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 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 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
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 are possible.signature(object = "Pca")
: prints the results signature(object = "Pca")
: calculates prediction using the results in
object
. An optional data frame or matrix in which to look for variables with which
to predict. If omitted, the scores are used. If the original fit used a formula or
a data frame or a matrix with column names, newdata must contain columns with the
same names. Otherwise it must contain the same number of columns,
to be used in the same order. See also predict.prcomp
and
predict.princomp
signature(x = "Pca")
: plots the variances against the
number of the principal component. See also plot.prcomp
and
plot.princomp
signature(x = "Pca")
: Plot a biplot, i.e. represent both
the observations and variables of a matrix of multivariate data on the same
plot. See also biplot.princomp
Valentin Todorov valentin.todorov@chello.at
PcaClassic
, PcaClassic-class
, PcaRobust-class
showClass("Pca")