R: Class "PcaHubert" - ROBust method for Principal Components...
PcaHubert-class
R Documentation
Class "PcaHubert" - ROBust method for Principal Components Analysis
Description
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 from the Class
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.
Slots
alpha:
Object of class "numeric" the fraction of outliers
the algorithm should resist - this is the argument alpha
quan:
Object of class "numeric" The quantile h used throughout the algorithm
Todorov V & Filzmoser P (2009),
An Object Oriented Framework for Robust Multivariate Analysis.
Journal of Statistical Software, 32(3), 1–47.
URL http://www.jstatsoft.org/v32/i03/.