fdasrvf (version 2.3.6)

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,
  Nsamp = 100,
  parallel = TRUE
)

Value

Returns a list containing

amp

amplitude tolerance bounds

ph

phase tolerance bounds

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)

Nsamp

number of functions per bootstrap (default = 100)

parallel

enable parallel processing (default = TRUE)

References

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.

Examples

if (FALSE) {
  out1 <- bootTB(simu_data$f, simu_data$time)
}