jointFPCA: Joint Vertical and Horizontal Functional Principal Component...

In fdasrvf: Elastic Functional Data Analysis

Joint Vertical and Horizontal Functional Principal Component Analysis

Description

This function calculates amplitude and phase joint functional principal component analysis on aligned data

Usage

jointFPCA(
  warp_data,
  no,
  id = round(length(warp_data$time)/2),
  C = NULL,
  ci = c(-1, 0, 1),
  showplot = T
)

Arguments

warp_data	fdawarp object from time_warping of aligned data
no	number of principal components to extract
id	integration point for f0 (default = midpoint)
C	balance value (default = NULL)
ci	geodesic standard deviations (default = c(-1,0,1))
showplot	show plots of principal directions (default = T)

Value

Returns a list containing

q_pca	srvf principal directions
f_pca	f principal directions
latent	latent values
coef	coefficients
U	eigenvectors
mu_psi	mean psi function
mu_g	mean g function
id	point use for f(0)
C	optimized phase amplitude ratio

References

Srivastava, A., Wu, W., Kurtek, S., Klassen, E., Marron, J. S., May 2011. Registration of functional data using fisher-rao metric, arXiv:1103.3817v2.

Jung, S. L. a. S. (2016). "Combined Analysis of Amplitude and Phase Variations in Functional Data." arXiv:1603.01775.

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.

Examples

jfpca <- jointFPCA(simu_warp, no = 3)

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