Define a Functional Parameter Object
Functional parameter objects are used as arguments to functions that
estimate functional parameters, such as smoothing functions like
smooth.basis. A functional parameter object is a functional
data object with additional slots specifying a roughness penalty, a
smoothing parameter and whether or not the functional parameter is to
be estimated or held fixed. Functional parameter objects are used as
arguments to functions that estimate functional parameters.
fdPar(fdobj=NULL, Lfdobj=NULL, lambda=0, estimate=TRUE,
penmat=NULL)fdobj |
a functional data object, functional basis object, a functional
parameter object or a matrix. If it a matrix, it is replaced by
fd(fdobj). If class(fdobj) == 'basisfd', it is converted to an
object of class |
Lfdobj |
either a nonnegative integer or a linear differential operator object. If
|
lambda |
a nonnegative real number specifying the amount of smoothing to be applied to the estimated functional parameter. |
estimate |
not currently used. |
penmat |
a roughness penalty matrix. Including this can eliminate the need to compute this matrix over and over again in some types of calculations. |
Functional parameters are often needed to specify initial
values for iteratively refined estimates, as is the case in
functions register.fd and smooth.monotone.
Often a list of functional parameters must be supplied to a function
as an argument, and it may be that some of these parameters are
considered known and must remain fixed during the analysis. This is
the case for functions fRegress and pda.fd, for
example.
a functional parameter object (i.e., an object of class fdPar),
which is a list with the following components:
fd |
a functional data object (i.e., with class |
Lfd |
a linear differential operator object (i.e., with class
|
lambda |
a nonnegative real number |
estimate |
not currently used |
penmat |
either NULL or a square, symmetric matrix with penmat[i, j] = integral over fd[['basis']][['rangeval']] of basis[i]*basis[j] |
Ramsay, James O., and Silverman, Bernard W. (2006), Functional Data Analysis, 2nd ed., Springer, New York.
Ramsay, James O., and Silverman, Bernard W. (2002), Applied Functional Data Analysis, Springer, New York
##
## Simple example
##
# set up range for density
rangeval <- c(-3,3)
# set up some standard normal data
x <- rnorm(50)
# make sure values within the range
x[x < -3] <- -2.99
x[x > 3] <- 2.99
# set up basis for W(x)
basisobj <- create.bspline.basis(rangeval, 11)
# set up initial value for Wfdobj
Wfd0 <- fd(matrix(0,11,1), basisobj)
WfdParobj <- fdPar(Wfd0)
WfdP3 <- fdPar(seq(-3, 3, length=11))
##
## smooth the Canadian daily temperature data
##
# set up the fourier basis
nbasis <- 365
dayrange <- c(0,365)
daybasis <- create.fourier.basis(dayrange, nbasis)
dayperiod <- 365
harmaccelLfd <- vec2Lfd(c(0,(2*pi/365)^2,0), dayrange)
# 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
daytime <- (1:365)-0.5
# Convert the data to a functional data object
daybasis65 <- create.fourier.basis(dayrange, nbasis, dayperiod)
templambda <- 1e1
tempfdPar <- fdPar(fdobj=daybasis65, Lfdobj=harmaccelLfd,
lambda=templambda)
#FIXME
#tempfd <- smooth.basis(CanadianWeather$tempav, daytime, tempfdPar)$fd
# Set up the harmonic acceleration operator
Lbasis <- create.constant.basis(dayrange);
Lcoef <- matrix(c(0,(2*pi/365)^2,0),1,3)
bfdobj <- fd(Lcoef,Lbasis)
bwtlist <- fd2list(bfdobj)
harmaccelLfd <- Lfd(3, bwtlist)
# Define the functional parameter object for
# smoothing the temperature data
lambda <- 0.01 # minimum GCV estimate
#tempPar <- fdPar(daybasis365, harmaccelLfd, lambda)
# smooth the data
#tempfd <- smooth.basis(daytime, CanadialWeather$tempav, tempPar)$fd
# plot the temperature curves
#plot(tempfd)
##
## with rangeval of class Date and POSIXct
##Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.