PcaLocantore-class: Class "PcaLocantore" Spherical Principal Components

In rrcov: Scalable Robust Estimators with High Breakdown Point

Class "PcaLocantore" Spherical Principal Components

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, rank, loadings, eigenvalues, scores, k, sd, od, cutoff.sd, cutoff.od, flag, n.obs, eig0, totvar0:

from the "Pca" class.

Extends

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

Methods

getQuan

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

Author(s)

Valentin Todorov [e-mail]

References

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v032.i03")}.

See Also

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

Examples

showClass("PcaLocantore")

rrcov documentation built on July 9, 2023, 6:03 p.m.