curve_principal_directions: Curve PCA

In fdasrvf: Elastic Functional Data Analysis

Curve PCA

Description

Calculate principal directions of a set of curves

Usage

curve_principal_directions(v, K, mu, len = NA, no = 3, N = 5, mode = "O")

Arguments

v	array (n,T,N1) of shooting vectors
K	array (nT,nT) covariance matrix
mu	array (n,T) of mean srvf
len	length of original curves (default NA)
no	number of components
N	number of samples on each side of mean
mode	Open ("O") or Closed ("C") curves

Value

Returns a list containing

s	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

out <- curve_karcher_mean(beta[, , 1, 1:2], maxit = 2)
# note: use more shapes, small for speed
K <- curve_karcher_cov(out$v)
out <- curve_principal_directions(out$v, K, out$mu)

fdasrvf documentation built on Nov. 19, 2023, 1:09 a.m.