fdasrvf (version 2.3.6)

pcaTB: 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)

Value

Returns a list containing

pca

pca output

tol

tolerance factor

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)

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