drkpk.Rd
\name{drkpk}
\alias{sspdsty}
\alias{mspdsty}
\alias{sspdsty1}
\alias{mspdsty1}
\alias{mspcdsty}
\alias{mspcdsty1}
\alias{msphzd}
\alias{msphzd1}
\alias{sspcox}
\alias{mspcox}
\alias{mspllrm}
\title{Numerical Engine for ssden, sshzd, and sshzd1}
\description{
Perform numerical calculations for the \code{\link{ssden}} and
\code{\link{sshzd}} suites.
}
\usage{
sspdsty(s, r, q, cnt, qd.s, qd.r, qd.wt, prec, maxiter, alpha, bias)
mspdsty(s, r, id.basis, cnt, qd.s, qd.r, qd.wt, prec, maxiter, alpha,
bias, skip.iter)
sspdsty1(s, r, q, cnt, int, prec, maxiter, alpha)
mspdsty1(s, r, id.basis, cnt, int, prec, maxiter, alpha)
mspcdsty(s, r, id.basis, cnt, qd.s, qd.r, xx.wt, qd.wt, prec, maxiter, alpha, skip.iter)
mspcdsty1(s, r, id.basis, cnt, int.s, int.r, prec, maxiter, alpha, skip.iter)
msphzd(s, r, id.wk, Nobs, cnt, qd.s, qd.r, qd.wt, random, prec, maxiter, alpha, skip.iter)
msphzd1(s, r, id.wk, Nobs, cnt, int.s, int.r, rho, random, prec, maxiter, alpha,
skip.iter)
sspcox(s, r, q, cnt, qd.s, qd.r, qd.wt, prec, maxiter, alpha, random, bias)
mspcox(s, r, id.basis, cnt, qd.s, qd.r, qd.wt, prec, maxiter, alpha, random, bias,
skip.iter)
mspllrm(s, r, id.basis, cnt, qd.s, qd.r, xx.wt, qd.wt, random, prec, maxiter, alpha,
skip.iter)
}
\details{
\code{sspdsty} is used by \code{\link{ssden}} to compute
cross-validated density estimate with a single smoothing
parameter. \code{mspdsty} is used by \code{\link{ssden}} to compute
cross-validated density estimate with multiple smoothing
parameters.
\code{msphzd} is used by \code{\link{sshzd}} to compute
cross-validated hazard estimate with single or multiple smoothing
parameters.
}
\arguments{
\item{s}{Unpenalized terms evaluated at data points.}
\item{r}{Basis of penalized terms evaluated at data points.}
\item{q}{Penalty matrix.}
\item{id.basis}{Index of observations to be used as "knots."}
\item{id.wk}{Index of observations to be used as "knots."}
\item{Nobs}{Total number of lifetime observations.}
\item{cnt}{Bin-counts for histogram data.}
\item{qd.s}{Unpenalized terms evaluated at quadrature nodes.}
\item{qd.r}{Basis of penalized terms evaluated at quadrature nodes.}
\item{qd.wt}{Quadrature weights.}
\item{prec}{Precision requirement for internal iterations.}
\item{maxiter}{Maximum number of iterations allowed for
internal iterations.}
\item{alpha}{Parameter defining cross-validation score for smoothing
parameter selection.}
\item{bias}{List of arrays incorporating possible sampling bias.}
\item{skip.iter}{Flag indicating whether to use initial values of
theta and skip theta iteration.}
\item{int}{Integrals of basis terms.}
\item{int.s}{Integrals of unpenalized terms.}
\item{int.r}{Integrals of basis of penalized terms.}
\item{rho}{rho function value on failure times.}
\item{xx.wt}{Weights at unique x.}
\item{random}{Input for parametric random effects in nonparametric
mixed-effect models.}
}
\references{
Du, P. and Gu, C. (2006), Penalized likelihood hazard estimation:
efficient approximation and Bayesian confidence intervals.
\emph{Statistics and Probability Letters}, \bold{76}, 244--254.
Du, P. and Gu, C. (2009), Penalized Pseudo-Likelihood Hazard
Estimation: A Fast Alternative to Penalized Likelihood.
\emph{Journal of Statistical Planning and Inference}, \bold{139},
891--899.
Gu, C. (2013), \emph{Smoothing Spline ANOVA Models (2nd Ed)}. New
York: Springer-Verlag.
Gu, C. and Wang, J. (2003), Penalized likelihood density
estimation: Direct cross-validation and scalable approximation.
\emph{Statistica Sinica}, \bold{13}, 811--826.
}
\keyword{internal}