plotuniout: Multivariate outlier plot for each dimension

Description Usage Arguments Value Author(s) References See Also Examples

Description

A multivariate outlier plot for each dimension is produced.

Usage

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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

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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.

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