https://github.com/cran/gss
Tip revision: 6357a6f4eaa7995257a183215b7f10faaede9208 authored by Chong Gu on 08 August 1977, 00:00:00 UTC
version 0.8-5
version 0.8-5
Tip revision: 6357a6f
drkpk.Rd
\name{drkpk}
\alias{sspdsty}
\alias{mspdsty}
\alias{msphzd}
\title{Numerical Engine for ssden and sshzd}
\description{
Calculate penalized likelihood density estimates and hazard
estimates via the Newton iteration and evaluate the cross-validation
score, as implemented in the RATFOR routine \code{dnewton.r} and
\code{hzdnewton.r}, and minimize the cross-validation score using
\code{\link{nlm}} and \code{\link{nlm0}}.
}
\usage{
sspdsty(s,r,q,cnt,qd.s,qd.r,qd.wt,prec,maxiter,alpha)
mspdsty(s,r,q,cnt,qd.s,qd.r,qd.wt,prec,maxiter,alpha)
msphzd(s,r,q,Nobs,cnt,qd.s,qd.r,qd.wt,prec,maxiter,alpha)
}
\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}{Fixed effects evaluated at data points.}
\item{r}{Basis of random effects evaluated at data points.}
\item{q}{Penalty matrix.}
\item{Nobs}{Total number of lifetime observations.}
\item{cnt}{Bin-counts for histogram data.}
\item{qd.s}{Fixed effects evaluated at quadrature nodes.}
\item{qd.r}{Basis of random effects 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.}
}
\references{
Gu, C. and Wang, J. (2002), \emph{Penalized Likelihood Density
Estimation: Direct Cross-Validation and Scalable Approximation}.
Available at \url{stat.purdue.edu/~chong/manu.html}.
Gu, C. (2002), \emph{Smoothing Spline ANOVA Models}. New York:
Springer-Verlag.
}
\keyword{smooth}