rkpk                   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 for fitting and
     predicting from Smoothing Spline ANOVA models.

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

     sspreg(s, q, y, method="v", varht=1)
     mspreg(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:

     'sspreg' is used by 'ssanova' to fit Gaussian models with a single
     smoothing parameter.  'mspreg' is used to fit Gaussian models with
     multiple single smoothing parameters.

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

     'getcrdr' and 'getsms' are used by 'predict.ssanova' 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.

