acprob {amap}R Documentation

Robust principal component analysis

Description

Robust principal component analysis / Analyse en composantes principales robuste

Usage

acprob(x,h,center=TRUE,reduce=TRUE,kernel="gaussien")

Arguments

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

Details

acpgen compute robust pca. i.e. spectral analysis of a robust variance instead of usual variance. Robust variance: see varrob

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 "loadings": see loadings for its print method.
scores if scores = TRUE, the scores of the supplied data on the principal components.
eig Eigen values

Author(s)

Antoine Lucas, http://genopole.toulouse.inra.fr/~lucas/amap

See Also

acp princomp


[Package Contents]