| PcaCov-class {rrcov} | R Documentation |
Robust PCA are obtained by replacing the classical covariance matrix
by a robust covariance estimator. This can be one of the available
in rrcov estimators, i.e. MCD, OGK, M or S estimator.
Objects can be created by calls of the form new("PcaCov", ...) but the
usual way of creating PcaHubert objects is a call to the function
PcaCov which serves as a constructor.
delta:quan:"numeric" The quantile h used throughout the algorithm 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
Class "PcaRobust", directly.
Class "Pca", by class "PcaRobust", distance 2.
signature(obj = "PcaCov"): ... Valentin Todorov valentin.todorov@chello.at
PcaRobust-class, Pca-class, PcaClassic, PcaClassic-class
showClass("PcaCov")