Class SPcaGrid - Sparse Robust PCA using PP - GRID search Algorithm
Class SPcaGrid - Sparse Robust PCA using PP - GRID search Algorithm
Holds the results of an approximation of the PP-estimators for sparse and robust PCA using the grid search algorithm in the plane.
1.1
class
Objects from the Class
Objects can be created by calls of the form new("SPcaGrid", ...) but the usual way of creating SPcaGrid objects is a call to the function SPcaGrid() 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" class.
Extends
Class "PcaGrid-class", directly. Class "PcaRobust-class", by class "PcaGrid-class", distance 2. Class "Pca-class", by class "PcaGrid-class", distance 3.
Methods
getQuan: signature(obj = "SPcaGrid"): ...
References
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") .
C. Croux, P. Filzmoser, H. Fritz (2013). Robust Sparse Principal Component Analysis, Technometrics 55 (2), pp. 202--2014, tools:::Rd_expr_doi("10.1080/00401706.2012.727746") .
V. Todorov, P. Filzmoser (2013). Comparing classical and robust sparse PCA. In R Kruse, M Berthold, C Moewes, M Gil, P Grzegorzewski, O Hryniewicz (eds.), Synergies of Soft Computing and Statistics for Intelligent Data Analysis, volume 190 of Advances in Intelligent Systems and Computing, pp. 283--291. Springer, Berlin; New York. ISBN 978-3-642-33041-4, tools:::Rd_expr_doi("10.1007/978-3-642-33042-1_31") .