locoutNeighbor: Diagnostic plot for identifying local outliers with varying...

In mvoutlier: Multivariate Outlier Detection Based on Robust Methods

Description

Computes global and pairwise Mahalanobis distances for visualizing global and local multivariate outliers. The size of the neighborhood (number of neighbors) is varying, but the fraction of neighbors is fixed.

Usage

locoutNeighbor(dat, X, Y, propneighb = 0.1, variant = c("dist", "knn"), usemax = 1/3, 
   npoints = 50, chisqqu = 0.975, indices = NULL, xlab = NULL, ylab = NULL, 
   colall = gray(0.7), colsel = 1, ...)

Arguments

dat	multivariate data set (without coordinates)
X	X coordinates of the data points
Y	Y coordinates of the data points
propneighb	proportion of neighbors to be included in tolerance ellipse
variant	either search for neighbors according to the Eucl.Distance, or according to kNN
usemax	for either variant: give fraction of points (max Dist) that is used for the plot
npoints	computation is done at most at npoints points
chisqqu	quantile of the chisquare distribution for splitting the plot
indices	if this is not NULL, these should be indices of observations to be highlighted
xlab	x-axis label for plot
ylab	y-axis label for plot
colall	color for lines if indices is NULL
colsel	color for lines if indices are selected
...	additional parameters for plotting

Details

For this diagnostic tool, the number of neighbors is varied up to a fraction of usemax observations. Then propneighb (called beta) is fixed, and for each observation we compute the degree of isolation from a fraction of 1-beta of its neighbors. Neighborhood can be defined either via the Euclidean distance or by k-Nearest-Neighbors. For computational reasons, all computations are done at most for npoints points. The critical value for outliers is the quantile chisqqu of the chisquare distribution. One can also provide indices of observations (for indices). Then the corresponding lines in the plots will be highlighted.

Value

indices.reg	indices of the (selected) observations being regular observations
indices.out	indices of the (selected) observations being golbal outliers

Author(s)

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

References

P. Filzmoser, A. Ruiz-Gazen, and C. Thomas-Agnan: Identification of local multivariate outliers. Submitted for publication, 2012.

See Also

locoutPercent, locoutSort

Examples

# use data from illustrative example in paper:
data(X)
data(Y)
data(dat)
res <- locoutNeighbor(dat,X,Y,variant="knn",usemax=1,chisqqu=0.975,indices=c(1,11,24,36),
              propneighb=0.1,npoints=100)

Example output

Loading required package: sgeostat
sROC 0.1-2 loaded

mvoutlier documentation built on July 30, 2021, 9:09 a.m.