https://github.com/cran/fda
Tip revision: 877347f65135b34c238ac4f27de992aa38d15347 authored by J. O. Ramsay on 03 November 2009, 00:00:00 UTC
version 2.4.0
version 2.4.0
Tip revision: 877347f
eval.fd.Rd
\name{eval.fd}
\alias{eval.fd}
\alias{predict.fd}
\alias{predict.fdPar}
\alias{predict.fdSmooth}
\alias{fitted.fdSmooth}
\alias{residuals.fdSmooth}
\title{
Values of a Functional Data Object
}
\description{
Evaluate a functional data object at specified argument values, or
evaluate a derivative or the result of applying a linear differential
operator to the functional object.
}
\usage{
eval.fd(evalarg, fdobj, Lfdobj=0, returnMatrix=FALSE)
\method{predict}{fd}(object, newdata=NULL, Lfdobj=0, returnMatrix=FALSE,
...)
\method{predict}{fdPar}(object, newdata=NULL, Lfdobj=0,
returnMatrix=FALSE, ...)
\method{predict}{fdSmooth}(object, newdata=NULL, Lfdobj=0,
returnMatrix=FALSE, ...)
\method{fitted}{fdSmooth}(object, returnMatrix=FALSE, ...)
\method{residuals}{fdSmooth}(object, returnMatrix=FALSE, ...)
}
\arguments{
\item{evalarg, newdata}{
a vector or matrix of argument values at which the functional data
object is to be evaluated. If a matrix with more than one column,
the number of columns must match ncol(dfobj[['coefs']]).
}
\item{fdobj}{
a functional data object to be evaluated.
}
\item{Lfdobj}{
either a nonnegative integer or a linear differential operator
object. If present, the derivative or the value of applying the
operator is evaluated rather than the functions themselves.
}
\item{object}{
an object of class \code{fd}
}
\item{returnMatrix}{
logical: Should a 2-dimensional array to be returned using a
special class from the Matrix package if appropriate?
}
\item{\dots}{
optional arguments for \code{predict}, not currently used
}
}
\details{
\code{eval.fd} evaluates \code{Lfdobj} of \code{fdobj} at
\code{evalarg}.
\code{predict.fd} is a convenience wrapper for
\code{eval.fd}. If \code{newdata} is NULL and
\code{fdobj[['basis']][['type']]} is \code{bspline}, \code{newdata} =
\code{unique(knots(fdojb,interior=FALSE))}; otherwise, \code{newdata}
= \code{fdobj[['basis']][['rangeval']]}.
\code{predict.fdSmooth}, \code{fitted.fdSmooth} and
\code{residuals.fdSmooth} are other wrappers for \code{eval.fd}.
}
\value{
an array of 2 or 3 dimensions containing the function
values. The first dimension corresponds to the argument values in
\code{evalarg},
the second to replications, and the third if present to functions.
}
\author{
Soren Hosgaard wrote an initial version of \code{predict.fdSmooth},
\code{fitted.fdSmooth}, and \code{residuals.fdSmooth}.
}
\seealso{
\code{\link{getbasismatrix}},
\code{\link{eval.bifd}},
\code{\link{eval.penalty}},
\code{\link{eval.monfd}},
\code{\link{eval.posfd}}
}
\examples{
##
## eval.fd
##
# set up the fourier basis
daybasis <- create.fourier.basis(c(0, 365), nbasis=65)
# Make temperature fd object
# Temperature data are in 12 by 365 matrix tempav
# See analyses of weather data.
# Set up sampling points at mid days
# Convert the data to a functional data object
tempfd <- smooth.basis(day.5, CanadianWeather$dailyAv[,,"Temperature.C"],
daybasis)$fd
# set up the harmonic acceleration operator
Lbasis <- create.constant.basis(c(0, 365))
Lcoef <- matrix(c(0,(2*pi/365)^2,0),1,3)
bfdobj <- fd(Lcoef,Lbasis)
bwtlist <- fd2list(bfdobj)
harmaccelLfd <- Lfd(3, bwtlist)
# evaluate the value of the harmonic acceleration
# operator at the sampling points
Ltempmat <- eval.fd(day.5, tempfd, harmaccelLfd)
# Confirm that it still works with
# evalarg = a matrix with only one column
# when fdobj[['coefs']] is a matrix with multiple columns
Ltempmat. <- eval.fd(matrix(day.5, ncol=1), tempfd, harmaccelLfd)
# confirm that the two answers are the same
\dontshow{stopifnot(}
all.equal(Ltempmat, Ltempmat.)
\dontshow{)}
# Plot the values of this operator
matplot(day.5, Ltempmat, type="l")
##
## predict.fd
##
predict(tempfd) # end points only at 35 locations
str(predict(tempfd, day.5)) # 365 x 35 matrix
str(predict(tempfd, day.5, harmaccelLfd))
# cublic splie with knots at 0, .5, 1
bspl3 <- create.bspline.basis(c(0, .5, 1))
plot(bspl3) # 5 bases
fd.bspl3 <- fd(c(0, 0, 1, 0, 0), bspl3)
pred3 <- predict(fd.bspl3)
pred3. <- matrix(c(0, .5, 0), 3)
dimnames(pred3.) <- list(NULL, 'reps 1')
\dontshow{stopifnot(}
all.equal(pred3, pred3.)
\dontshow{)}
pred.2 <- predict(fd.bspl3, c(.2, .8))
pred.2. <- matrix(.176, 2, 1)
dimnames(pred.2.) <- list(NULL, 'reps 1')
\dontshow{stopifnot(}
all.equal(pred.2, pred.2.)
\dontshow{)}
##
## predict.fdSmooth
##
lipSm9 <- smooth.basisPar(liptime, lip, lambda=1e-9)$fd
plot(lipSm9)
##
## with evalarg of class Date and POSIXct
##
# Date
July4.1776 <- as.Date('1776-07-04')
Apr30.1789 <- as.Date('1789-04-30')
AmRev <- c(July4.1776, Apr30.1789)
BspRevolution <- create.bspline.basis(AmRev)
AmRevYears <- seq(July4.1776, Apr30.1789, length.out=14)
(AmRevLinear <- as.numeric(AmRevYears-July4.1776))
fitLin <- smooth.basis(AmRevYears, AmRevLinear, BspRevolution)
AmPred <- predict(fitLin, AmRevYears)
# POSIXct
AmRev.ct <- as.POSIXct1970(c('1776-07-04', '1789-04-30'))
BspRev.ct <- create.bspline.basis(AmRev.ct)
AmRevYrs.ct <- seq(AmRev.ct[1], AmRev.ct[2], length.out=14)
(AmRevLin.ct <- as.numeric(AmRevYrs.ct-AmRev.ct[2]))
fitLin.ct <- smooth.basis(AmRevYrs.ct, AmRevLin.ct, BspRev.ct)
AmPred.ct <- predict(fitLin.ct, AmRevYrs.ct)
}
% docclass is function
\keyword{smooth}