Extracts an S3 family object, which (at least) the usual elements "family", "link", "linkfun", "linkinv", "variance", "mu.eta", "dev.resids", plus the additional elements: "phi" which includes just the dispersion parameter to be used, and "simulate" which can generate random variates for a given linear predictor and weight vector (this function of course also uses the "phi" value)
getFamily(family, phi)the family argument passed to glmBayesMfp
the dispersion argument passed to glmBayesMfp
The returned family object also includes a custom ‘init’ function,
which takes the response vector (or matrix) (‘y’) and the corresponding weight vector
(‘weights’), processes them to response vector ‘y’ and possibly altered weights
‘weights’, and includes starting values ‘mustart’ for the IWLS algorithm.
For example, here the binomial special case of two-column-response matrix is treated exactly in
the same way as in glm.