getHDmembers {HDoutliers} | R Documentation |
Partitioning Stage of the hdoutliers Algorithm
Description
Implements the first stage of the hdoutliers Algorithm, in which the data is partitioned according to exemplars and their associated lists of members.
Usage
getHDmembers(data, maxrows = 10000, radius = NULL)
Arguments
data |
A vector, matrix, or data frame consisting of numeric and/or categorical variables. |
maxrows |
If the number of observations is greater than |
radius |
Threshold for determining membership in the exemplars's lists
(used only when the number of observations is greater than |
Details
If the number of observations exceeds maxrows
, the data is
partitioned into lists corresponding to exemplars
and their members within radius
of each exemplar,
to reduce the number of nearest-neighbor computations required for
outlier detection.
When there are fewer observations, the result is a list whose elements are
the individual observations (each observation is an exemplar, with no
other members).
Value
A list in which each component is a vector of observation indexes.
The first index in each list is the index of the exemplar
defining that list, and any remaining indexes are the
associated members, within radius
of the exemplar.
References
Wilkinson, L. (2016). Visualizing Outliers. <https://www.cs.uic.edu/~wilkinson/Publications/outliers.pdf>.
See Also
Examples
data(dots)
mem.W <- getHDmembers(dots$W)
out.W <- getHDoutliers(dots$W,mem.W)
data(ex2D)
mem.ex2D <- getHDmembers(ex2D)
out.ex2D <- getHDoutliers(ex2D,mem.ex2D)
## Not run:
n <- 100000 # number of observations
set.seed(3)
x <- matrix(rnorm(2*n),n,2)
nout <- 10 # number of outliers
x[sample(1:n,size=nout),] <- 10*runif(2*nout,min=-1,max=1)
mem.x <- getHDmembers(x)
out.x <- getHDoutliers(x,mem.x)
## End(Not run)