plotuniout {StatDA} | R Documentation

## Multivariate outlier plot for each dimension

### Description

A multivariate outlier plot for each dimension is produced.

### Usage

    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>

### References

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

### See Also

`arw`, `covMcd`

### Examples

    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))

[Package StatDA version 1.0 Index]