aout.kernel {alphaOutlier} | R Documentation |
\alpha
-outliers in arbitrary univariate data using kernel density estimation
Given the arguments of the density
, aout.kernel
identifies \alpha
-outliers in a given data set.
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, ...)
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 |
plottitle |
character string. Title of the plot. |
kernel |
See |
nkernel |
See |
kern.bw |
See |
kern.adj |
See |
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 |
w |
a vector. See |
... |
Further arguments for |
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 |
A. Rehage
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))