aq.plot: Adjusted Quantile Plot

In mvoutlier: Multivariate Outlier Detection Based on Robust Methods

Description

The function aq.plot plots the ordered squared robust Mahalanobis distances of the observations against the empirical distribution function of the $MD^2_i$. In addition the distribution function of $chisq_p$ is plotted as well as two vertical lines corresponding to the chisq-quantile specified in the argument list (default is 0.975) and the so-called adjusted quantile. Three additional graphics are created (the first showing the data, the second showing the outliers detected by the specified quantile of the $chisq_p$ distribution and the third showing these detected outliers by the adjusted quantile).

Usage

aq.plot(x, delta=qchisq(0.975, df=ncol(x)), quan=1/2, alpha=0.05)

Arguments

x	matrix or data.frame containing the data; has to be at least two-dimensional
delta	quantile of the chi-squared distribution with ncol(x) degrees of freedom. This quantile appears as cyan-colored vertical line in the plot.
quan	proportion of observations which are used for mcd estimations; has to be between 0.5 and 1, default ist 0.5
alpha	Maximum thresholding proportion (optional scalar, default: alpha = 0.05)

Details

The function aq.plot plots the ordered squared robust Mahalanobis distances of the observations against the empirical distribution function of the $MD^2_i$. The distance calculations are based on the MCD estimator.

For outlier detection two different methods are used. The first one marks observations as outliers if they exceed a certain quantile of the chi-squared distribution. The second is an adaptive procedure searching for outliers specifically in the tails of the distribution, beginning at a certain chisq-quantile (see Filzmoser et al., 2005).

The function behaves differently depending on the dimension of the data. If the data is more than two-dimensional the data are projected on the first two robust principal components.

Value

outliers

boolean vector of outliers

Author(s)

Moritz Gschwandtner <[e-mail]>
Peter Filzmoser <[e-mail]> http://cstat.tuwien.ac.at/filz/

References

P. Filzmoser, R.G. Garrett, and C. Reimann. Multivariate outlier detection in exploration geochemistry. Computers & Geosciences, 31:579-587, 2005.

Examples

# create data:
set.seed(134)
x <- cbind(rnorm(80), rnorm(80), rnorm(80))
y <- cbind(rnorm(10, 5, 1), rnorm(10, 5, 1), rnorm(10, 5, 1))
z <- rbind(x,y)
# execute:
aq.plot(z, alpha=0.1)

Example output

Loading required package: sgeostat
sROC 0.1-2 loaded
Projection to the first and second robust principal components.
Proportion of total variation (explained variance): 0.7402121
$outliers
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
[85]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE

mvoutlier documentation built on July 30, 2021, 9:09 a.m.