# plotmvoutlier: Multivariate outlier plot In StatDA: Statistical Analysis for Environmental Data

## Description

This function plots multivariate outliers. One possibility is to distinguish between outlier and no outlier. The alternative is to distinguish between the different percentils (e.g. <25%, 25%<x<50%,...).

## Usage

 ```1 2 3 4``` ```plotmvoutlier(coord, data, quan = 1/2, alpha = 0.025, symb = FALSE, bw = FALSE, plotmap = TRUE, map = "kola.background", which.map = c(1, 2, 3, 4), map.col = c(5, 1, 3, 4), map.lwd = c(2, 1, 2, 1), pch2 = c(3, 21), cex2 = c(0.7, 0.2), col2 = c(1, 1), lcex.fac = 1, ...) ```

## Arguments

 `coord` the coordinates for the points `data` the value for the different coordinates `quan` Number of subsets used for the robust estimation of the covariance matrix. Allowed are values between 0.5 and 1., see covMcd `alpha` Maximum thresholding proportion `symb` if FALSE, only two different symbols (outlier and no outlier) will be used `bw` if TRUE, symbols are in gray-scale (only if symb=TRUE) `plotmap` if TRUE, the map is plotted `map` the name of the background map `which.map, map.col, map.lwd` parameters for the background plot, see plotbg `pch2, cex2, col2` graphical parameters for the points `lcex.fac` factor for multiplication of symbol size (only if symb=TRUE) `...` further parameters for the plot

## Details

The function computes a robust estimation of the covariance and then the Mahalanobis distances are calculated. With this distances the data set is divided into outliers and non outliers. If symb=FALSE only two different symbols are used otherwise different grey scales are used to distinguish the different types of outliers.

## Value

 `o` returns the outliers `md` the square root of the Mahalanobis distance `euclidean` the Euclidean distance of the scaled data

## Author(s)

Peter Filzmoser <P.Filzmoser@tuwien.ac.at> http://cstat.tuwien.ac.at/filz/

## References

C. Reimann, P. Filzmoser, R.G. Garrett, and R. Dutter: Statistical Data Analysis Explained. Applied Environmental Statistics with R. John Wiley and Sons, Chichester, 2008.

`plotbg`, `covMcd`, `arw`
 ``` 1 2 3 4 5 6 7 8 9 10``` ```data(moss) X=moss[,"XCOO"] Y=moss[,"YCOO"] el=c("Ag","As","Bi","Cd","Co","Cu","Ni") x=log10(moss[,el]) data(kola.background) plotmvoutlier(cbind(X,Y),x,symb=FALSE,map.col=c("grey","grey","grey","grey"), map.lwd=c(1,1,1,1), xlab="",ylab="",frame.plot=FALSE,xaxt="n",yaxt="n") ```