| rkpk1 {gss} | R Documentation |
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.
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)
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. |
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.
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.