I believe that the value of "radius" that you are using is incorrect. If you
have a data
matrix X whose columns are jointly distributed N(mu,Sigma) then a
confidence
ellipse for mu is determined by
n * (x - Xbar)' S^{-1}(x - Xbar) ~ T^2
where Xbar is the mean vector for X and S is the sample covariance matrix,
and where "T^2" means Hotelling's T-squared distribution, which is equal to
(n-1)*2/(n-2) * F_{2,n-2}
the latter representing the F distribution on 2 and n-2 degrees of freedom.
Thus (I think) your radius should be
radius <- sqrt(2 * (npts-1) * qf(0.95, 2, npts-2)/(npts*(npts-2)))
where npts <- length(a). Note that it is qf(0.95,2,npts-2) and *NOT*
qf(0.95,2,npts-1).
To get the corresponding *prediction* ellipse simply multiply the foregoing
radius by sqrt(npts+1). By "prediction ellipse" I mean an ellipse such that
the probability that a new independent observation from the same population
will fall in that ellipse is the given probability (e.g. 0.95). Note that
this does
not mean that 95% of the data will fall in the calculated ellipse (basically
because
of the *dependence* between S and the individual observations).
These confidence and prediction ellipses are (I'm pretty sure) valid under
the assumption that the data are (two dimensional, independent) Gaussian,
and that you use the sample covariance and sample mean as "shape" and
"centre". I don't know what impact your robustification procedure of using
cov.trob() will/would have on the properties of these ellipses.
A script which does the ellipses for your toy data, using the sample
covariance
and sample mean (rather than output from cov.trob()) is as follows:
#
# Script scr.amatulli
#
require(car)
a <- c(12,12,4,5,63,63,23)
b <- c(13,15,7,10,73,83,43)
npts <- length(a)
shape <- var(cbind(a, b))
center <- c(mean(a),mean(b))
rconf <- sqrt(2 * (npts-1) * qf(0.95, 2, npts-2)/(npts*(npts-2)))
rpred <- sqrt(npts+1)*rconf
conf.elip <- ellipse(center, shape, rconf,draw = FALSE)
pred.elip <- ellipse(center, shape, rpred,draw = FALSE)
plot(pred.elip, type='l')
points(a,b)
lines(conf.elip,col="red")
cheers,
Rolf Turner
On 01/27/2013 10:12 AM, Giuseppe Amatulli wrote:
Hi,
I'm using the R library(car) to draw confidence/prediction ellipses in a
scatterplot.
>From what i understood the ellipse() function return an ellipse based
parameters: shape, center, radius .
If i read dataEllipse() function i can see how these parameters are
calculated for a confidence ellipse.
ibrary(car)
a=c(12,12,4,5,63,63,23)
b=c(13,15,7,10,73,83,43)
v <- cov.trob(cbind(a, b))
shape <- v$cov
center <- v$center
radius <- sqrt(2 * qf(0.95, 2, length(a) - 1)) # radius <- sqrt(dfn *
qf(level, dfn, dfd))
conf.elip = ellipse(center, shape, radius,draw = F)
plot(conf.elip, type='l')
points(a,b)
My question is how I can calculate shape, center and radius to obtain a
prediction ellipses rather than a confidence ellipse?
Thanks in Advance
Giuseppe
