Bootstrap independent and identically distributed functional data
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 |
estad |
Estimate function of interest. Default is to estimate the mean function. Other options are |
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
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# Bootstrapping the distribution of a summary statistics of functional data. fbootstrap(data = ElNino_ERSST_region_1and2)
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