Description

The Spherical Principal Components procedure was proposed by Locantore et al., (1999) as a functional data analysis method. The idea is to perform classical PCA on the the data, \ projected onto a unit sphere. The estimates of the eigenvectors are consistent and the procedure is extremly fast. The simulations of Maronna (2005) show that this method has very good performance.

Objects from the Class

Objects can be created by calls of the form new("PcaLocantore", ...) but the usual way of creating PcaLocantore objects is a call to the function PcaLocantore which serves as a constructor.

Slots

delta:

Accuracy parameter

quan:

Object of class "numeric" The quantile h used throughout the algorithm

call, center, scale, loadings, eigenvalues, scores, k, sd, od, cutoff.sd, cutoff.od, flag, n.obs:

from the "Pca" class.

Extends

Class "PcaRobust", directly. Class "Pca", by class "PcaRobust", distance 2.

Methods

getQuan

signature(obj = "PcaLocantore"): ...

Author(s)

Valentin Todorov valentin.todorov@chello.at

References

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. URL http://www.jstatsoft.org/v32/i03/.

See Also

PcaRobust-class, Pca-class, PcaClassic, PcaClassic-class

Examples