PcaClassic-class {rrcov} | R Documentation |
Contains the results of a classical Principal Components Analysis
Objects can be created by calls of the form new("PcaClassic", ...)
but the
usual way of creating PcaClassic
objects is a call to the function
PcaClassic
which serves as a constructor.
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 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
:"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 "Pca"
, directly.
signature(obj = "PcaClassic")
: returns the number of
observations used in the computation, i.e. n.obs Valentin Todorov valentin.todorov@chello.at
PcaRobust-class
, Pca-class
, PcaClassic
showClass("PcaClassic")