Pca-class {rrcov}R Documentation

Class "Pca" - virtual base class for all classic and robust PCA classes

Description

The class Pca searves as a base class for deriving all other classes representing the results of the classical and 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 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

Methods

getCenter
signature(obj = "Pca"): center of the data
getEigenvalues
signature(obj = "Pca"): the eigenvalues of the covariance/correlation matrix, though the calculation is actually done with the singular values of the data matrix)
getLoadings
signature(obj = "Pca"): returns the matrix of variable loadings (i.e., a matrix whose columns contain the eigenvectors). The function prcomp returns this matrix in the element rotation.
getPrcomp
signature(obj = "Pca"): returns an S3 object prcomp for compatibility with the functions prcomp() and princomp(). Thus the standard plots screeplot() and biplot() can be used
getScores
signature(obj = "Pca"): returns the rotated data (the centred (and scaled if requested) data multiplied by the loadings matrix).
getSdev
signature(obj = "Pca"): returns the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the covariance/correlation matrix, though the calculation is actually done with the singular values of the data matrix)
plot
signature(x = "Pca"): produces a distance plot (if k=rank) or distance-distance plot (ifk<rank)
print
signature(x = "Pca"): prints the results. The difference to the show() method is that additional parametesr ar epossible.
show
signature(object = "Pca"): prints the results

Author(s)

Valentin Todorov valentin.todorov@chello.at

See Also

PcaClassic, PcaClassic-class, PcaRobust-class

Examples

showClass("Pca")

[Package rrcov version 0.4-01 Index]