Hi John,
>>>>> "JF" == John Fox <[EMAIL PROTECTED]> writes:
JF> Dear Volker, If the data ellipse (or, in this case, circle) is
JF> scaled so that its shadows (projections) on the axes each
JF> includes 68% of the data (that is of the marginal distribution
JF> of each variable), then the ellipse will include less than 68%
JF> of the data (i.e., of the joint distribution of the two
JF> variables). Conversely, to include 68% of the data in the
JF> ellipse, the shadows of the ellipse have to be larger.
JF> Did I understand your point correctly?
I am not sure. I will try to rephrase my initial request:
Let X by a one--dimensional random variable (standard normal
distribution; mean=0; std=1). The 68% confidence intervall of X will
approximately be: [-1,1]. Now, if I combine X with a stochastically
independent second random variable Y, the marginal distribution of X
should not change. Therefore, the projections of the error ellipse on
the X--axis should still be: [-1,1].
If I do this with the function data.ellipse:
data.ellipse(rnorm(10000),rnorm(10000),levels=0.68,plot.points=F)
I get a projection on the X-axis which is larger than [-1,1]. In fact,
it is a little bit larger than [-sqrt(2),+sqrt(2)].
My interpretation is that this is due to the construction of the
radius in data.ellipse:
dfn<-2
radius <- sqrt ( dfn * qf(level, dfn, dfd ))
I would expect a dfn<-1 here (such that the radius would correspond to
the t-distribution).
Does this make sense?
Volker
--
___________________________________________________________
Dr. Volker Franz
Max-Planck-Institute for Biological Cybernetics
Tuebingen, Germany
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