mvoutlier (version 2.1.4)

locoutSort: Interactive diagnostic plot for identifying local outliers

Description

Computes global and pairwise Mahalanobis distances for visualizing global and local multivariate outliers. The plot is split into regular (left) and global (right) outliers, and points can be selected interactively. In a second plot, these points are shown by spatial coordinates.

Usage

locoutSort(dat, X, Y, distc = NULL, k = 10, propneighb = 0.1, chisqqu = 0.975, 
    sel = NULL, ...)

Value

list(sel=sel,index.regular=res$indices.regular,index.outliers=res$indices.outliers)

sel

plot coordinates of the selected region

indices.reg

indices of the bservations being regular observations

indices.out

indices of the observations being golbal outliers

Arguments

dat

multivariate data set (without coordinates)

X

X coordinates of the data points

Y

Y coordinates of the data points

distc

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

propneighb

proportion of neighbors to be included in tolerance ellipse

chisqqu

quantile of the chisquare distribution for splitting the plot

sel

optional list with x and y, i.e. coordinates with selected polygon

...

additional parameters for plotting

Author

Peter Filzmoser <> http://cstat.tuwien.ac.at/filz/

Details

For this diagnostic tool, the number of neighbors is fixed, and propneighb (called beta) is also fixed. For each observation we compute the degree of isolation from a fraction of 1-beta of its neighbors. The observations are sorted according to this degree of isolation, and this sorted index forms the x-axis of the left plot. This plot is also split into regular (left) and global (right) outliers. Then one can select with the mouse a region in this plot, meaning an observation and (some of) its neighbors. Alternatively, this region can be supplied by sel. The selected observations are then shown in the right plot. Links to the neighbors are also shown.

References

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

See Also

locoutPercent, locoutNeighbor

Examples

# use data from illustrative example in paper:
data(X)
data(Y)
data(dat)
sel <- locoutSort(dat,X,Y,k=10,propneighb=0.1,chisqqu=0.975,
    sel=list(x=c(87.5,87.5,89.3,89.3),y=c(4.3,0.7,0.7,4.3)))