Various constructors for tfb-vectors from different kinds of inputs.
tfb(data = data.frame(), basis = c("spline", "fpc", "wavelet"), ...)tfb_wavelet(data, ...)
as.tfb(data, basis = c("spline", "fpc"), ...)
a tfb-object (or a data.frame/matrix for the conversion
functions, obviously.)
a matrix, data.frame or list of suitable shape, or another
tf-object containing functional data.
either "spline" (see tfb_spline(), the default) or "fpc"
(see tfb_fpc()).
(wavelet not implemented yet)
further arguments for tfb_spline() or tfb_fpc()
tfb is a wrapper for functions that set up spline-, principal component- or
wavelet-based representations of functional data. For all three, the input
data \(x_i(t)\) are represented as weighted sums of a set of common basis
functions \(B_k(t); k = 1,\dots, K\) identical for all observations and
weight or coefficient vectors \(b_i = (b_{i1}, \dots, b_{iK})\) estimated
for each observation: \(x_i(t) \approx \sum_k B_k(t) b_{ik}\). Depending on
the value of basis, the basis functions \(B(t)\) will either be spline
functions or the first few estimated eigenfunctions of the covariance
operator of the \(x(t)\) (fpc) or wavelets (wavelet).
See tfb_spline() for more details on spline basis representation (the
default). See tfb_fpc() for using an functional principal component
representation with an orthonormal basis estimated from the data instead.
Other tfb-class:
fpc_wsvd(),
tfb_fpc(),
tfb_spline()
Other tfb-class:
fpc_wsvd(),
tfb_fpc(),
tfb_spline()