Multivariate outlier plot for each dimension
A multivariate outlier plot for each dimension is produced.
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, ...)
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 |
o |
returns the outliers |
md |
the square root of the Mahalanobis distance |
euclidean |
the Euclidean distance of the scaled data |
Peter Filzmoser <P.Filzmoser@tuwien.ac.at> http://cstat.tuwien.ac.at/filz/
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.
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))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.