rkpk                   package:gss                   R Documentation

_N_u_m_e_r_i_c_a_l _E_n_g_i_n_e _f_o_r _s_s_a_n_o_v_a _a_n_d _g_s_s_a_n_o_v_a

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

     Perform numerical calculations for the 'ssanova' and 'gssanova'
     suites.

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

     sspreg1(s, r, q, y, method, alpha, varht, random)
     mspreg1(s, r, q, y, method, alpha, varht, random)

     sspngreg(family, s, r, q, y, wt, offset, alpha, nu, random)
     mspngreg(family, s, r, q, y, wt, offset, alpha, nu, random)
     ngreg(dc, family, sr, q, y, wt, offset, nu, alpha)

     ngreg.proj(dc, family, sr, q, y0, wt, offset, nu)

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

  family: Description of the error distribution.  Supported are
          exponential families '"binomial"', '"poisson"', '"Gamma"',
          and '"nbinomial"'.  Also supported are accelerated life model
          families '"weibull"', '"lognorm"', and '"loglogis"'.

       s: Unpenalized terms evaluated at data points.

       r: Basis of penalized terms evaluated at data points.

       q: Penalty matrix.

       y: Response vector.

      wt: Model weights.

  offset: Model offset.

  method: '"v"' for GCV, '"m"' for GML, or '"u"' for Mallows' CL.

   alpha: Parameter modifying GCV or Mallows' CL scores for smoothing
          parameter selection.

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

   varht: External variance estimate needed for 'method="u"'.

  random: Input for parametric random effects in nonparametric
          mixed-effect models.

      dc: Coefficients of fits.

      sr: 'cbind(s,r)'.

      y0: Components of the fit to be projected.

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

     'sspreg1' is used by 'ssanova' to compute regression estimates
     with a single smoothing parameter. 'mspreg1' is used by 'ssanova'
     to compute regression estimates with multiple smoothing
     parameters.

     'ssngpreg' is used by 'gssanova' to compute non-Gaussian
     regression estimates with a single smoothing parameter. 
     'mspngreg' is used by 'gssanova' to compute non-Gaussian
     regression estimates with multiple smoothing parameters.  'ngreg'
     is used by 'ssngpreg' and 'mspngreg' to perform Newton iteration
     with fixed smoothing parameters and to calculate cross-validation
     scores on return.

     'ngreg.proj' is used by 'project.gssanova' to calculate the
     Kullback-Leibler projection for non-Gaussian regression.

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

     Kim, Y.-J. and Gu, C. (2004), Smoothing spline Gaussian
     regression: more scalable computation via efficient approximation.
     _Journal of the Royal Statistical Society, Ser. B_, *66*, 337-356.

