fd | R Documentation |
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
.
fd(coef=NULL, basisobj=NULL, fdnames=NULL)
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 If A functional data object is "univariate" if if(is.null(coef)) coef <- rep(0, basisobj[['nbasis']]) |
basisobj |
a functional basis object defining the basis
|
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". |
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.
A functional data object (i.e., having class fd
), which is a
list with components named coefs
, basis
, and
fdnames
.
Ramsay, James O., and Silverman, Bernard W. (2006), Functional Data Analysis, 2nd ed., Springer, New York.
Ramsay, James O., and Silverman, Bernard W. (2002), Applied Functional Data Analysis, Springer, New York
smooth.basis
smooth.fdPar
smooth.basisPar
density.fd
create.bspline.basis
arithmetic.fd
##
## 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)
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)
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