\name{fbootstrap}
\alias{fbootstrap}
\title{Bootstrap independent and identically distributed functional data}
\description{
Computes bootstrap or smoothed bootstrap samples based on independent and identically distributed functional data.
}
\usage{
fbootstrap(data, estad = func.mean, alpha = 0.05, nb = 200, suav = 0,
 media.dist = FALSE, graph = FALSE, ...)
}
\arguments{
  \item{data}{An object of class \code{\link[rainbow]{fds}} or \code{fts}.}
  \item{estad}{Estimate function of interest. Default is to estimate the mean function. Other options are \code{func.mode} or \code{func.var}.}
  \item{alpha}{Significance level used in the smooth bootstrapping.}
  \item{nb}{Number of bootstrap samples.}
  \item{suav}{Smoothing parameter.}
  \item{media.dist}{Estimate mean function.}
  \item{graph}{Graphical output.}
  \item{\dots}{Other arguments.}
}
\value{
A list containing the following components is returned.
  \item{estimate}{Estimate function.}
  \item{max.dist}{Max distance of bootstrap samples.}
  \item{rep.dist}{Distances of bootstrap samples.}
  \item{resamples}{Bootstrap samples.}
  \item{center}{Functional mean.}
}
\references{
M. Febrero and P. Galeano and W. Gonzalez-Manteiga (2007) "A functional analysis of NOx levels: location and scale estimation and outlier detection", \emph{Computational Statistics}, \bold{22}(3), 411-427.

M. Febrero and P. Galeano and W. Gonzalez-Manteiga (2008) "Outlier detection in functional data by depth measures, with application to identify abnormal NOx levels", \emph{Environmetrics}, \bold{19}(4), 331-345.

M. Febrero and P. Galeano and W. Gonzalez-Manteiga (2009) "Measures of influence for the functional linear model with scalar response", \emph{Journal of Multivariate Analysis}, \bold{forthcoming}. 
}
\author{Han Lin Shang}
\examples{
fbootstrap(data = ElNino)
}
\keyword{multivariate}