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.
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), ...)multivariate data set (without coordinates)
X coordinates of the data points
Y coordinates of the data points
maximum distance to search for neighbors; if nothing is provided, k for kNN is used
number of nearest neighbors to search - not taken if a value for dist is provided
quantile of the chisquare distribution for splitting the plot
sort local outliers accorting to given percentage
sort local inliers accorting to given percentage
number of lines to be plotted for upper part
number of lines to be plotted for lower part
if this is not NULL, these should be indices of observations to be highlighted
x-axis label for plot
y-axis label for plot
color for lines
additional parameters for plotting
list containing indices of regular and outlying observations
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.
P. Filzmoser, A. Ruiz-Gazen, and C. Thomas-Agnan: Identification of local multivariate outliers. Submitted for publication, 2012.
# NOT RUN {
# 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))
# }
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