| bootTB {fdasrvf} | R Documentation |
This function computes tolerance bounds for functional data containing phase and amplitude variation using bootstrap sampling
bootTB(f, time, a = 0.05, p = 0.99, B = 500, no = 5, parallel = T)
f |
matrix of functions |
time |
vector describing time sampling |
a |
confidence level of tolerance bound (default = 0.05) |
p |
coverage level of tolerance bound (default = 0.99) |
B |
number of bootstrap samples (default = 500) |
no |
number of principal components (default = 5) |
parallel |
enable parallel processing (default = T) |
Returns a list containing
amp |
amplitude tolerance bounds |
ph |
phase tolerance bounds |
J. D. Tucker, J. R. Lewis, C. King, and S. Kurtek, “A Geometric Approach for Computing Tolerance Bounds for Elastic Functional Data,” Journal of Applied Statistics, 10.1080/02664763.2019.1645818, 2019.
Tucker, J. D., Wu, W., Srivastava, A., Generative Models for Function Data using Phase and Amplitude Separation, Computational Statistics and Data Analysis (2012), 10.1016/j.csda.2012.12.001.
Jung, S. L. a. S. (2016). "Combined Analysis of Amplitude and Phase Variations in Functional Data." arXiv:1603.01775 [stat.ME].
## Not run:
data("simu_data")
out1 = bootTB(simu_data$f,simu_data$time)
## End(Not run)