

_F_a_m_i_l_y _O_b_j_e_c_t_s _f_o_r _M_o_d_e_l_s

     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, ...)

_A_r_g_u_m_e_n_t_s:

    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').

_D_e_s_c_r_i_p_t_i_o_n:

     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.

_R_e_f_e_r_e_n_c_e_s:

     McCullagh P. and J. A. Nelder (1989).  Generalized
     Linear 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.

_S_e_e _A_l_s_o:

     `glm', `power'.

_E_x_a_m_p_l_e_s:

     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

