multivariate_pca {fdasrvf}R Documentation

Curve PCA

Description

Calculate principal directions of a set of curves

Usage

multivariate_pca(
  align_data,
  no = 3,
  var_exp = NULL,
  ci = c(-1, 0, 1),
  mode = "O",
  showplot = TRUE
)

Arguments

align_data

fdacurve object from multivariate_karcher_mean of aligned data

no

number of components

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))

mode

Open ("O") or Closed ("C") curves

showplot

show plots of principal directions (default = TRUE)

Value

Returns a curve_pca object containing

latent

singular values

U

singular vectors

coef

principal coefficients

pd

principal directions

References

Srivastava, A., Klassen, E., Joshi, S., Jermyn, I., (2011). Shape analysis of elastic curves in euclidean spaces. Pattern Analysis and Machine Intelligence, IEEE Transactions on 33 (7), 1415-1428.

Examples

align_data <- multivariate_karcher_mean(beta[, , 1, 1:2], maxit = 2)
out <- multivariate_pca(align_data)

[Package fdasrvf version 2.4.0 Index]