fbootstrap {ftsa}R Documentation

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

data An object of class fds or fts.
estad Estimate function of interest. Default is to estimate the mean function. Other options are func.mode or func.var.
alpha Significance level used in the smooth bootstrapping.
nb Number of bootstrap samples.
suav Smoothing parameter.
media.dist Estimate mean function.
graph Graphical output.
... Other arguments.

Value

A list containing the following components is returned.

estimate Estimate function.
max.dist Max distance of bootstrap samples.
rep.dist Distances of bootstrap samples.
resamples Bootstrap samples.
center Functional mean.

Author(s)

Han Lin Shang

References

M. Febrero and P. Galeano and W. Gonzalez-Manteiga (2007) "A functional analysis of NOx levels: location and scale estimation and outlier detection", Computational Statistics, 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", Environmetrics, 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", Journal of Multivariate Analysis, 101(2), 327-339.

Examples

fbootstrap(data = ElNino)

[Package ftsa version 1.3 Index]