refund (version 0.1-1)

create.prep.func: 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 d

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

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

See Also

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