Smoothing matrix with roughness penalties by basis representation.
Provides the smoothing matrix S with roughness penalties.
S.basis(tt, basis, lambda = 0, Lfdobj = vec2Lfd(c(0, 0)), w = NULL, ...)
tt |
Discretization points. |
basis |
Basis to use. See create.basis. |
lambda |
A roughness penalty. By default, no penalty |
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 |
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.
Return the smoothing matrix S.
Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@usc.es
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 as S.np
## 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)
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