cor.plot: Correlation Plot: robust versus classical bivariate...

In mvoutlier: Multivariate Outlier Detection Based on Robust Methods

Description

The function corr.plot plots the (two-dimensional) data and adds two correlation ellipsoids, based on classical and robust estimation of location and scatter. Robust estimation can be thought of as estimating the mean and covariance of the 'good' part of the data.

Usage

corr.plot(x, y, quan=1/2, alpha=0.025, ...)

Arguments

x	vector to be plotted against y and of which the correlation with y is calculated.
y	vector to be plotted against x and of which the correlation with x is calculated.
quan	amount of observations which are used for mcd estimations. has to be between 0.5 and 1, default ist 0.5
alpha	Determines the size of the ellipsoids. An observation will be outside of the ellipsoid if its mahalanobis distance exceeds the 1-alpha quantile of the chi-squared distribution.
...	additional graphical parameters

Value

cor.cla	correlation between x and y based on classical estimation of location and scatter
cor.rob	correlation between x and y based on robust estimation of location and scatter

Author(s)

Moritz Gschwandtner <[e-mail]>
Peter Filzmoser <[e-mail]> http://cstat.tuwien.ac.at/filz/

See Also

covMcd

Examples

# create data:
x <- cbind(rnorm(100), rnorm(100))
y <- cbind(rnorm(10, 3, 1), rnorm(10, 3, 1))
z <- rbind(x,y)
# execute:
corr.plot(z[,1], z[,2])

Example output

Loading required package: sgeostat
sROC 0.1-2 loaded
$cor.cla
[1] 0.4884164

$cor.rob
[1] 0.1830074

mvoutlier documentation built on July 30, 2021, 9:09 a.m.