cor.plot {mvoutlier} | R Documentation |
Correlation Plot: robust versus classical bivariate correlation
Description
The function cor.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
cor.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 <e0125439@student.tuwien.ac.at>
Peter Filzmoser <P.Filzmoser@tuwien.ac.at>
http://www.statistik.tuwien.ac.at/public/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:
cor.plot(z[,1], z[,2])
[Package
mvoutlier version 1.4
Index]