vertFPCA {fdasrvf}

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
)

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)

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)

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)