Smoothing matrix with roughness penalties by basis representation.

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

Value

Return the smoothing matrix S.

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.

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.

Author

Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@udc.es

Examples

if (FALSE) { # \dontrun{
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)
} # }