Sign Method for Outlier Identification in High Dimensions - Simple Version
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 Mahalanobis distances.
sign1(x, makeplot = FALSE, qcrit = 0.975, ...)
x |
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) |
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) |
Based on the robustly sphered and normed data, robust principal components are computed. These are used for computing the covariance matrix which is the basis for Mahalanobis distances. A critical value from the chi-square distribution is then used as outlier cutoff.
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. |
const |
outlier cutoff value. |
Peter Filzmoser <P.Filzmoser@tuwien.ac.at> http://cstat.tuwien.ac.at/filz/
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 (1999). Robust principal components for functional data, Test 8, 1–73.
# geochemical data from northern Europe
data(bsstop)
x=bsstop[,5:14]
# identify multivariate outliers
x.out=sign1(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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.