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")