jointFPCA {fdasrvf} | R Documentation |
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),
showplot = T
)
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 |
id |
integration point for f0 (default = midpoint) |
C |
balance value (default = NULL) |
ci |
geodesic standard deviations (default = c(-1,0,1)) |
showplot |
show plots of principal directions (default = T) |
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 |
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)