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Last data update: 2014.03.03

R: Leland Wilkinson's HDoutliers Algorithm for Outlier Detection

HDoutliers

R Documentation

Leland Wilkinson's HDoutliers Algorithm for Outlier Detection

Description

Detects outliers based on a probability model.

Usage

HDoutliers(data, maxrows = 10000, radius = NULL, alpha = 0.05)

Arguments

`data`	A vector, matrix, or data frame consisting of numeric and/or categorical variables.
`maxrows`	If the number of observations is greater than `maxrows`, `HDoutliers` reduces the number used in nearest-neighbor computations to a set of exemplars. The default value is 10000.
`radius`	Threshold for determining membership in the exemplars's lists (used only when the number of observations is greater than maxrows). An observation is added to an exemplars' lists if its distance to that exemplar is less than `radius`. The default value is .1/(log n)^(1/p), where n is the number of observations and p is the dimension of the data.
`alpha`	Threshold for determining the cutoff for outliers. Observations are considered outliers outliers if they fall in the (1- alpha) tail of the distribution of the nearest-neighbor distances between exemplars.

Details

If the number of observations exceeds maxrows, the data is first partitioned into lists associated with exemplars and their members within radius of each exemplar, to reduce the number of nearest-neighbor computations required for outlier detection.
An exponential distribution is then fitted to the upper tail of the nearest-neighbor distances between exemplars. Observations are considered outliers if they fall in the (1- alpha) tail of the fitted CDF.

Value

The indexes of the observations determined to be outliers.

References

Wilkinson, L. (2016). Visualizing Outliers.

Examples


data(dots)
out.W <- HDoutliers(dots$W)
## Not run: 
plotHDoutliers(dots$W,out.W)
## End(Not run)

data(ex2D)
out.ex2D <- HDoutliers(ex2D)
## Not run: 
plotHDoutliers(ex2D,out.ex2D)
## End(Not run)

## 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)

out.x <- HDoutliers(x)
## End(Not run)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(HDoutliers)
Loading required package: FNN
Loading required package: FactoMineR
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/HDoutliers/HDoutliers.Rd_%03d_medium.png", width=480, height=480)
> ### Name: HDoutliers
> ### Title: Leland Wilkinson's HDoutliers Algorithm for Outlier Detection
> ### Aliases: HDoutliers
> ### Keywords: cluster
> 
> ### ** Examples
> 
> 
> data(dots)
> out.W <- HDoutliers(dots$W)
> ## Not run: 
> ##D plotHDoutliers(dots$W,out.W)
> ## End(Not run)
> 
> data(ex2D)
> out.ex2D <- HDoutliers(ex2D)
> ## Not run: 
> ##D plotHDoutliers(ex2D,out.ex2D)
> ## End(Not run)
> 
> ## Not run: 
> ##D n <- 100000 # number of observations
> ##D set.seed(3)
> ##D x <- matrix(rnorm(2*n),n,2)
> ##D nout <- 10 # number of outliers
> ##D x[sample(1:n,size=nout),] <- 10*runif(2*nout,min=-1,max=1)
> ##D 
> ##D out.x <- HDoutliers(x)
> ## End(Not run)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>