Various constructor and conversion methods.

as.tfb(data, basis = c("spline", "fpc"), ...)

# S3 method for tfb
as.data.frame(x, row.names = NULL, optional = FALSE, arg = NULL, ...)

# S3 method for tfb
as.matrix(x, arg = NULL, ...)

tfb(data, basis = c("spline", "fpc", "wavelet"), ...)

tfb_wavelet(data, ...)

## Arguments

data 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() an tfb object not used not used optional vector of argument values

## Value

a tfb-object (or a data.frame/matrix for the conversion functions, obviously.)

## Details

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.

tfb_fpc(), tfb_spline()