swh:1:snp:9523f0d466702d960abd89c52a35d2466e8b9dc4
Tip revision: 0fe7f88c11abe3368cc5d4c4a96db0538b7b4dc2 authored by J. O. Ramsay on 02 August 2020, 11:17:16 UTC
version 5.1.5.1
version 5.1.5.1
Tip revision: 0fe7f88
smooth.fd.Rd
\name{smooth.fd}
\alias{smooth.fd}
\title{
Smooth a Functional Data Object Using an Indirectly Specified
Roughness Penalty
}
\description{
Smooth data already converted to a functional data object, fdobj,
using criteria consolidated in a functional data parameter object,
fdParobj. For example, data may have been converted to a functional
data object using function \code{smooth.basis} using a fairly large set of
basis functions. This 'fdobj' is then smoothed as specified in
'fdParobj'.
}
\usage{
smooth.fd(fdobj, fdParobj)
}
\arguments{
\item{fdobj}{
a functional data object to be smoothed.
}
\item{fdParobj}{
a functional parameter object. This object is defined by a roughness
penalty in slot \code{Lfd} and a smoothing parameter lambda in slot
\code{lambda}, and this information is used to further smooth argument \code{fdobj}.
}
}
\value{
a functional data object.
}
\seealso{
\code{\link{smooth.basis}},
}
\examples{
# Shows the effects of two levels of smoothing
# where the size of the third derivative is penalized.
# The null space contains quadratic functions.
x <- seq(-1,1,0.02)
y <- x + 3*exp(-6*x^2) + rnorm(rep(1,101))*0.2
# set up a saturated B-spline basis
basisobj <- create.bspline.basis(c(-1,1),81)
# convert to a functional data object that interpolates the data.
result <- smooth.basis(x, y, basisobj)
yfd <- result$fd
# set up a functional parameter object with smoothing
# parameter 1e-6 and a penalty on the 3rd derivative.
yfdPar <- fdPar(yfd, 2, 1e-6)
yfd1 <- smooth.fd(yfd, yfdPar)
\dontrun{
# FIXME: using 3rd derivative here gave error?????
yfdPar3 <- fdPar(yfd, 3, 1e-6)
yfd1.3 <- smooth.fd(yfd, yfdPar3)
#Error in bsplinepen(basisobj, Lfdobj, rng) :
# Penalty matrix cannot be evaluated
# for derivative of order 3 for B-splines of order 4
}
# set up a functional parameter object with smoothing
# parameter 1 and a penalty on the 3rd derivative.
yfdPar <- fdPar(yfd, 2, 1)
yfd2 <- smooth.fd(yfd, yfdPar)
# plot the data and smooth
plot(x,y) # plot the data
lines(yfd1, lty=1) # add moderately penalized smooth
lines(yfd2, lty=3) # add heavily penalized smooth
legend(-1,3,c("0.000001","1"),lty=c(1,3))
# plot the data and smoothing using function plotfit.fd
plotfit.fd(y, x, yfd1) # plot data and smooth
}
% docclass is function
\keyword{smooth}