horizFPCA: Horizontal Functional Principal Component Analysis

In fdasrvf: Elastic Functional Data Analysis

Horizontal Functional Principal Component Analysis

Description

This function calculates vertical functional principal component analysis on aligned data

Usage

horizFPCA(warp_data, no, 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
ci	geodesic standard deviations (default = c(-1,0,1))
showplot	show plots of principal directions (default = T)

Value

Returns a hfpca object containing

gam_pca	warping functions principal directions
psi_pca	srvf principal directions
latent	latent values
U	eigenvectors
vec	shooting vectors
mu	Karcher Mean

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

hfpca <- horizFPCA(simu_warp, no = 3)

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