pcaTB: Tolerance Bound Calculation using Elastic Functional PCA

In fdasrvf: Elastic Functional Data Analysis

Tolerance Bound Calculation using Elastic Functional PCA

Description

This function computes tolerance bounds for functional data containing phase and amplitude variation using principal component analysis

Usage

pcaTB(f, time, m = 4, B = 1e+05, a = 0.05, p = 0.99)

Arguments

f	matrix of functions
time	vector describing time sampling
m	number of principal components (default = 4)
B	number of monte carlo iterations
a	confidence level of tolerance bound (default = 0.05)
p	coverage level of tolerance bound (default = 0.99)

Value

Returns a list containing

pca	pca output
tol	tolerance factor

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 <- pcaTB(simu_data$f, simu_data$time)

## End(Not run)

fdasrvf documentation built on Nov. 19, 2023, 1:09 a.m.