aout.kernel: Find alpha-outliers in arbitrary univariate data using kernel...

In alphaOutlier: Obtain Alpha-Outlier Regions for Well-Known Probability Distributions

Description

Given the arguments of the density, aout.kernel identifies α-outliers in a given data set.

Usage

aout.kernel(data, alpha, plot = TRUE, plottitle = "", kernel = "gaussian", 
nkernel = 1024, kern.bw = "SJ", kern.adj = 1, 
xlim = NA, ylim = NA, outints = FALSE, w = NA, ...)

Arguments

data	a vector. The data set to be examined.
alpha	an atomic vector. Determines the maximum amount of probability mass the outlier region may contain.
plot	boolean. If TRUE, a plot of the data and estimated density with shaded outlier region is printed.
plottitle	character string. Title of the plot.
kernel	See kernel in density.
nkernel	See n in density.
kern.bw	See bw in density.
kern.adj	See adjust in density.
xlim	a vector. Specify if you want to change the x-limits of the plot.
ylim	a vector. Specify if you want to change the y-limits of the plot.
outints	boolean. If TRUE, then the bounds of the inlier-regions and the chosen bandwidth are shown.
w	a vector. See weights in density.
...	Further arguments for density and plot.

Value

If outints = TRUE, a list of

Results	A data frame containing one row for each observation. The observations are labelled whether they are outlying, the value of the estimated density at the observation is shown and the bound of the outlier identifier.
Bounds.of.Inlier.Regions	The bounds of the inlier region(s).
KDE.Chosen.Bandwidth	The bandwidth that was chosen by density.

Author(s)

A. Rehage

Examples

set.seed(23)
tempx <- rnorm(1000, 0, 1)
tempx[1] <- -2.5
aout.kernel(tempx[1:10], alpha = 0.1, kern.adj = 1, xlim = c(-3,3), outints = TRUE)
# not run:
# aout.kernel(tempx[1:200], alpha = 0.1, kern.adj = 1, xlim = c(-3,3))

alphaOutlier documentation built on May 2, 2019, 3:59 p.m.