PcaNA-class: Class "PcaNA" Principal Components for incomplete data

In rrcovNA: Scalable Robust Estimators with High Breakdown Point for Incomplete Data

Class "PcaNA" Principal Components for incomplete data

Description

Contains the results of the computations of classical and robust principal components for incomplete data using an EM algorithm as descibed by Serneels and Verdonck (2008)

Objects from the Class

Objects can be created by calls of the form new("PcaNA", ...) but the usual way of creating PcaNA objects is a call to the function PcaNA which serves as a constructor.

Slots

call, center, scale, loadings, eigenvalues, scores, k, sd, od, cutoff.sd, cutoff.od, flag, n.obs:

from the "Pca" class.

Ximp:

the data matrix with imputed missing values

Extends

Class "Pca", directly.

Methods

getQuan

signature(obj = "PcaNA"): ...

Author(s)

Valentin Todorov [e-mail]

References

Serneels S & Verdonck T (2008), Principal component analysis for data containing outliers and missing elements. Computational Statistics and Data Analisys, 52(3), 1712–1727 .

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. <doi:10.18637/jss.v032.i03>.

See Also

PcaRobust-class, Pca-class, PcaClassic, PcaClassic-class

Examples

showClass("PcaNA")

rrcovNA documentation built on July 9, 2023, 6:26 p.m.