horizFPCA {fdasrvf}

Horizontal Functional Principal Component Analysis

Description

This function calculates vertical functional principal component analysis on aligned data

Usage

horizFPCA(warp_data, no = 3, var_exp = NULL, 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

var_exp

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

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)