| Pca-class {rrcov} | R Documentation |
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
A virtual Class: No objects may be created from it.
call:"language" center:"vector" the center of the data loadings:"matrix" the matrix
of variable loadings (i.e., a matrix whose columns contain the eigenvectors) eigenvalues:"vector" the eigenvalues scores:"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:"numeric" number of (choosen) principal components sd:"Uvector" Score distances within the robust PCA subspace od:"Uvector" Orthogonal distances to the robust PCA subspace cutoff.sd:"numeric" Cutoff value for the score distancescutoff.od:"numeric" Cutoff values for the orthogonal distances flag:"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:"numeric" the number of observations signature(obj = "Pca"): center of the data signature(obj = "Pca"): the eigenvalues of the
covariance/correlation matrix, though the calculation is actually done
with the singular values of the data matrix) 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. 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 usedsignature(obj = "Pca"): returns the rotated data (the centred
(and scaled if requested) data multiplied by the loadings matrix). 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) signature(x = "Pca"): produces a distance plot (if k=rank) or
distance-distance plot (ifk<rank) signature(x = "Pca"): prints the results. The difference to the show()
method is that additional parametesr ar epossible.signature(object = "Pca"): prints the results Valentin Todorov valentin.todorov@chello.at
PcaClassic, PcaClassic-class, PcaRobust-class
showClass("Pca")