S.basis

Smoothing matrix with roughness penalties by basis representation.

Provides the smoothing matrix S with roughness penalties.

Keywords
smooth
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.

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

Aliases
  • S.basis
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)
# }
Documentation reproduced from package fda.usc, version 2.0.2, License: GPL-2

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