refund: smooth.construct.fpc.smooth.spec – R documentation

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smooth.construct.fpc.smooth.spec

Basis constructor for FPC terms

Description

Basis constructor for FPC terms

Usage

## S3 method for class 'fpc.smooth.spec'
smooth.construct(object, data, knots)

Arguments

`object`	a `fpc.smooth.spec` object, usually generated by a term `s(x, bs="fpc")`; see Details.
`data`	a list containing the data (including any `by` variable) required by this term, with names corresponding to `object$term` (and `object$by`). Only the first element of this list is used.
`knots`	not used, but required by the generic `smooth.construct`.

Details

object must contain an xt element. This is a list that can contain the following elements:

X: (required) matrix of functional predictors
method: (required) the method of finding principal components; options include "svd" (unconstrained), "fpca.sc", "fpca.face", or "fpca.ssvd"
npc: (optional) the number of PC's to retain
pve: (only needed if npc not supplied) the percent variance explained used to determine npc
penalize: (required) if FALSE, the smoothing parameter is set to 0
bs: the basis class used to pre-smooth X; default is "ps"

Any additional options for the pre-smoothing basis (e.g. k, m, etc.) can be supplied in the corresponding elements of object. See s for a full list of options.

Value

An object of class "fpc.smooth". In addtional to the elements listed in smooth.construct, the object will contain

`sm`	the smooth that is fit in order to generate the basis matrix over `object$term`
`V.A`	the matrix of principal components

Author(s)

Jonathan Gellar JGellar@mathematica-mpr.com

References

Reiss, P. T., and Ogden, R. T. (2007). Functional principal component regression and functional partial least squares. Journal of the American Statistical Association, 102, 984-996.