# NOT RUN {
#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,
breaks=growth$age)
# Smooth the data
# in afda-ch06.R, and register to individual smooths:
cvec0.growth <- matrix(0,nbasis.growth,1)
Wfd0.growth <- fd(cvec0.growth, wbasis.growth)
growfdPar2.3 <- fdPar(Wfd0.growth, Lfdobj.growth, lambda.gr2.3)
hgtmfd.all <- with(growth, smooth.basis(age, hgtm, growfdPar2.3)$fd)
# Register the growth velocity rather than the
# growth curves directly
smBv <- deriv(hgtmfd.all, 1)
##
## 2. Register the first 2 Berkeley boys using the default basis
## for the warping function
##
# register.fd takes time, so use only 2 curves as an illustration
# to minimize compute 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)
# Notice that all the warping functions all have simple shapes
# due to the use of the simplest possible basis
##
## 3. Define a more flexible basis for the warping functions
##
if(!CRAN()){
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)
# 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$regfd)
# plot the old and the new targets
par(mfrow=c(1,1),ask=FALSE)
plot(smBv1)
lines(smBv0, lty=2)
# Notice how the new target (solid line) has sharper features and
# a stronger pubertal growth spurt relative to the old target
# (dashed line). Now register to the new target
smB.reg.2 <- register.fd(smBv1, smBv[1:nBoys], WfdParobj)
plotreg.fd(smB.reg.2)
# Plot the mean of these curves as well as the first and second targets
par(mfrow=c(1,1),ask=FALSE)
plot(mean.fd(smB.reg.2$regfd))
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$regfd, WfdParobj)
plotreg.fd(smB.reg.3)
# Notice that the warping functions only deviate from the straight line
# over the pre-pubertal region, and that there are some small adjustments
# to the registered curves as well over the pre-pubertal region.
##
## 5. register and plot the angular acceleration of the gait data
##
gaittime <- as.numeric(dimnames(gait)[[1]])*20
gaitrange <- c(0,20)
# set up a fourier basis object
gaitbasis <- create.fourier.basis(gaitrange, nbasis=21)
# }
# NOT RUN {
<!-- % set up a functional parameter object penalizing harmonic acceleration -->
# }
# NOT RUN {
harmaccelLfd <- vec2Lfd(c(0, (2*pi/20)^2, 0), rangeval=gaitrange)
gaitfdPar <- fdPar(gaitbasis, harmaccelLfd, 1e-2)
# smooth the data
gaitfd <- smooth.basis(gaittime, gait, gaitfdPar)$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,
periodic=TRUE)
# plot the results
plotreg.fd(gaitreglist)
# display horizonal shift values
print(round(gaitreglist$shift,1))
}
# }
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