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 |
ci |
geodesic standard deviations (default = c(-1,0,1)) |
mode |
Open ( |
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]