create.prep.func: Construct a function for preprocessing functional predictors

In refund: Regression with Functional Data

Construct a function for preprocessing functional predictors

Description

Prior to using functions X as predictors in a scalar-on-function regression, it is often necessary to presmooth curves to remove measurement error or interpolate to a common grid. This function creates a function to do this preprocessing depending on the method specified.

Usage

create.prep.func(
  X,
  argvals = seq(0, 1, length = ncol(X)),
  method = c("fpca.sc", "fpca.face", "fpca.ssvd", "bspline", "interpolate"),
  options = NULL
)

Arguments

X	an N by J=ncol(argvals) matrix of function evaluations X_i(t_{i1}),., X_i(t_{iJ}); i=1,.,N. For FPCA-based processing methods, these functions are used to define the eigen decomposition used to preprocess current and future data (for example, in predict.pfr)
argvals	matrix (or vector) of indices of evaluations of X_i(t); i.e. a matrix with ith row (t_{i1},.,t_{iJ})
method	character string indicating the preprocessing method. Options are "fpca.sc", "fpca.face", "fpca.ssvd", "bspline", and "interpolate". The first three use the corresponding existing function; "bspline" uses an (unpenalized) cubic bspline smoother with nbasis basis functions; "interpolate" uses linear interpolation.
options	list of options passed to the preprocessing method; as an example, options for fpca.sc include pve, nbasis, and npc.

Value

a function that returns the preprocessed functional predictors, with arguments

newX	The functional predictors to process
argvals.	Indices of evaluation of newX
options.	Any options needed to preprocess the predictor functions

Author(s)

Jeff Goldsmith [e-mail]

See Also

pfr, fpca.sc, fpca.face, fpca.ssvd

refund documentation built on Sept. 21, 2024, 1:07 a.m.