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
:Object of class "language"
center
:Object of class "vector"
the center of the data
scale
:Object of class "vector"
the scaling applied to each variable
rank
:Object of class "numeric"
the rank of the data matrix
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
eig0
:Object of class "vector"
all eigenvalues
totvar0
:Object of class "numeric"
the total variance explained (=sum(eig0)
)
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
Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1--47. tools:::Rd_expr_doi("10.18637/jss.v032.i03").
PcaRobust-class
, Pca-class
, PcaClassic