fbootstrap {ftsa} | R Documentation |
Computes bootstrap or smoothed bootstrap samples based on independent and identically distributed functional data.
fbootstrap(data, estad = func.mean, alpha = 0.05, nb = 200, suav = 0, media.dist = FALSE, graph = FALSE, ...)
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. |
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. |
Han Lin Shang
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.
fbootstrap(data = ElNino)