rkpk1                  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_1 _a_n_d _g_s_s_a_n_o_v_a_1

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

     Calculate penalized least squares regression estimates via the
     normal equation and evaluate the GCV, GML, or Mallows' CL scores,
     as implemented in the RATFOR routine 'reg.r', and minimize the
     cross-validation score using 'nlm'.

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

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

     sspngreg1(family,s,r,q,y,wt,offset,alpha,nu,random)
     mspngreg1(family,s,r,q,y,wt,offset,alpha,nu,random)
     ngreg1(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 'ssanova1' to compute regression estimates
     with a single smoothing parameter. 'mspreg1' is used by 'ssanova1'
     to compute regression estimates with multiple smoothing
     parameters.

     'ssngpreg1' is used by 'gssanova1' to compute non-Gaussian
     regression estimates with a single smoothing parameter. 
     'mspngreg1' is used by 'gssanova1' to compute non-Gaussian
     regression estimates with multiple smoothing parameters.  'ngreg1'
     is used by 'ssngpreg1' and 'mspngreg1' to perform Newton iteration
     with fixed smoothing parameters and to calculate cross-validation
     scores on return.

     'ngreg.proj' is used by 'project.gssanova1' to calculate
     Kullback-Leibler projection for non-Gaussian regression.

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

     Kim, Y.-J. and Gu, C. (2002) _Penalized Least Squares Regression:
     Fast Computation via Efficient Approximation_. Available at <URL:
     http://stat.purdue.edu/~chong/manu.html>.

