shape_CI: Shape Confidence Interval Calculation using Bootstrap...

In fdasrvf: Elastic Functional Data Analysis

Shape Confidence Interval Calculation using Bootstrap Sampling

Description

This function computes Confidence bounds for shapes using elastic metric

Usage

shape_CI(
  beta,
  a = 0.95,
  no = 5,
  Nsamp = 100,
  mode = "O",
  rotated = TRUE,
  scale = TRUE,
  lambda = 0,
  parallel = TRUE
)

Arguments

beta	Array of sizes n \times T \times N describing N curves of dimension n evaluated on T points
a	confidence level (default = 0.95)
no	number of principal components (default = 5)
Nsamp	number of functions to generate (default = 100)
mode	Open ("O") or Closed ("C") curves
rotated	Optimize over rotation (default = TRUE)
scale	scale curves to unit length (default = TRUE)
lambda	A numeric value specifying the elasticity. Defaults to 0.0.
parallel	enable parallel processing (default = T)

Value

Return shape confidence intervals

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.

fdasrvf documentation built on Oct. 5, 2024, 1:08 a.m.