Perform numerical calculations for the ssanova
and
gssanova
suites.
sspreg1(s, r, q, y, wt, method, alpha, varht, random)
mspreg1(s, r, id.basis, y, wt, method, alpha, varht, random, skip.iter)
ngreg1(family, s, r, id.basis, y, wt, offset, method, varht, alpha, nu, random, skip.iter)sspreg91(s, r, q, y, cov, method, alpha, varht)
mspreg91(s, r, id.basis, y, cov, method, alpha, varht, skip.iter)
sspngreg(family, s, r, q, y, wt, offset, alpha, nu, random)
mspngreg(family, s, r, id.basis, y, wt, offset, alpha, nu, random, skip.iter)
ngreg(dc, family, sr, q, y, wt, offset, nu, alpha)
regaux(s, r, q, nlambda, fit)
ngreg.proj(dc, family, sr, q, y0, wt, offset, nu)
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"
.
Unpenalized terms evaluated at data points.
Basis of penalized terms evaluated at data points.
Penalty matrix.
Index of observations to be used as "knots."
Response vector.
Model weights.
Input for covariance function for correlated data.
Model offset.
"v"
for GCV, "m"
for GML, or "u"
for Mallows' CL.
Parameter modifying GCV or Mallows' CL scores for smoothing parameter selection.
Optional argument for future support of nbinomial, weibull, lognorm, and loglogis families.
External variance estimate needed for method="u"
.
Input for parametric random effects in nonparametric mixed-effect models.
Flag indicating whether to use initial values of theta and skip theta iteration.
Smoothing parameter in effect.
Fitted model.
Coefficients of fits.
cbind(s,r)
.
Components of the fit to be projected.
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.
regaux
is used by sspreg1
, mspreg1
,
ssngpreg
, and mspngreg
to obtain auxiliary information
needed by predict.ssanova
for standard error calculation.
ngreg.proj
is used by project.gssanova
to
calculate the Kullback-Leibler projection for non-Gaussian
regression.
Gu, C. (1992), Cross validating non Gaussian data. Journal of Computational and Graphical Statistics, 1, 169--179.
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.