fd: Define a Functional Data Object

Description

This is the constructor function for objects of the fd class. Each function that sets up an object of this class must call this function. This includes functions smooth.basis, density.fd, and so forth that estimate functional data objects that smooth or otherwise represent data. Ordinarily, users of the functional data analysis software will not need to call this function directly, but these notes are valuable to understanding the components of a list of class fd.

Usage

fd(coef=NULL, basisobj=NULL, fdnames=NULL)

Value

A functional data object (i.e., having class fd), which is a list with components named coefs, basis, and

fdnames.

Arguments

coef

a vector, matrix, or three-dimensional array of coefficients.

The first dimension (or elements of a vector) corresponds to basis functions.

A second dimension corresponds to the number of functional observations, curves or replicates. If coef is a vector, it represents only a single functional observation.

If coef is an array, the third dimension corresponds to variables for multivariate functional data objects.

A functional data object is "univariate" if coef is a vector or matrix and "multivariate" if it is a three-dimensional array.

if(is.null(coef)) coef <- rep(0, basisobj[['nbasis']])

basisobj

a functional basis object defining the basis

if(is.null(basisobj)){ if(is.null(coef)) basisobj <- basisfd() else { rc <- range(coef) if(diff(rc)==0) rc <- rc+0:1 nb <- max(4, nrow(coef)) basisobj <- create.bspline.basis(rc, nbasis = nb) } }

fdnames

A list of length 3, each member being a string vector containing labels for the levels of the corresponding dimension of the discrete data. The first dimension is for argument values, and is given the default name "time", the second is for replications, and is given the default name "reps", and the third is for functions, and is given the default name "values".

Details

To check that an object is of this class, use function is.fd.

Normally only developers of new functional data analysis functions will actually need to use this function.

Examples

Run this code

##
## default
##
fd()
oldpar <- par(no.readonly=TRUE)
##
## The simplest b-spline basis:  order 1, degree 0, zero interior knots:
##       a single step function
##
bspl1.1    <- create.bspline.basis(norder=1, breaks=0:1)
fd.bspl1.1 <- fd(0, basisobj=bspl1.1)

fd.bspl1.1a <- fd(basisobj=bspl1.1)
 stopifnot( 
all.equal(fd.bspl1.1, fd.bspl1.1a)
 ) 
# TRUE

# the following three lines shown an error in a non-cran check:
# if(!CRAN()) {
#   fd.bspl1.1b <- fd(0)
# }

##
## Cubic spline:  4  basis functions
##
bspl4 <- create.bspline.basis(nbasis=4)
plot(bspl4)
parab4.5 <- fd(c(3, -1, -1, 3)/3, bspl4)
# = 4*(x-.5)^2
plot(parab4.5)

##
## Fourier basis
##
f3 <- fd(c(0,0,1), create.fourier.basis())
plot(f3)
# range over +/-sqrt(2), because
# integral from 0 to 1 of cos^2 = 1/2
# so multiply by sqrt(2) to get
# its square to integrate to 1.

##
## subset of an fd object
##

gaitbasis3 <- create.fourier.basis(nbasis=5)
gaittime = (1:20)/21
gaitfd3    <- smooth.basis(gaittime, gait, gaitbasis3)$fd
gaitfd3[1]
par(oldpar)