acprob {amap} | R Documentation |
Robust principal component analysis / Analyse en composantes principales robuste
acprob(x,h,center=TRUE,reduce=TRUE,kernel="gaussien")
x |
Matrix / data frame |
h |
Scalar: bandwidth of the Kernel |
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 |
acpgen
compute robust pca. i.e. spectral analysis of a robust
variance instead of usual variance. Robust variance: see
varrob
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
"loadings" : see loadings for its print
method. |
scores |
if scores = TRUE , the scores of the supplied
data on the principal components. |
eig |
Eigen values |
Antoine Lucas, http://genopole.toulouse.inra.fr/~lucas/amap