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: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 of the data
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
criterion to use for computing the cutoff values for the orthogonal and score distances. Default is 0.975.
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))
signature(obj = "Pca"): center of the data
signature(obj = "Pca"): return the scaling applied to each variable
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 used
signature(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
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").
PcaClassic, PcaClassic-class, PcaRobust-class