

   FFaammiillyy OObbjjeeccttss ffoorr MMooddeellss

        family(object)

        binomial(link = "logit")
        gaussian(link ="identity")
        Gamma(link = "inverse")
        inverse.gaussian(link = "1/mu^2")
        poisson(link = "log")
        quasi(link = "identity", variance = "constant")

        print.family(x, ...)

   AArrgguummeennttss::

       link: a specification for the model link function.  The
             `binomial' family admits the links `logit', `pro-
             bit' and `cloglog' (complementary log-log); the
             `Gamma' family the links `identity', `inverse' and
             `log', the `poisson' family the links `identity'
             `log' and `sqrt', and the `quasi' family the links
             `logit', `probit', `cloglog',  `identity',
             `inverse', `log', `1/mu^2' and `sqrt'.  The func-
             tion `power' can also be used to create a power
             link function for the `quasi' family.

             The other families have only one permissible link
             function: `identity' for the `gaussian' family,
             and `1/mu^2' for the `inverse.gaussian' family.

   variance: for all families, other than `quasi', the variance
             function is determined by the family.  The `quasi'
             family will accept the specifications `constant',
             `mu(1-mu)', `mu', `mu^2' and `mu^3' as variance
             function.

     object: the function `family' accesses the `family'
             objects which are stored within objects created by
             modelling functions (e.g. `glm').

   DDeessccrriippttiioonn::

        Family objects provide a convenient way to specify the
        details of the models used by functions such as `glm'.
        See the documentation for `glm' for the details on how
        such model fitting takes place.

   RReeffeerreenncceess::

        McCullagh P. and J. A. Nelder (1989).  Generalized Lin-
        ear Models.  London: Chapman and Hall.

        Dobson, A. J. (1983).  An Introduction to Statistical
        Modelling.  London: Chapman and Hall.

        Cox, D. R. and E. J. Snell (1981).  Applied Statistics;
        Principles and Examples.  London: Chapman and Hall.

   SSeeee AAllssoo::

        `glm', `power'.

   EExxaammpplleess::

        nf <- gaussian()# Normal family
        nf
        str(nf)# internal STRucture

        gf <- Gamma()
        gf
        str(gf)
        gf$linkinv
        all(1:10 == gf$linkfun(gf$linkinv(1:10)))# is TRUE
        gf$variance(-3:4) #- == (.)^2

