bootTB {fdasrvf}

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 = 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)

Nsamp

number of functions per bootstrap (default = 100)

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, “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

## Not run: 
  out1 <- bootTB(simu_data$f, simu_data$time)

## End(Not run)