norm.fdata: Approximates Lp-norm for functional data.

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

Approximates Lp-norm for functional data.

Description

Approximates Lp-norm for functional data (fdata) object using metric or semimetric functions. Norm for functional data using by default Lp-metric.

Usage

norm.fdata(fdataobj, metric = metric.lp, ...)

norm.fd(fdobj)

Arguments

fdataobj	fdata class object.
metric	Metric function, by default metric.lp.
...	Further arguments passed to or from other methods.
fdobj	Functional data or curves of fd class.

Details

By default it computes the L2-norm with p = 2 and weights w with length=(m-1).

The observed points on each curve are equally spaced (by default) or not.

Author(s)

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

See Also

See also metric.lp and norm
Alternative method: inprod of fda-package

Examples

## Not run: 
x<-seq(0,2*pi,length=1001)
fx1<-sin(x)/sqrt(pi)
fx2<-cos(x)/sqrt(pi)
argv<-seq(0,2*pi,len=1001)
fdat0<-fdata(rep(0,len=1001),argv,range(argv))
fdat1<-fdata(fx1,x,range(x))
metric.lp(fdat1)
metric.lp(fdat1,fdat0)
norm.fdata(fdat1)
# The same
integrate(function(x){(abs(sin(x)/sqrt(pi))^2)},0,2*pi)
integrate(function(x){(abs(cos(x)/sqrt(pi))^2)},0,2*pi)

bspl1<- create.bspline.basis(c(0,2*pi),21)
fd.bspl1 <- fd(basisobj=bspl1)
fd.bspl2<-fdata2fd(fdat1,nbasis=21)
norm.fd(fd.bspl1)
norm.fd(fd.bspl2)

## End(Not run)

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