mkdata.binomial             package:gss             R Documentation

_U_t_i_l_i_t_y _F_u_n_c_t_i_o_n_s _f_o_r _E_r_r_o_r _F_a_m_i_l_i_e_s

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

     Utility functions for fitting Smoothing Spline ANOVA models with
     non-Gaussian responses.

_U_s_a_g_e:

     mkdata.binomial(y, eta, wt, offset)
     dev.resid.binomial(y, eta, wt)
     dev.null.binomial(y, wt, offset)

     mkdata.poisson(y, eta, wt, offset)
     dev.resid.poisson(y, eta, wt)
     dev.null.poisson(y, wt, offset)

     mkdata.Gamma(y, eta, wt, offset)
     dev.resid.Gamma(y, eta, wt)
     dev.null.Gamma(y, wt, offset)

     mkdata.inverse.gaussian(y, eta, wt, offset)
     dev.resid.inverse.gaussian(y, eta, wt)
     dev.null.inverse.gaussian(y, wt, offset)

     mkdata.nbinomial(y, eta, wt, offset, alpha)
     dev.resid.nbinomial(y, eta, wt)
     dev.null.nbinomial(y, wt, offset)

     mkdata.weibull(y, eta, wt, offset, alpha)
     dev.resid.weibull(y, eta, wt, alpha)
     dev.null.weibull(y, wt, offset, alpha)

     mkdata.lognorm(y, eta, wt, offset, alpha)
     dev.resid.lognorm(y, eta, wt, alpha)
     dev.null.lognorm(y, wt, offset, alpha)

     mkdata.loglogis(y, eta, wt, offset, alpha)
     dev.resid.loglogis(y, eta, wt, alpha)
     dev.null.loglogis(y, wt, offset, alpha)

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

       y: Model response.

     eta: Fitted values on link scale.

      wt: Model weights.

  offset: Model offset.

   alpha: Size for nbinomial.  Inverse scale for log life time.

_D_e_t_a_i_l_s:

     These are not to be called by the user.

     `mkdata.x' create the pseudo data to be used in iterated penalized
     least squares fitting.  `dev.resid.x' calculate the deviance
     residuals.  `dev.null.x' calculate the deviance of the constant
     null model.

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

     `gssanova'.

