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 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

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 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 are possible.
show
signature(object = "Pca"): prints the results
predict
signature(object = "Pca"): calculates prediction using the results in object. An optional data frame or matrix in which to look for variables with which to predict. If omitted, the scores are used. If the original fit used a formula or a data frame or a matrix with column names, newdata must contain columns with the same names. Otherwise it must contain the same number of columns, to be used in the same order. See also predict.prcomp and predict.princomp
screeplot
signature(x = "Pca"): plots the variances against the number of the principal component. See also plot.prcomp and plot.princomp
biplot
signature(x = "Pca"): Plot a biplot, i.e. represent both the observations and variables of a matrix of multivariate data on the same plot. See also biplot.princomp

Author(s)

Valentin Todorov valentin.todorov@chello.at

See Also

PcaClassic, PcaClassic-class, PcaRobust-class

Examples

showClass("Pca")

[Package rrcov version 0.5-01 Index]