fda: landmarkreg – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

landmarkreg

Landmark Registration of Functional Observations

Description

It is common to see that among a set of functions certain prominent features such peaks and valleys, called $landmarks$, do not occur at the same times, or other argument values. This is called $phase variation$, and it can be essential to align these features before proceeding with further functional data analyses. This function uses the timings of these features to align or register the curves. The registration involves estimating a nonlinear transformation of the argument continuum for each functional observation. This transformation is called a warping function. It must be strictly increasing and smooth.

Usage

landmarkreg(fdobj, ximarks, x0marks=xmeanmarks,
            WfdPar, monwrd=FALSE, ylambda=1e-10)

Arguments

`fdobj`	a functional data object containing the curves to be registered.
`ximarks`	a matrix containing the timings or argument values associated with the landmarks for the observations in `fd` to be registered. The number of rows N equals the number of observations, and the number of columns NL equals the number of landmarks. These landmark times must be in the interior of the interval over which the functions are defined.
`x0marks`	a vector of length NL of times of landmarks for target curve. If not supplied, the mean of the landmark times in `ximarks` is used.
`WfdPar`	a functional parameter object defining the warping functions that transform time in order to register the curves.
`monwrd`	A logical value: if `TRUE`, the warping function is estimated using a monotone smoothing method; otherwise, a regular smoothing method is used, which is not guaranteed to give strictly monotonic warping functions.
`ylambda`	Smoothing parameter controlling the smoothness of the registered functions. It can happen with high dimensional bases that local wiggles can appear in the registered curves or their derivatives that are not seen in the unregistered versions. In this case, this parameter should be increased to the point where they disappear.

Details

It is essential that the location of every landmark be clearly defined in each of the curves as well as the template function. If this is not the case, consider using the continuous registration function register.fd. Although requiring that a monotone smoother be used to estimate the warping functions is safer, it adds considerably to the computatation time since monotone smoothing is itself an iterative process. It is usually better to try an initial registration with this feature to see if there are any failures of monotonicity. Moreover, monotonicity failures can usually be cured by increasing the smoothing parameter defining WfdPar. Not much curvature is usually required in the warping functions, so a rather low power basis, usually B-splines, is suitable for defining the functional parameter argument WfdPar. A registration with a few prominent landmarks is often a good preliminary to using the more sophisticated but more lengthy process in register.fd.

Value

a named list of length 2 with components:

`fdreg`	a functional data object for the registered curves.
`warpfd`	a functional data object for the warping functions.

Examples

#See the analysis for the lip data in the examples.

## setting parameters
library(lattice)
data(landmark.reg.expData) ## containing an simple object called sampleData

# Preferred:
# eps <- .Machine$double.eps
# to reduce compute time:
eps <- 1000*.Machine$double.eps
from <- -1.0187
to <- 9.4551
# Preferred:
# nb <- 201
# to reduce compute time:
nb <- 31
nbreaks <- 11
## assign landmarks
landmark <- matrix(c(0.4999, 0.657, 0.8141, 0.5523, 0.5523,
                     3.3279, 3.066, 3.0137, 3.2231, 3.2231),
                   ncol=2)
wbasis <- create.bspline.basis(rangeval=c(from, to),
	                       norder=4, breaks=seq(from, to, len=nbreaks))
Wfd0   <- fd(matrix(0,wbasis$nbasis,1),wbasis)
WfdPar <- fdPar(Wfd0, 1, 1e-4)
## get the density of the data
x <- split(sampleData, factor(sampleData$which))
# to save time, reduce the number of curves from 5 to 3
k <- 3
densY <- sapply(x[1:k], function(z){
            r <- range(z[, 1])
            z <- z[, 1]
            z <- z[z>r[1]+eps & z<r[2]-eps]
            density(z, from=from, to=to, n=nb, na.rm=TRUE)$y
        })

argvals <- seq(from, to, len=nb)
fdobj   <- smooth.basis(argvals, densY, wbasis,
                   fdnames = c("x", "samples", "density"))$fd

regDens   <- landmarkreg(fdobj, landmark[1:k,], WfdPar=WfdPar, monwrd=TRUE)

warpfdobj <- regDens$warpfd
warpedX   <- as.matrix(eval.fd(warpfdobj, argvals))
matplot(argvals, warpedX, type="l")
funs      <- apply(warpedX, 2, approxfun, argvals)

fda

Functional Data Analysis

v5.1.9

GPL (>= 2)

Authors

J. O. Ramsay <ramsay@psych.mcgill.ca> [aut,cre], Spencer Graves <spencer.graves@effectivedefense.org> [ctb], Giles Hooker <gjh27@cornell.edu> [ctb]

Initial release

2020-12-16