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\name{ssd}
\alias{ssd}
\alias{dssd}
\alias{pssd}
\alias{qssd}
\alias{rssd}
\concept{Spline Smoothed Distribution}
\title{Spline Smoothed Distribution}
\description{
Density, distribution function, quantile function and random
generation from smoothing spline estimates.
}
\usage{
dssd(x, param, log = FALSE)
pssd(q, param)
qssd(p, param)
rssd(n, param)
}
\arguments{
\item{x, q}{
a numeric vector of quantiles.
}
\item{p}{
a numeric vector of probabilities.
}
\item{n}{
number of observations.
}
\item{param}{
an object as returned by the function \code{ssdFit}.
}
\item{log}{
a logical flag by default \code{FALSE}.
Should labels and a main title drawn to the plot?
}
}
\details{
\code{dssd} gives the density,
\code{pssd} gives the distribution function,
\code{qssd} gives the quantile function, and
\code{rssd} generates random deviates.
}
\value{
numeric vector
}
\author{
Diethelm Wuertz, Chong Gu for the underlying \code{gss} package.
}
\references{
Gu, C. (2002),
\emph{Smoothing Spline ANOVA Models},
New York Springer--Verlag.
Gu, C. and Wang, J. (2003),
\emph{Penalized likelihood density estimation:
Direct cross-validation and scalable approximation},
Statistica Sinica, 13, 811--826.
}
\examples{
## ssdFit -
set.seed(1953)
r = rnorm(500)
hist(r, breaks = "FD", probability = TRUE,
col = "steelblue", border = "white")
## ssdFit -
param = ssdFit(r)
## dssd -
u = seq(min(r), max(r), len = 301)
v = dssd(u, param)
lines(u, v, col = "orange", lwd = 2)
}
\keyword{distribution}
|
