vertFPCA: Vertical Functional Principal Component Analysis

In fdasrvf: Elastic Functional Data Analysis

Vertical Functional Principal Component Analysis

Description

This function calculates vertical functional principal component analysis on aligned data

Usage

vertFPCA(
  warp_data,
  no,
  id = round(length(warp_data$time)/2),
  ci = c(-1, 0, 1),
  showplot = TRUE
)

Arguments

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

Value

Returns a vfpca object containing

q_pca	srvf principal directions
f_pca	f principal directions
latent	latent values
coef	coefficients
U	eigenvectors
id	point used for f(0)

References

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

vfpca <- vertFPCA(simu_warp, no = 3)

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