# statsmodels.genmod.families.family.InverseGaussian.loglike¶

InverseGaussian.loglike(endog, mu, var_weights=1.0, freq_weights=1.0, scale=1.0)

The log-likelihood function in terms of the fitted mean response.

Parameters: endog (array) – Usually the endogenous response variable. mu (array) – Usually but not always the fitted mean response variable. var_weights (array-like) – 1d array of variance (analytic) weights. The default is 1. freq_weights (array-like) – 1d array of frequency weights. The default is 1. scale (float) – The scale parameter. The default is 1. ll – The value of the loglikelihood evaluated at (endog, mu, var_weights, freq_weights, scale) as defined below. float

Notes

Where $$ll_i$$ is the by-observation log-likelihood:

$ll = \sum(ll_i * freq\_weights_i)$

ll_i is defined for each family. endog and mu are not restricted to endog and mu respectively. For instance, you could call both loglike(endog, endog) and loglike(endog, mu) to get the log-likelihood ratio.