jointFPCAh: 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 using the SRVF framework using MFPCA and h representation

Usage

jointFPCAh(
  warp_data,
  var_exp = 0.99,
  id = round(length(warp_data$time)/2),
  C = NULL,
  ci = c(-1, 0, 1),
  srvf = TRUE,
  showplot = TRUE
)

Arguments

warp_data	fdawarp object from time_warping of aligned data
var_exp	compute no based on value percent variance explained (default: 0.99) will override no
id	integration point for f0 (default = midpoint)
C	balance value (default = NULL)
ci	geodesic standard deviations (default = c(-1,0,1))
srvf	use srvf (default = TRUE)
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

jfpcah <- jointFPCAh(simu_warp)

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