rkpk0                  package:gss                  R Documentation

_I_n_t_e_r_f_a_c_e _t_o _R_K_P_A_C_K

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

     Call RKPACK routines for numerical calculations concerning the
     'ssanova0' and 'gssanova0' suites.

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

     sspreg0(s, q, y, method="v", varht=1)
     mspreg0(s, q, y, method="v", varht=1, prec=1e-7, maxiter=30)
     sspregpoi(family, s, q, y, wt, offset, method="u",
               varht=1, nu, prec=1e-7, maxiter=30)
     mspregpoi(family, s, q, y, wt, offset, method="u",
               varht=1, nu, prec=1e-7, maxiter=30)
     getcrdr(obj, r)
     getsms(obj)

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

       s: Design matrix of unpenalized terms.

       q: Penalty matrices of penalized terms.

       y: Model response.

  method: Method for smoothing parameter selection.

   varht: Assumed dispersion parameter, needed only for 'method="u"'.

    prec: Precision requirement for iterations.

 maxiter: Maximum number of iterations allowed.

  family: Error family.

      wt: Model weights.

  offset: Model offset.

     obj: Object returned from a call to 'sspreg', 'mspreg',
          'sspregpoi', or 'mspregpoi'.

      nu: Optional argument for nbinomial, weibull, lognorm, and
          loglogis families.

       r: Inputs for standard error calculation.

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

     'sspreg0' is used by 'ssanova0' to fit Gaussian models with a
     single smoothing parameter.  'mspreg0' is used to fit Gaussian
     models with multiple smoothing parameters.

     'sspregpoi' is used by 'gssanova0' to fit non Gaussian models with
     a single smoothing parameter.  'mspregpoi' is used to fit non
     Gaussian models with multiple smoothing parameters.

     'getcrdr' and 'getsms' are used by 'predict.ssanova0' to calculate
     standard errors of the fitted terms.

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

     Gu, C. (1989), RKPACK and its applications: Fitting smoothing
     spline models.  In _ASA Proceedings of Statistical Computing
     Section_, pp. 42-51.

     Gu, C. (1992), Cross validating non Gaussian data.  _Journal of
     Computational and Graphical Statistics_, *1*, 169-179.

