictors lin_pred.
loglike(endog, mu[, freq_weights, scale]) The log-likelihood 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 Gaussian exponential family distribution
resid_dev(endog, mu[, scale]) Gaussian deviance residuals
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]) Gaussian deviance function
fitted(lin_pred) Fitted values based on linear predictors lin_pred.
loglike(endog, mu[, freq_weights, scale]) The log-likelihood 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 Gaussian exponential family distribution
resid_dev(endog, mu[, scale]) Gaussian deviance residuals
starting_mu(y) Starting value for mu in the IRLS algorithm.
weights(mu) Weights for IRLS steps