family                  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)
     cv.binomial(y, eta, wt, hat, alpha)
     y0.binomial(y, eta0, wt)
     proj0.binomial(y0, eta, offset)
     kl.binomial(eta0, eta1, wt)
     cfit.binomial(y, wt, offset)

     mkdata.poisson(y, eta, wt, offset)
     dev.resid.poisson(y, eta, wt)
     dev.null.poisson(y, wt, offset)
     cv.poisson(y, eta, wt, hat, alpha, sr, q)
     y0.poisson(eta0)
     proj0.poisson(y0, eta, wt, offset)
     kl.poisson(eta0, eta1, wt)
     cfit.poisson(y, wt, offset)

     mkdata.Gamma(y, eta, wt, offset)
     dev.resid.Gamma(y, eta, wt)
     dev.null.Gamma(y, wt, offset)
     cv.Gamma(y, eta, wt, hat, rss, alpha)
     y0.Gamma(eta0)
     proj0.Gamma(y0, eta, wt, offset)
     kl.Gamma(eta0, eta1, wt)
     cfit.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, nu)
     dev.resid.nbinomial(y, eta, wt)
     dev.null.nbinomial(y, wt, offset)
     cv.nbinomial(y, eta, wt, hat, alpha)
     y0.nbinomial(y,eta0,nu)
     proj0.nbinomial(y0, eta, wt, offset)
     kl.nbinomial(eta0, eta1, wt, nu)
     cfit.nbinomial(y, wt, offset, nu)

     mkdata.weibull(y, eta, wt, offset, nu)
     dev.resid.weibull(y, eta, wt, nu)
     dev.null.weibull(y, wt, offset, nu)
     cv.weibull(y, eta, wt, hat, nu, alpha)
     y0.weibull(y, eta0, nu)
     proj0.weibull(y0, eta, wt, offset, nu)
     kl.weibull(eta0, eta1, wt, nu, int)
     cfit.weibull(y, wt, offset, nu)

     mkdata.lognorm(y, eta, wt, offset, nu)
     dev.resid.lognorm(y, eta, wt, nu)
     dev0.resid.lognorm(y, eta, wt, nu)
     dev.null.lognorm(y, wt, offset, nu)
     cv.lognorm(y, eta, wt, hat, nu, alpha)
     y0.lognorm(y, eta0, nu)
     proj0.lognorm(y0, eta, wt, offset, nu)
     kl.lognorm(eta0, eta1, wt, nu, y0)
     cfit.lognorm(y, wt, offset, nu)

     mkdata.loglogis(y, eta, wt, offset, nu)
     dev.resid.loglogis(y, eta, wt, nu)
     dev0.resid.loglogis(y, eta, wt, nu)
     dev.null.loglogis(y, wt, offset, nu)
     cv.loglogis(y, eta, wt, hat, nu, alpha)
     y0.loglogis(y, eta0, nu)
     proj0.loglogis(y0, eta, wt, offset, nu)
     kl.loglogis(eta0, eta1, wt, nu, y0)
     cfit.loglogis(y, wt, offset, nu)

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

       y: Model response.

     eta: Fitted values on link scale.

      wt: Model weights.

  offset: Model offset.

      nu: 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'.

