refund: create.prep.func – R documentation

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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 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 ajg2202@cumc.columbia.edu

create.prep.func

Description

Usage

Arguments

Value

Author(s)

See Also

refund

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