sign1 {mvoutlier}

Sign Method for Outlier Identification in High Dimensions - Simple 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 Mahalanobis distances.

Usage

sign1(x, makeplot = FALSE, qcrit = 0.975, ...)

Arguments

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)

Details

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.

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.

const

outlier cutoff value.

Author(s)

Peter Filzmoser <[e-mail]> http://cstat.tuwien.ac.at/filz/

References

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.

See Also

pcout, sign2

Examples

# 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)