Robust principal component analysis
acprob(x,h,center=TRUE,reduce=TRUE,kernel="gaussien")Matrix / data frame
Scalar: bandwidth of the Kernel
The kernel used. This must be one of '"gaussien"', '"quartic"', '"triweight"', '"epanechikov"' , '"cosinus"' or '"uniform"'
A logical value indicating whether we center data
A logical value indicating whether we "reduce" data i.e. divide each column by standard deviation
An object of class acp The object is a list with components:
the standard deviations of the principal components.
the matrix of variable loadings (i.e., a matrix
whose columns contain the eigenvectors). This is of class
"loadings": see loadings for its print
method.
if scores = TRUE, the scores of the supplied
data on the principal components.
Eigen values
acpgen compute robust pca. i.e. spectral analysis of a robust
variance instead of usual variance. Robust variance: see
varrob
H. Caussinus, M. Fekri, S. Hakam and A. Ruiz-Gazen, A monitoring display of multivariate outliers Computational Statistics & Data Analysis, Volume 44, Issues 1-2, 28 October 2003, Pages 237-252
Caussinus, H and Ruiz-Gazen, A. (1993): Projection Pursuit and Generalized Principal Component Analyses, in New Directions in Statistical Data Analysis and Robustness (eds. Morgenthaler et al.), pp. 35-46. Birk\"auser Verlag Basel.
Caussinus, H. and Ruiz-Gazen, A. (1995). Metrics for Finding Typical Structures by Means of Principal Component Analysis. In Data Science and its Applications (eds Y. Escoufier and C. Hayashi), pp. 177-192. Tokyo: Academic Press.
Antoine Lucas and Sylvain Jasson, Using amap and ctc Packages for Huge Clustering, R News, 2006, vol 6, issue 5 pages 58-60.