plotuniout: Multivariate outlier plot for each dimension

In StatDA: Statistical Analysis for Environmental Data

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 <[e-mail]> 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

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 June 7, 2023, 6:26 p.m.