horizFPNS: Horizontal Functional Principal Component Analysis

In fdasrvf: Elastic Functional Data Analysis

Horizontal Functional Principal Component Analysis

Description

This function calculates vertical functional principal nested sphere analysis on aligned data

Usage

horizFPNS(warp_data, var_exp = 0.99, ci = c(-1, 0, 1), showplot = TRUE)

Arguments

warp_data	fdawarp object from time_warping of aligned data
var_exp	compute no based on value percent variance explained (example: 0.95)
ci	geodesic standard deviations (default = c(-1,0,1))
showplot	show plots of principal directions (default = T)

Value

Returns a hfpns object containing

gam_pns	warping functions principal directions
psi_pns	srvf principal directions
PNS	PNS object

References

Yu, Q., Lu, X., and Marron, J. S. (2017), “Principal Nested Spheres for Time-Warped Functional Data Analysis,” Journal of Computational and Graphical Statistics, 26, 144–151.

Examples

hfpns <- horizFPNS(simu_warp)

fdasrvf documentation built on Nov. 5, 2025, 5:51 p.m.