smooth.fd: Smooth a Functional Data Object Using an Indirectly Specified...

In fda: Functional Data Analysis

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 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

fdobj	a functional data object to be smoothed.
fdParobj	a functional parameter object. This object is defined by a roughness penalty in slot Lfd and a smoothing parameter lambda in slot lambda, and this information is used to further smooth argument fdobj.

Value

a functional data object.

See Also

smooth.basis,

Examples

oldpar <- par(no.readonly=TRUE)
#  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)

#. this code throws an error for. non-cran check
# if (!CRAN()) {
# 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
par(oldpar)

fda documentation built on May 31, 2023, 9:19 p.m.