# plotuniout: Multivariate outlier plot for each dimension In StatDA: Statistical Analysis for Environmental Data

## Description

A multivariate outlier plot for each dimension is produced.

## Usage

 ```1 2``` ```plotuniout(x, symb = FALSE, quan = 1/2, alpha = 0.025, bw = FALSE, pch2 = c(3, 1), cex2 = c(0.7, 0.4), col2 = c(1, 1), lcex.fac = 1, ...) ```

## Arguments

 `x` dataset `symb` if FALSE, only two different symbols (outlier and no outlier) will be used `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, see arw `bw` if TRUE, symbols are in gray-scale (only if symb=TRUE) `pch2, cex2, col2` graphical parameters for the points `lcex.fac` factor for multiplication of symbol size (only if symb=TRUE) `...` further graphical parameters for the plot

## 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.

## See Also

`arw`, `covMcd`

## Examples

 ```1 2 3 4 5``` ```data(moss) el=c("Ag","As","Bi","Cd","Co","Cu","Ni") dat=log10(moss[,el]) ans<-plotuniout(dat,symb=FALSE,cex2=c(0.9,0.1),pch2=c(3,21)) ```

StatDA documentation built on March 13, 2020, 2:42 a.m.