fdasrvf (version 2.3.6)

jointFPCA: 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 = 3,
  var_exp = NULL,
  id = round(length(warp_data$time)/2),
  C = NULL,
  ci = c(-1, 0, 1),
  srvf = TRUE,
  showplot = TRUE
)

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

Arguments

warp_data

fdawarp object from time_warping of aligned data

no

number of principal components to extract (default = 3)

var_exp

compute no based on value percent variance explained (example: 0.95) 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)

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)