fdata.bootstrap: Bootstrap samples of a functional statistic

Description

fdata.bootstrap provides bootstrap samples for functional data.

Usage

fdata.bootstrap(fdataobj,statistic=func.mean,alpha=0.05,nb=200,
smo=0.0,draw=FALSE,draw.control=NULL,...)

Arguments

fdataobj

fdata class object.

statistic

Sample statistic. It must be a function that returns an object of class fdata. By default, it uses sample mean func.mean. See Descriptive

alpha

Significance value.

Number of bootstrap resamples.

smo

The smoothing parameter for the bootstrap samples as a proportion of the sample variance matrix.

draw

=TRUE, plot the bootstrap samples and the statistic.

draw.control

list that it specifies the col, lty and lwd for objects: fdataobj, statistic, IN and OUT.

...

Further arguments passed to or from other methods.

Value

statisticfdata class object with the statistic estimate from nb bootstrap samples.
dbandBootstrap estimate of (1-alpha)% distance.
rep.distDistance from every replicate.
resamplesfdata class object with the bootstrap resamples.
fdataobjfdata class object.

References

Cuevas A., Febrero-Bande, M. and Fraiman, R. (2007). Robust estimation and classification for functional data via projection-based depth notions. Computational Statistics 22, 3: 481{-}496. Cuevas A., Febrero-Bande, M., Fraiman R. 2006. On the use of bootstrap for estimating functions with functional data. Computational Statistics and Data Analysis 51: 1063{-}1074.

Examples

Run this code

data(tecator)
absorp<-tecator$absorp.fdata
#No run
#Bootstrap for Trimmed Mean with depth mode
#out.boot=fdata.bootstrap(absorp,statistic=func.trim.FM,nb=200,draw=TRUE)
#names(out.boot)
#Bootstrap for Median with with depth mode
#control=list("col"=c("grey","blue","cyan"),"lty"=c(2,1,1),"lwd"=c(1,3,1))
#out.boot=fdata.bootstrap(absorp,statistic=func.med.mode,
#draw=TRUE,draw.control=control)