fdasrvf (version 1.9.4)

bootTB: Tolerance Bound Calculation using Bootstrap Sampling

Description

This function computes tolerance bounds for functional data containing phase and amplitude variation using bootstrap sampling

Usage

bootTB(f, time, a = 0.05, p = 0.99, B = 500, no = 5, parallel = T)

Arguments

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)

Value

Returns a list containing

amp

amplitude tolerance bounds

ph

phase tolerance bounds

References

J. D. Tucker, J. R. Lewis, C. King, and S. Kurtek, <U+201C>A Geometric Approach for Computing Tolerance Bounds for Elastic Functional Data,<U+201D> 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].

Examples

# NOT RUN {
  data("simu_data")
  out1 = bootTB(simu_data$f,simu_data$time)
# }