https://github.com/cran/fda
Tip revision: 162fdbd4bc36e851c7abd22dd9b35cd24527f8e0 authored by J. O. Ramsay on 03 November 2009, 00:00:00 UTC
version 2.3.2
version 2.3.2
Tip revision: 162fdbd
smooth.bibasis.Rd
\name{smooth.bibasis}
\alias{smooth.bibasis}
\title{
Smooth a discrete surface over a rectangular lattice
}
\description{
Estimate a smoothing function f(s, t) over a rectangular lattice
}
\usage{
smooth.bibasis(sarg, targ, y, fdPars, fdPart, fdnames=NULL, returnMatrix=FALSE)
}
\arguments{
\item{sarg, targ}{
vectors of argument values for the first and second dimensions,
respectively, of the surface function.
}
\item{y}{
an array containing surface values measured with noise
}
\item{fdPars, fdPart}{
functional parameter objects for \code{sarg} and \code{targ},
respectively
}
\item{fdnames}{
a list of length 3 containing character vectors of names for
\code{sarg}, \code{targ}, and the surface function f(s, t).
}
\item{returnMatrix}{
logical: If TRUE, a two-dimensional is returned using a
special class from the Matrix package.
}
}
%\details{}
\value{
a list with the following components:
\item{fdobj}{
a functional data object containing a smooth of the data.
}
\item{df}{
a degrees of freedom measure of the smooth
}
\item{gcv}{
the value of the generalized cross-validation or GCV criterion. If
the function is univariate, GCV is a vector containing the error
sum of squares for each function, and if the function is
multivariate, GCV is a NVAR by NCURVES matrix.
}
\item{coef}{
the coefficient matrix for the basis function expansion of
the smoothing function
}
\item{SSE}{
the error sums of squares. SSE is a vector or a matrix of the same
size as GCV.
}
\item{penmat}{
the penalty matrix.
}
\item{y2cMap}{
the matrix mapping the data to the coefficients.
}
}
\seealso{
\code{\link{smooth.basis}}
}
%\examples{}
% docclass is function
\keyword{smooth}