locoutPercent: Diagnostic plot for identifying local outliers with fixed...

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 fixed, but the fraction of neighbors is varying.

Usage

locoutPercent(dat, X, Y, dist = NULL, k = 10, chisqqu = 0.975, sortup = 10, sortlow = 90, 
  nlinesup = 10, nlineslow = 10, indices = NULL, xlab = "(Sorted) Index", 
  ylab = "Distance to neighbor", col = gray(0.7), ...)

Arguments

dat	multivariate data set (without coordinates)
X	X coordinates of the data points
Y	Y coordinates of the data points
dist	maximum distance to search for neighbors; if nothing is provided, k for kNN is used
k	number of nearest neighbors to search - not taken if a value for dist is provided
chisqqu	quantile of the chisquare distribution for splitting the plot
sortup	sort local outliers accorting to given percentage
sortlow	sort local inliers accorting to given percentage
nlinesup	number of lines to be plotted for upper part
nlineslow	number of lines to be plotted for lower part
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
col	color for lines
...	additional parameters for plotting

Details

For this diagnostic tool, the number of neighbors is fixed, but propneighb (called beta) is varied. 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. 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

ret	list containing indices of regular and outlying observations

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

locoutNeighbor, locoutSort

Examples

# use data from illustrative example in paper:
data(X)
data(Y)
data(dat)
res <- locoutPercent(dat,X,Y,k=10,chisqqu=0.975, indices=c(1,11,24,36))

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