aout.kernel {alphaOutlier}

Find \alpha-outliers in arbitrary univariate data using kernel density estimation

Description

Given the arguments of the density, aout.kernel identifies \alpha-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))