acpgen {amap} | R Documentation |
Generalised principal component analysis
Description
Generalised principal component analysis
Usage
acpgen(x,h1,h2,center=TRUE,reduce=TRUE,kernel="gaussien")
K(u,kernel="gaussien")
W(x,h,D=NULL,kernel="gaussien")
Arguments
x |
Matrix or data frame |
h |
Scalar: bandwidth of the Kernel |
h1 |
Scalar: bandwidth of the Kernel for W |
h2 |
Scalar: bandwidth of the Kernel for U |
kernel |
The kernel used. This must be one of '"gaussien"', '"quartic"', '"triweight"', '"epanechikov"' , '"cosinus"' or '"uniform"' |
center |
A logical value indicating whether we center data |
reduce |
A logical value indicating whether we "reduce" data i.e. divide each column by standard deviation |
D |
A product scalar matrix / une matrice de produit scalaire |
u |
Vector |
Details
acpgen
compute generalised pca. i.e. spectral analysis of
, and project
with
on the principal vector sub-spaces.
a column vector of
variables of individu
(input data)
W
compute estimation of noise in the variance.
with variance estimation;
U
compute robust variance.
with estimator of the mean.
K
compute kernel, i.e.
gaussien:
quartic:
triweight:
epanechikov:
cosinus:
Value
An object of class acp The object is a list with components:
sdev |
the standard deviations of the principal components. |
loadings |
the matrix of variable loadings (i.e., a matrix
whose columns contain the eigenvectors). This is of class
|
scores |
if |
eig |
Eigen values |
Author(s)
Antoine Lucas
References
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.
See Also
Examples
data(lubisch)
lubisch <- lubisch[,-c(1,8)]
p <- acpgen(lubisch,h1=1,h2=1/sqrt(2))
plot(p,main='ACP robuste des individus')
# See difference with acp
p <- princomp(lubisch)
class(p)<- "acp"