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

scale:

Object of class "vector" the scaling applied to each variable

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

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

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

Examples