R: Sign Method for Outlier Identification in High Dimensions -...
sign2
R Documentation
Sign Method for Outlier Identification in High Dimensions - Sophisticated Version
Description
Fast algorithm for identifying multivariate outliers in high-dimensional
and/or large datasets, using spatial signs, see Filzmoser, Maronna, and
Werner (CSDA, 2007). The computation of the distances is based on
principal components.
a numeric matrix or data frame which provides the data for
outlier detection
makeplot
a logical value indicating whether a diagnostic plot
should be generated (default to FALSE)
explvar
a numeric value between 0 and 1 indicating how much variance
should be covered by the robust PCs (default to 0.99)
qcrit
a numeric value between 0 and 1 indicating the quantile to
be used as critical value for outlier detection (default to 0.975)
...
additional plot parameters, see help(par)
Details
Based on the robustly sphered and normed data, robust principal components
are computed which are needed for determining distances for each
observation. The distances are transformed to approach chi-square
distribution, and a critical value is then used as outlier cutoff.
Value
wfinal01
0/1 vector with final weights for each observation;
weight 0 indicates potential multivariate outliers.
x.dist
numeric vector with distances used for outlier detection.
P. Filzmoser, R. Maronna, M. Werner.
Outlier identification in high dimensions,
Computational Statistics and Data Analysis, 52, 1694–1711, 2008.
N. Locantore, J. Marron, D. Simpson, N. Tripoli, J. Zhang, and K. Cohen.
Robust principal components for functional data,
Test 8, 1-73, 1999.
See Also
pcout, sign1
Examples
# geochemical data from northern Europe
data(bsstop)
x=bsstop[,5:14]
# identify multivariate outliers
x.out=sign2(x,makeplot=FALSE)
# visualize multivariate outliers in the map
op <- par(mfrow=c(1,2))
data(bss.background)
pbb(asp=1)
points(bsstop$XCOO,bsstop$YCOO,pch=16,col=x.out$wfinal01+2)
title("Outlier detection based on signout")
legend("topleft",legend=c("potential outliers","regular observations"),pch=16,col=c(2,3))
# compare with outlier detection based on MCD:
x.mcd <- robustbase::covMcd(x)
pbb(asp=1)
points(bsstop$XCOO,bsstop$YCOO,pch=16,col=x.mcd$mcd.wt+2)
title("Outlier detection based on MCD")
legend("topleft",legend=c("potential outliers","regular observations"),pch=16,col=c(2,3))
par(op)