S.basis: Smoothing matrix with roughness penalties by basis...

In fda.usc: Functional Data Analysis and Utilities for Statistical Computing

Smoothing matrix with roughness penalties by basis representation.

Description

Provides the smoothing matrix S with roughness penalties.

Usage

S.basis(tt, basis, lambda = 0, Lfdobj = vec2Lfd(c(0, 0)), w = NULL, ...)

Arguments

tt	Discretization points.
basis	Basis to use. See create.basis.
lambda	A roughness penalty. By default, no penalty lambda=0.
Lfdobj	See eval.penalty.
w	Optional case weights.
...	Further arguments passed to or from other methods. Arguments to be passed by default to create.basis

Details

Provides the smoothing matrix S for the discretization points tt and bbasis with roughness penalties. If lambda=0 is not used penalty, else a basis roughness penalty matrix is caluclated using getbasispenalty.

Value

Return the smoothing matrix S.

Author(s)

Manuel Febrero-Bande, Manuel Oviedo de la Fuente [e-mail]

References

Ramsay, James O. and Silverman, Bernard W. (2006). Functional Data Analysis, 2nd ed., Springer, New York.

Wasserman, L. All of Nonparametric Statistics. Springer Texts in Statistics, 2006.

See Also

See Also as S.np

Examples

## Not run: 
np=101
tt=seq(0,1,len=np)

nbasis=11
base1 <- create.bspline.basis(c(0, np), nbasis)
base2 <- create.fourier.basis(c(0, np), nbasis)

S1<-S.basis(tt,basis=base1,lambda=3)
image(S1) 
S2<-S.basis(tt,basis=base2,lambda=3)
image(S2)

## End(Not run)

fda.usc documentation built on Oct. 17, 2022, 9:06 a.m.