smooth.fdPar: Smooth a functional data object using a directly specified...

In fda: Functional Data Analysis

Smooth a functional data object using a directly specified roughness penalty

Description

Smooth data already converted to a functional data object, fdobj, using directly specified criteria.

Usage

smooth.fdPar(fdobj, Lfdobj=NULL, lambda=1e-4,
             estimate=TRUE, penmat=NULL)

Arguments

fdobj	a functional data object to be smoothed.
Lfdobj	either a nonnegative integer or a linear differential operator object. If NULL, Lfdobj depends on fdobj[['basis']][['type']]: bspline Lfdobj <- int2Lfd(max(0, norder-2)), where norder = norder(fdobj). fourier Lfdobj = a harmonic acceleration operator: Lfdobj <- vec2Lfd(c(0,(2*pi/diff(rng))^2,0), rng) where rng = fdobj[['basis']][['rangeval']]. anything else Lfdobj <- int2Lfd(0)
lambda	a nonnegative real number specifying the amount of smoothing to be applied to the estimated functional parameter.
estimate	a logical value: if TRUE, the functional parameter is estimated, otherwise, it is held fixed.
penmat	a roughness penalty matrix. Including this can eliminate the need to compute this matrix over and over again in some types of calculations.

Details

1. fdPar

2. smooth.fd

Value

a functional data object.

References

Ramsay, James O., Hooker, Giles, and Graves, Spencer (2009), Functional data analysis with R and Matlab, Springer, New York.

Ramsay, James O., and Silverman, Bernard W. (2005), Functional Data Analysis, 2nd ed., Springer, New York.

Ramsay, James O., and Silverman, Bernard W. (2002), Applied Functional Data Analysis, Springer, New York.

See Also

smooth.fd, fdPar, smooth.basis, smooth.pos, smooth.morph

Examples

	#  see smooth.basis

fda documentation built on Sept. 30, 2024, 9:19 a.m.