Description

The class PcaRobust searves as a base class for deriving all other classes representing the results of the robust Principal Component Analisys methods

Objects from the Class

A virtual Class: No objects may be created from it.

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 projected on the space of the principal components data (the centred (and scaled if requested) data multiplied by the loadings 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

No methods defined with class "PcaRobust" in the signature.

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

Pca-class, PcaClassic-class,

Examples