register.fd: Register Functional Data Objects Using a Continuous Criterion

View source: R/register.fd.R

register.fdR Documentation

Register Functional Data Objects Using a Continuous Criterion


A function is said to be aligned or registered with a target function if its salient features, such as peaks, valleys and crossings of fixed thresholds, occur at about the same argument values as those of the target. Function register.fd aligns these features by transforming or warping the argument domain of each function in a nonlinear but strictly order-preserving fashion. Multivariate functions may also be registered. If the domain is time, we say that this transformation transforms clock time to system time. The transformation itself is called a warping function.


register.fd(y0fd=NULL, yfd=NULL, WfdParobj=NULL,
            conv=1e-04, iterlim=20, dbglev=1, periodic=FALSE, crit=2)



a functional data object defining one or more target functions for registering the functions in argument yfd. If the functions to be registered are univariate, then y0fd may contain only a single function, or it may contain as many functions as are in yfd. If yfd contains multivariate functions, then y0fd may either as many functions as there are variables in yfd, or it may contain as many functions as are in yfd and these functions must then be multivariate and be of the same dimension as those in yfd.

If yfd is supplied as a named argument and y0fd is not, then y0fd is computed inside the function to be the mean of the functions in yfd.

If the function is called with a single unnamed argument, and there is no other argument that is named as y0fd then this unnamed argument is taken to be actually yfd rather than y0fd, and then also y0fd is computed to be the man of the functions supplied.


a functional data object defining the functions to be registered to target y0fd. The functions may be either univariate or multivariate.

If yfd contains a single multivariate function is to be registered, it essential that the coefficient array for y0fd have class array, have three dimensions, and that its second dimension be of length 1.


a functional parameter object containing either a single function or the same number of functions as are contained in yfd. The coefficients supply the initial values in the estimation of a functions $W(t)$ that defines the warping functions $h(t)$ that register the set of curves. WfdParobj also defines the roughness penalty and smoothing parameter used to control the roughness of $h(t)$.

The basis used for this object must be a B-spline type, and the order of the B-spline basis must be at least 2 (piecewise linear).

If WFDPAROBJ is not supplied, it is constructed from a bspline basis of order 2 with 2 basis functions; that is, a basis for piecewise linear functions. The smoothing parameter lambda for this default is 0.


a criterion for convergence of the iterations.


a limit on the number of iterations.


either 0, 1, or 2. This controls the amount information printed out on each iteration, with 0 implying no output, 1 intermediate output level, and 2 full output. R normally postpones displaying these results until the entire computation is computed, an option that it calls "output buffering." Since the total computation time may be considerable, one may opt for turning this feature off by un-checking this box in the "Misc" menu item in the R Console.


a logical variable: if TRUE, the functions are considered to be periodic, in which case a constant can be added to all argument values after they are warped.


an integer that is either 1 or 2 that indicates the nature of the continuous registration criterion that is used. If 1, the criterion is least squares, and if 2, the criterion is the minimum eigenvalue of a cross-product matrix. In general, criterion 2 is to be preferred.


The warping function that smoothly and monotonically transforms the argument is defined by Wfd is the same as that defines the monotone smoothing function in for function smooth.monotone. See the help file for that function for further details.


a named list of length 4 containing the following components:


A functional data object containing the registered functions.


A functional data object containing the warping functions $h(t)$.


A functional data object containing the functions $h W(t)$ that define the warping functions $h(t)$.


If the functions are periodic, this is a vector of time shifts.


The target function object y0fd.


The function object yfd containing the functions to be registered.


Ramsay, James O., Hooker, Giles, and Graves, Spencer (2009), Functional data analysis with R and Matlab, Springer, New York.

Ramsay, James O., and Silverman, Bernard W. (2005), Functional Data Analysis, 2nd ed., Springer, New York.

Ramsay, James O., and Silverman, Bernard W. (2002), Applied Functional Data Analysis, Springer, New York.

See Also

smooth.monotone, smooth.morph, register.newfd


oldpar <- par(no.readonly=TRUE)
#See the analyses of the growth data for examples.

## 1.  smooth the growth data for the Berkeley boys
# Specify smoothing weight
lambda.gr2.3 <- .03
# Specify what to smooth, namely the rate of change of curvature
Lfdobj.growth    <- 2
# Set up a B-spline basis for smoothing the discrete data
nage <- length(growth$age)
norder.growth <- 6
nbasis.growth <- nage + norder.growth - 2
rng.growth <- range(growth$age)
wbasis.growth <- create.bspline.basis(rangeval=rng.growth,
                   nbasis=nbasis.growth, norder=norder.growth,
# Smooth the data
# in afda-ch06.R, and register to individual smooths:
cvec0.growth <- matrix(0,nbasis.growth,1)
Wfd0.growth  <- fd(cvec0.growth, wbasis.growth)
growfdPar2.3 <- fdPar(Wfd0.growth, Lfdobj.growth, lambda.gr2.3)
hgtmfd.all   <- with(growth, smooth.basis(age, hgtm, growfdPar2.3)$fd)
# Register the growth velocity rather than the
# growth curves directly
smBv <- deriv.fd(hgtmfd.all, 1)

## 2.  Register the first 2 Berkeley boys using the default basis
##     for the warping function
# register.fd takes time, so we use only 2 curves as an illustration
# to minimize computing time in these examples
nBoys <- 2
#  Define the target function as the mean of the first nBoys records
smBv0 = mean.fd(smBv[1:nBoys])
#  Register these curves.  The default choice for the functional
#  parameter object WfdParObj is used.
smB.reg.0 <- register.fd(smBv0, smBv[1:nBoys])
#  plot each curve.  Click on the R Graphics window to show each plot.
#  The left panel contains:
#    -- the unregistered curve (dashed blue line)
#    -- the target function (dashed red line)
#    -- the registered curve (solid blue line)
#  The right panel contains:
#    -- the warping function h(t)
#    -- the linear function corresponding to no warping
#  plotreg.fd(smB.reg.0) # this function has been removed
#  Notice that all the warping functions all have simple shapes
#  due to the use of the simplest possible basis

if (!CRAN()) {
## 3.  Define a more flexible basis for the warping functions
Wnbasis   <- 4
Wbasis    <- create.bspline.basis(rng.growth, Wnbasis)
Wfd0      <- fd(matrix(0,Wnbasis,1),Wbasis)
#  set up the functional parameter object using only
#      a light amount smoothing
WfdParobj <- fdPar(Wfd0, Lfdobj=2, lambda=0.01)
#  register the curves
smB.reg.1 <- register.fd(smBv0, smBv[1:nBoys], WfdParobj)
#. plotreg.fd(smB.reg.1). #. removed
#  Notice that now the warping functions can have more complex shapes

## 4.  Change the target to the mean of the registered functions ...
##     this should provide a better target for registration
smBv1 <- mean.fd(smB.reg.1$regfd)
#  plot the old and the new targets
lines(smBv0, lty=2)
#  Notice how the new target (solid line) has sharper features and
#  a stronger pubertal growth spurt relative to the old target
#  (dashed line).  Now register to the new target
smB.reg.2 <- register.fd(smBv1, smBv[1:nBoys], WfdParobj)
#. plotreg.fd(smB.reg.2). #. removed
#  Plot the mean of these curves as well as the first and second targets
lines(smBv0, lty=2)
lines(smBv1, lty=3)
#  Notice that there is almost no improvement over the age of the
#  pubertal growth spurt, but some further detail added in the
#  pre-pubertal region.  Now register the previously registered
#  functions to the new target.
smB.reg.3 <- register.fd(smBv1, smB.reg.1$regfd, WfdParobj)
#  plotreg.fd(smB.reg.3). function removed
#  Notice that the warping functions only deviate from the straight line
#  over the pre-pubertal region, and that there are some small adjustments
#  to the registered curves as well over the pre-pubertal region.

## 5.  register and plot the angular acceleration of the gait data
gaittime  <- as.matrix(0:19)+0.5
gaitrange <- c(0,20)
#  set up a fourier basis object
gaitbasis <- create.fourier.basis(gaitrange, nbasis=21)
# set up a functional parameter object penalizing harmonic acceleration
harmaccelLfd <- vec2Lfd(c(0, (2*pi/20)^2, 0), rangeval=gaitrange)
gaitfdPar    <- fdPar(fd(matrix(0,gaitbasis$nbasis,1), gaitbasis), 
                      harmaccelLfd, 1e-2)
#  smooth the data
gaitfd <- smooth.basis(gaittime, gait, gaitfdPar)$fd
#  compute the angular acceleration functional data object
D2gaitfd    <- deriv.fd(gaitfd,2)
names(D2gaitfd$fdnames)[[3]] <- "Angular acceleration"
D2gaitfd$fdnames[[3]] <- c("Hip", "Knee")
#  compute the mean angular acceleration functional data object
D2gaitmeanfd  <- mean.fd(D2gaitfd)
names(D2gaitmeanfd$fdnames)[[3]] <- "Mean angular acceleration"
D2gaitmeanfd$fdnames[[3]] <- c("Hip", "Knee")
#  register the functions for the first 2 boys
#  argument periodic = TRUE causes register.fd to estimate a horizontal shift
#  for each curve, which is a possibility when the data are periodic
#  set up the basis for the warping functions
nwbasis   <- 4
wbasis    <- create.bspline.basis(gaitrange,nwbasis,3)
Warpfd    <- fd(matrix(0,nwbasis,nBoys),wbasis)
WarpfdPar <- fdPar(Warpfd)
#  register the functions
gaitreglist <- register.fd(D2gaitmeanfd, D2gaitfd[1:nBoys], WarpfdPar,
#  plot the results
#. plotreg.fd(gaitreglist). function removed
#  display horizonal shift values

fda documentation built on May 29, 2024, 11:26 a.m.