R: Adjusted Quantile Plot

aq.plot {mvoutlier} | R Documentation

## Adjusted Quantile Plot

### 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 <e0125439@student.tuwien.ac.at>
Peter Filzmoser <P.Filzmoser@tuwien.ac.at> 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)

[Package mvoutlier version 2.0.9 Index]