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fdasrvf (version 2.3.6)

shape_CI: 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
)

Value

Return shape confidence intervals

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)

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.