rkpk1 {gss}R Documentation

Numerical Engine for ssanova1 and gssanova1

Description

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.

Usage

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,fit0,wt,offset,nu)

Arguments

family Description of the error distribution. Supported are exponential families "binomial", "poisson", and "Gamma"
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).
fit0 The fit to be projected.

Details

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.

References

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


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