PcaClassic-class {rrcov}R Documentation

Class "PcaClassic" - Principal Components Analysis

Description

Contains the results of a classical Principal Components Analysis

Objects from the Class

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

Slots

call:
Object of class "language"
center:
Object of class "vector" the center of the data
loadings:
Object of class "matrix" the matrix of variable loadings (i.e., a matrix whose columns contain the eigenvectors)
eigenvalues:
Object of class "vector" the eigenvalues
scores:
Object of class "matrix" the scores - the value of the rotated data (the centred (and scaled if requested) data multiplied by the rotation matrix) is returned. Hence, cov(scores) is the diagonal matrix diag(eigenvalues)
k:
Object of class "numeric" number of (choosen) principal components
sd:
Object of class "Uvector" Score distances within the robust PCA subspace
od:
Object of class "Uvector" Orthogonal distances to the robust PCA subspace
cutoff.sd:
Object of class "numeric" Cutoff value for the score distances
cutoff.od:
Object of class "numeric" Cutoff values for the orthogonal distances
flag:
Object of class "Uvector" The observations whose score distance is larger than cutoff.sd or whose orthogonal distance is larger than cutoff.od can be considered as outliers and receive a flag equal to zero. The regular observations receive a flag 1
n.obs:
Object of class "numeric" the number of observations

Extends

Class "Pca", directly.

Methods

getQuan
signature(obj = "PcaClassic"): returns the number of observations used in the computation, i.e. n.obs

Author(s)

Valentin Todorov valentin.todorov@chello.at

See Also

PcaRobust-class, Pca-class, PcaClassic

Examples

showClass("PcaClassic")

[Package rrcov version 0.4-03 Index]