The ROBPCA algorithm was proposed by Hubert et al (2005) and stays for 'ROBust method for Principal Components Analysis'. It is resistant to outliers in the data. The robust loadings are computed using projection-pursuit techniques and the MCD method. Therefore ROBPCA can be applied to both low and high-dimensional data sets. In low dimensions, the MCD method is applied.
Objects can be created by calls of the form new("PcaHubert", ...)
but the
usual way of creating PcaHubert
objects is a call to the function
PcaHubert
which serves as a constructor.
alpha
:Object of class "numeric"
the fraction of outliers
the algorithm should resist - this is the argument alpha
quan
:The quantile h
used throughout the algorithm
skew
:Whether the adjusted outlyingness algorithm for skewed data was used
ao
:Object of class "Uvector"
Adjusted outlyingness within the robust PCA subspace
call
, center
, scale
, rank
, loadings
,
eigenvalues
, scores
, k
,
sd
, od
, cutoff.sd
, cutoff.od
,
flag
, n.obs
, eig0
, totvar0
:from the "Pca"
class.
Class "PcaRobust"
, directly.
Class "Pca"
, by class "PcaRobust", distance 2.
signature(obj = "PcaHubert")
: Returns the quantile
used throughout the algorithm
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
, PcaClassic-class