ellipses {compositions} | R Documentation |
Draws ellipses from a mean and a variance into a plot.
ellipses(mean,...) ## S3 method for class 'acomp': ellipses(mean,var,r=1,...,steps=72,thinRatio=NULL,aspanel=FALSE) ## S3 method for class 'rcomp': ellipses(mean,var,r=1,...,steps=72,thinRatio=NULL,aspanel=FALSE) ## S3 method for class 'aplus': ellipses(mean,var,r=1,...,steps=72,thinRatio=NULL) ## S3 method for class 'rplus': ellipses(mean,var,r=1,...,steps=72,thinRatio=NULL) ## S3 method for class 'rmult': ellipses(mean,var,r=1,...,steps=72,thinRatio=NULL)
mean |
a compositional dataset or value of means or midpoints of the ellipses |
var |
a variance matrix or a set of variance matrices given by
var[i,,] (multiple covariance matrices are not consitently
implemented as of today). The principal axis of the variance give
the axis of
the ellipses, whereas the square-root of the eigenvalues times r give the
half-diameters of the ellipse. |
r |
a scaling of the half-diameters |
... |
further graphical parameters |
steps |
the number of discretisation points to draw the ellipses. |
thinRatio |
The ellipse function now be default plots the whole ellipsiod by giving its principle circumferences. However this is not reasonable for the thinner directions. If a direction other than the first two eigendirections has an eigenvalue not bigger than thinRatio*rmax it is not plotted. Thus thinRatio=1 reinstantiates the old behavior of the function. Later thinratio=NULL will become the default, in which case the projection of the ellipse is plotted. However this is not implemented yet. |
aspanel |
Is the function called as slave to draw in a panel of a gsi.pairs plot, or as a user function setting up the plots. |
The ellipsoid/ellipse drawn is given by the solutions of
(x-mean)^tvar^{-1}(x-mean)=r^2
in the respective geometry of the parameter space. Note that these ellipses can be added to panel plots (by means of orthogonal projections in the corresponding geometry).
There are actually three possibilities of drawing a a hyperdimensional ellipsoid or ellipse and non of them is perfect.
var
-Matrix given.
K.Gerald v.d. Boogaart http://www.stat.boogaart.de
data(SimulatedAmounts) plot(acomp(sa.lognormals)) tt<-acomp(sa.lognormals); ellipses(mean(tt),var(tt),r=2,col="red") tt<-rcomp(sa.lognormals); ellipses(mean(tt),var(tt),r=2,col="blue") plot(aplus(sa.lognormals[,1:2])) tt<-aplus(sa.lognormals[,1:2]); ellipses(mean(tt),var(tt),r=2,col="red") tt<-rplus(sa.lognormals[,1:2]); ellipses(mean(tt),var(tt),r=2,col="blue") plot(rplus(sa.lognormals[,1:2])) tt<-aplus(sa.lognormals[,1:2]); ellipses(mean(tt),var(tt),r=2,col="red") tt<-rplus(sa.lognormals[,1:2]); ellipses(mean(tt),var(tt),r=2,col="blue") tt<-rmult(sa.lognormals[,1:2]); ellipses(mean(tt),var(tt),r=2,col="green")