\name{fRegress.CV}
\alias{fRegress.CV}
\title{
Computes Cross-validated Error Sum of Integrated Squared Errors for a
Functional Regression Model
}
\description{
For a functional regression model, a cross-validated error sum of
squares is computed. For a functional dependent variable this is the
sum of integrated squared errors. For a scalar response, this function
has been superceded by the OCV and gcv elements returned by
\code{fRegress}. This function aids the choice of smoothing parameters
in this model using the cross-validated error sum of squares
criterion.
}
\usage{
fRegress.CV(y, xfdlist, betalist, wt=NULL, CVobs=1:N, ...)
}
\arguments{
\item{y}{
the dependent variable object.
}
\item{xfdlist}{
a list whose members are functional parameter objects specifying
functional independent variables. Some of these may also be vectors
specifying scalar independent variables.
}
\item{betalist}{
a list containing functional parameter objects specifying the
regression functions and their level of smoothing.
}
\item{wt}{
weights for weighted least squares. Defaults to all 1's.
}
\item{CVobs}{
Indices of observations to be deleted. Defaults to 1:N.
}
\item{\dots}{
optional arguments not used by \code{fRegress.CV} but needed for
superficial compatibability with \code{fRegress} methods.
}
}
\value{
A list containing
\item{SSE.CV}{ The sum of squared errors, or integrated squared errors}
\item{errfd.cv}{ Either a vector or a functional data object giving the
cross-validated errors }
}
\seealso{
\code{\link{fRegress}},
\code{\link{fRegress.stderr}}
}
\examples{
#See the analyses of the Canadian daily weather data.
}
% docclass is function
\keyword{smooth}