StatDA (version 1.7.11)

plotmvoutlier: Multivariate outlier plot

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

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, ...)

Value

o

returns the outliers

md

the square root of the Mahalanobis distance

euclidean

the Euclidean distance of the scaled data

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

Author

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

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.

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

plotbg, covMcd, arw

Examples

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")