fdasrvf (version 2.3.6)

vertFPCA: Vertical Functional Principal Component Analysis

Description

This function calculates vertical functional principal component analysis on aligned data

Usage

vertFPCA(
  warp_data,
  no = 3,
  var_exp = NULL,
  id = round(length(warp_data$time)/2),
  ci = c(-1, 0, 1),
  showplot = TRUE
)

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)

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

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)

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)